Biology subjects

Ahn, J.-S.

Publications and source records attributed to Ahn, J.-S..

3 recordsLinked to original sources

bioRxiv2025

Blood-based Epigenetic Instability Linked to Human Aging and Disease

The abundance, dynamics, and context-dependent heterogeneity of DNA methylation--where a pattern considered abnormal in one cell type may be normal in another--poses challenges in identifying methylation abnormalities linked to disease risk. Through genome-wide analyses, we identified CpG sites with remarkably consistent methylation profiles in healthy whole blood, predominantly existing in an unmethylated state. We examined alterations at these epigenetically stable loci in diverse cohorts, including those with cardiovascular disease and hematological cancers. Our findings reveal methylation pattern disruption in myeloid and lymphoid malignancies, correlating with clonal burden fluctuations during leukemia treatment. In non-cancer cohorts, we observed that normally stable CpG sites exhibited progressive instability with advancing age, which was also associated with the onset of cardiovascular disease and decreased survival rates. This study links DNA methylation instability with the expansion of risk-prone blood cells and highlights its role as a biomarker for both cancer and cardiovascular disease.

genetics↗

bioRxiv2023

Correlation of two different devices for the evaluation of primary implant stability depending on dental implant length and bone density: an in vitro study

Non-invasive objective implant stability measurements are needed to determine the appropriate timing of prosthetic fitting after implant placement. We compared the early implant stability results obtained using resonance frequency analysis (RFA) and damping capacity analysis (DCA) depending on the implant length and bone density. Total 60, 4.0 mm diameter implants of various lengths (7.3 mm, 10 mm, and 13 mm) were used. Group I and Group II had implants placed in an artificial bone model with a uniform density of 15 PCF (0.24 g/cm3) and 30 PCF (0.48 g/cm3), respectively. RFA was performed using an Osstell(R) Beacon+; DCA was performed using Anycheck(R). Measurements were repeated five times for each implant. Statistical significance was set at P <0.05. In Group I, bone density and primary implant stability were positively correlated, while implant length and primary implant stability were positively correlated. In Group II, the implant stability quotient (ISQ) and implant stability test (IST) values in did not change significantly above a certain length. Primary implant stability was positively correlated with bone density and improved with increasing implant length at low bone densities. Compared with the Osstell(R) Beacon+, the simplicity of Anycheck(R) was easy to use and accessible.

bioengineering↗

bioRxiv2019

An integrated computational and experimental study to elucidate Staphylococcus aureus metabolism

Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understanding its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 non-essential mutants from the Nebraska Transposon Mutant Library was performed and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 840 metabolic genes, 1442 metabolites, and 1566 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data and, therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

systems biology↗