Macrogen, Inc. is a South Korean public biotechnology company. The company's headquarters are located in Seoul. The company was founded in 1997 by Jeong-sun Seo, a professor at Seoul National University. It was venture capital-backed until its initial public offering (IPO) and subsequent listing on the KOSDAQ market in 2000, making it the first Korean biotechnology firm to raise funds through an IPO. The company announced plans in 2010 to map the "Korean genome" based on the notion that United States-backed genome mapping efforts up to that time were representative of the "Caucasian genome" rather than the "Human genome" as claimed. By 2015, the company was described by one news outlet as "a global leader in personalized genomic medicine".
BACKGROUND: GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the development of polygenic risk scores (PRSs) for estimating genetic risk based on common variant burden. However, GWASs have limitations in analyzing rare variants due to insufficient statistical power, thereby constraining PRS performance. METHODS: We conducted whole-genome sequencing of 1752 Japanese patients with CAD and 3019 controls. A machine learning-based analytical framework was applied to identify and interpret rare genetic variants associated with CAD pathogenesis. RESULTS: This approach identified 59 CAD-related genes, including known causal genes such as LDLR and those not previously captured by GWASs. A rare variant-based risk score derived from the framework demonstrated distinct clinical characteristics compared with a conventional common variant-based PRS. The rare variant-based risk score significantly discriminated CAD cases and predicted cardiovascular mortality in an independent cohort. Furthermore, combining the rare variant-based risk score with the traditional PRS improved CAD prediction compared with the PRS alone (area under the curve, 0.66 versus 0.61; P =0.007). CONCLUSIONS: These findings underscore the distinct and complementary value of the rare variant-based risk score compared with the conventional PRS, highlighting the enhanced predictive power achieved through their integration. This comprehensive approach proposes broader genetic profiling, offering substantial potential for improved clinical risk stratification and personalized prevention strategies.
Objective:Adolescence is a critical developmental stage during which mental health vulnerabilities often emerge. Traditional self-report methods are insufficient to capture the complexity of emotional and physiological responses, underscoring the need for data-driven, personalized mental health strategies. This study aimed to develop and validate a structured multimodal data collection system for adolescents to support the future advancement of precision mental health care. Methods:This study was conducted as the baseline phase of a longitudinal panel study designed to construct and validate a structured multimodal dataset for adolescent mental health research. A total of 74 adolescents aged 11-15 years from schools and community facilities in Korea was selected through convenience sampling. Multimodal data were collected by integrating six data types: self-reported surveys, electroencephalography (EEG), heart rate variability (HRV), genotyping, microbiome data, and video-based psychological counseling. Data collection was standardized through a three-phase protocol (pre-, on-site, and post-assessment), and participant privacy was protected via pseudonymization based on international standards. Variables were systematically labeled and structured to enable cross-modality analysis. Statistical analyses, including correlation and descriptive statistics, were performed to examine preliminary relationships across modalities. Results:The study successfully constructed a comprehensive dataset encompassing biological and psychosocial indicators from 74 adolescents. Preliminary analysis revealed statistically significant associations between survey-based BMI and both genomic data (ρ = 0.30, p < 0.01) and microbiome-based obesity indicators (ρ = 0.27, p < 0.05), whereas other psychological constructs (e.g., stress, resilience) showed non-significant cross-modal correlations. Conclusions:This study presents a replicable framework for collecting rich, multimodal data from adolescents in real-world settings. By enabling integrative analysis of biological and psychosocial variables, the dataset lays the groundwork for personalized mental health prediction and intervention strategies. Future research should expand longitudinally and optimize context alignment to improve predictive precision and clinical utility.
In vivo base editing is advancing toward clinical translation for hereditary deafness, yet efficacy has been established almost exclusively after neonatal intervention, a stage corresponding to human mid-gestation. Whether correction remains effective in a mature, progressively degenerating cochlea, and what sets the limits of such intervention, remain unresolved. We generated a full-length humanized mouse model carrying the MPZL2 c.220C > T East Asian founder variant (FL- hMPZL2 Q74X/Q74X ), which reproduced patient-derived nonsense-mediated decay and recapitulated the natural history of DFNB111, revealing a sequential pathology in which Deiters' cell (DC) disorganization precedes DC–outer hair cell (OHC) degeneration. Using a dual-adeno-associated virus (AAV)-ie-K558R system encoding PAM-flexible ABE8eWQ–SpRY, we treated mice after hearing onset and, subsequently, after cochlear maturation. When DC architecture remained intact, treatment durably improved low- to mid-frequency hearing, restored MPZL2 expression, delayed DC disorganization, preserved DC and OHC survival, and shifted DC-associated transcriptional programs toward a wild-type-like state. Once DC disorganization emerged, treatment conferred no benefit despite comparable on-target editing. Extracochlear editing was minimal, with no organ toxicity, cytotoxicity, or inflammatory responses in human cells. Therefore, base editing remains effective after cochlear maturation in MPZL2 -associated DFNB111, with the therapeutic window bounded by cochlear structural integrity rather than chronological age or editing competence.
PURPOSE:Recent studies have revealed a diverse gastric microbiota beyond Helicobacter pylori, suggesting a role in gastric cancer (GC). We aimed to investigate the composition and characteristics of the microbiota in GC and non-cancerous gastric mucosa (NC), with a particular focus on their relationship to molecular subtypes. MATERIALS AND METHODS:We conducted 16S rRNA sequencing and whole transcriptomic analysis on fresh-frozen GC and NC tissue samples from 192 GC patients, as well as saliva samples from 12 GC patients and 18 healthy individuals. Microsatellite instability (MSI), Epstein-Barr virus (EBV) in situ hybridization, and immunohistochemistry for p53 and E-cadherin were used to define molecular subtypes. RESULTS:GC tissues exhibited significantly higher diversity compared to matched NC tissues, with microbial profiles marked by decreased Helicobacter and increased Streptococcus, Prevotella, and Lactobacillus. Saliva samples predominantly contained oral bacteria and exhibited distinct microbial profiles from gastric tissues. In GC tissue, Helicobacter abundance was negatively correlated with key immune checkpoint genes (CTLA-4, PDCD1, CD274, and LAG3), whereas Prevotella, Streptococcus, and Fusobacterium were positively correlated. MSI-high and EBV-positive subtypes showed lower levels of Helicobacter but higher levels of Lactobacillus, Prevotella, and Streptococcus compared to the epithelial-mesenchymal transition-like subtype. Notably, within MSI-high GC, a subgroup characterized by Lactobacillus-enriched and otherwise microbiota-depleted profiles was significantly associated with poorer overall and disease-free survival. CONCLUSION:These findings underscore distinct microbial patterns across GC molecular subtypes, suggesting potential biomarkers for GC diagnosis and treatment.
