Biology subjects

Shin, Y. J.

Publications and source records attributed to Shin, Y. J..

4 recordsLinked to original sources

bioRxiv2025

A Machine Learning- 3D Microvessel Platform Identifies Kinase Targets Restoring Blood-Brain-Barrier Endothelial Integrity.

Disruption of the brain endothelial barrier is a hallmark of traumatic brain injury (TBI), and contributes to cerebral edema, coagulopathy, and delayed neurological deficits, yet there are no mechanism-guided therapies that directly stabilize human brain vessels. Here we used a three-dimensional (3D) human brain endothelial vessel platform with quantifiable macromolecular barrier function to assess responses to thrombin and plasma from patients with TBI. This endothelial-focused 3D system isolates the central cellular regulator of blood-brain barrier function and enables mechanistic analysis of endothelial signaling and drug responses as a foundation for personalized vascular therapeutics. By integrating this platform with machine learning-guided kinase analysis, we identified kinase pathways associated with barrier stabilization versus disruption and nominated inhibitors that reversed thrombin- and TBI plasma-induced leakage as well as compounds that exacerbated endothelial loss. This work establishes an important first step toward precision vascular therapeutics in TBI, by linking patient-derived plasma phenotypes to specific endothelial barrier responses and pharmacologically tractable kinase signaling, and it provides a generalizable framework for mechanistic drug discovery across neurovascular disorders. SIGNIFICANCE STATEMENTBlood-brain barrier (BBB) disruption drives cerebral edema, coagulopathy, ischemia, and neurological deficits after TBI. Targeting kinases as therapeutics is promising but challenged by complex inhibitor profiles and patient variability. We present a machine learning-guided platform built on 3D perfusable human brain endothelial-centric microvessels to systematically screen and select kinase inhibitors that reverse TBI-induced endothelial barrier breakdown. By pinpointing key kinase and gene regulators and demonstrating restoration of endothelial barrier integrity in patient-derived plasma models, our approach provides a transformative strategy for stabilizing the endothelial barrier after TBI and offers a versatile framework for therapeutic discovery across neurovascular disorders.

bioengineering↗

bioRxiv2022

Amyloid beta peptides (Aβ) from Alzheimer's disease neuronal secretome induce endothelial activation in a human cerebral microvessel model

In Alzheimers disease (AD), secretion and deposition of amyloid beta peptides (A{beta}) have been associated with blood-brain barrier dysfunction. However, the role of A{beta} in endothelial cell (EC) dysfunction remains elusive. Here we investigated AD mediated EC activation by studying the effect of A{beta} secreted from human induced pluripotent stem cell-derived cortical neurons (hiPSC- CN) harboring a familial AD mutation (Swe+/+) on human brain microvascular endothelial cells (HBMECs) in 2D and 3D perfusable microvessels. We demonstrated that increased A{beta} levels in Swe+/+ conditioned media (CM) led to stress fiber formation and upregulation of genes associated with endothelial inflammation and immune-adhesion. Perfusion of A{beta}-rich Swe+/+ CM induced acute formation of von Willebrand factor (VWF) fibers in the vessel lumen, which was attenuated by reducing A{beta} levels in CM. Our findings suggest that A{beta} can trigger rapid inflammatory and thrombogenic responses within cerebral microvessels, which may exacerbate AD pathology.

bioengineering↗

bioRxiv2020

Spatiotemporal Dynamics of Intra-tumoral Dependence on NEK2-EZH2 Signaling in Glioblastoma Cancer Progression

The highly lethal brain cancer glioblastoma undergoes dynamic changes in molecular profile and cellular phenotype throughout tumor core establishment and in primary-to-recurrent tumor progression. These dynamic changes allow glioblastoma tumors to escape from multimodal therapies, resulting in patient lethality. Here, we identified the emergence of dependence on NEK2-mediated EZH2 signaling, specifically in therapy-resistant tumor core-located glioblastoma cells. In patient-derived glioblastoma core models, NEK2 was required for in vivo tumor initiation, propagation, and radio-resistance. Mechanistically, in glioblastoma core cells, NEK2 binds with EZH2 to prevent its proteasome-mediated degradation in a kinase-dependent manner. Clinically, NEK2 expression is elevated in recurrent tumors after therapeutic failure as opposed to their matched primary untreated cases, and its high expression is indicative of worse prognosis. For therapeutic development, we designed a novel NEK2 kinase inhibitor CMP3a, which effectively attenuated growth of murine glioblastoma models and exhibited a synergistic effect with radiation therapy. Collectively, the emerging NEK2-EZH2 signaling axis is critical in glioblastoma, particularly within the tumor core, and the small molecule inhibitor CMP3a for NEK2 is a potential novel therapeutic agent for glioblastoma.

cancer biology↗

bioRxiv2019

Are trade-offs between flexibility and efficiency in systematic conservation planning avoidable?

Species distribution models (SDMs) have been proposed as a way to provide robust inference about species-specific sites suitabilities, and have been increasingly used in systematic conservation planning (SCP) applications. However, despite the fact that the use of SDMs in SCP may raise some potential issues, conservation studies have overlooked to assess the implications of SDMs uncertainties. The integration of these uncertainties in conservation solutions requires the development of a reserve-selection approach based on a suitable optimization algorithm. A large body of research has shown that exact optimization algorithms give very precise control over the gap to optimality of conservation solutions. However, their major shortcoming is that they generate a single binary and indivisible solution. Therefore, they provide no flexibility in the implementation of conservation solutions by stakeholders. On the other hand, heuristic decision-support systems provide large amounts of sub-optimal solutions, and therefore more flexibility. This flexibility arises from the availability of many alternative and sub-optimal conservation solutions. The two principles of efficiency and flexibility are implicitly linked in conservation applications, with the most mathematically efficient solutions being inflexible and the flexible solutions provided by heuristics suffering sub-optimality. In order to avoid the trade-offs between flexibility and efficiency in systematic conservation planning, we propose in this paper a new reserve-selection framework based on mathematical programming optimization combined with a post-selection of SDM outputs. This approach leads to a reserve-selection framework that might provide flexibility while simultaneously addressing efficiency and representativeness of conservation solutions and the adequacy of conservation targets. To exemplify the approach we a nalyzed an experimental design crossing pre- and post-selection of SDM outputs versus heuristics and exact mathematical optimizations. We used the Mediterranean Sea as a biogeographical template for our analyses, integrating the outputs of 8 SDM techniques for 438 fishes species.

ecology↗