Macrogen (South Korea)

ObjectiveAdolescence 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.MethodsThis 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.ResultsThe 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.ConclusionsThis 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.

The infant gut microbiome is essential for long-term health and is linked to atopic dermatitis (AD), although the underlying mechanisms are not fully understood. This study investigated gut microbiome-host interactions in 31 infants with AD and 29 healthy controls using multi-omics approaches, including metagenomic, host transcriptomic, and metabolomic analyses. Microbial diversity was significantly altered in AD, with Bifidobacterium longum and Clostridium innocuum associated with these changes. At the strain-level, only B. longum differed significantly between groups, with pangenome analyses identifying genetic variations potentially affecting amino acid and lipid metabolites. Notably, B. longum subclade I, which was more prevalent in healthy controls, correlated with host transcriptomic pathways involved in phosphatidylinositol 3-kinase-AKT signaling and neuroactive ligand-receptor pathways, as well as specific metabolites, including tetrahydrocortisol and ornithine. These findings highlight the role of B. longum strain-level variation in infants, offering new insights into microbiome-host interactions related to AD.

Camels possess exceptional adaptability, allowing them to withstand extreme temperatures in desert environments. They conserve water by reducing their metabolic rate and regulating body temperature. The heart of the camel plays a crucial role in this adaptation, with specific genes expressed in cardiac tissue that are essential for mammalian adaptation, regulating cardiac function and responding to environmental stressors. One such gene, nebulin‐related‐anchoring protein (NRAP), is involved in the assembly of myofibrils and the transmission of force within the heart. In our study of the NRAP gene across various livestock species, including three camel species, we identified a camel‐specific exon region in the NRAP transcripts. This additional exon (exon 4) contains an open reading frame predicted in camels. To investigate its function, we generated knock‐in mice expressing camel NRAP exon 4. These ‘camelized mice’ exhibited normal phenotypic characteristics compared with wild‐type mice but showed elevated body temperatures under cold stress. Transcriptome analyses of the hearts from camelized mice under cold stress revealed differentially expressed inflammatory cytokine genes, known to influence cardiac function by modulating the contractility of cardiac muscle cells. We propose further investigations utilizing these camelized mice to explore these findings in greater depth.

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5’-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

The genomic structures of Vigna hirtella Ridl. and Vigna trinervia (B.Heyne ex Wight & Arn.) Tateishi & Maxted, key ancestral species of the allotetraploid Vigna reflexo-pilosa var. glabra (Roxb.) N.Tomooka & Maxted, remain poorly understood. This study presents a comprehensive genomic comparison of these species to deepen our knowledge of their evolutionary trajectories. By comparing the genomic profiles of V. hirtella and V. trinervia with those of V. reflexo-pilosa, we investigate the complex genomic mechanisms underlying allopolyploid evolution within the genus Vigna. Comparison of the chloroplast genome revealed that V. trinervia is closely related to V. reflexo-pilosa. De novo assembly of the whole genome, followed by synteny analysis and Ks value calculations, confirms that V. trinervia is closely related to the A genome of V. reflexo-pilosa, and V. hirtella to its B genome. Furthermore, the comparative analyses reveal that V. reflexo-pilosa retains residual signatures of a previous polyploidization event, particularly evident in higher gene family copy numbers. Our research provides genomic evidence for polyploidization within the genus Vigna and identifies potential donor species of allotetraploid species using de novo assembly techniques. Given the Southeast Asian distribution of both V. hirtella and V. trinervia, natural hybridization between these species, with V. trinervia as the maternal ancestor and V. hirtella as the paternal donor, seems plausible.

Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF’s RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted.

DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance.