Human Genomics最新文献

Background: A rapid decline in forced expiratory volume in one second (FEV₁) can lead to chronic airway disease and is associated with poor prognosis. This study aims to identify single nucleotide polymorphisms (SNPs) associated with the annual rate of FEV₁ decline, investigate the differential effects based on various phenotypes, and develop a genetic risk score (GRS) for predicting rapid FEV₁ decline.

Methods: We conducted a genome-wide association study (GWAS) within the prospective Korean Genome and Epidemiology Study (KoGES) cohort. The association of each SNP with the annual decline rate of FEV₁ was assessed using a linear regression model. We employed elastic-net regression analysis to identify SNPs associated with FEV₁ decline and developed a GRS.

Results: In 7,357 participants, we identified 21 SNPs associated with rapid FEV₁ decline. Elastic-net regression model highlighted 12 SNPs with high likelihood of replication. Subgroup analyses revealed SNPs with different effects on FEV₁ decline based on sex, age, and smoking history. The GRS, derived from the top 1,000 SNPs, was significant associated with annual FEV₁ change (adjusted R² = 0.308). A GRS below - 17 indicated an accelerated decline in FEV₁.

Conclusion: Our study identified novel SNPs associated with FEV₁ decline and developed a GRS that could help predict individuals at higher risk of rapid FEV₁ decline. The influence of genetic factors on the annual rate of decline in FEV₁ may vary depending on phenotypic characteristics.

Background: BRCA1 and BRCA2, known as tumor suppressor genes, have been shown to increase the risk of developing breast and ovarian cancer. Intronic variants that can result in aberrant splicing events are classified as Variant uncertain significance until the functional impact is clearly predicted or confirmed. Thus, the purpose of this study is to assist in the interpretation of splicing variants and to reclassify the clinical significance of non-coding region variants that are classified as VUS.

Results: The variants BRCA1:c.80 + 3_80 + 5del, BRCA1:c.548-15G > A, BRCA2:c.8755-19 A > G, and BRCA2:c.317-10 A > G were ultimately chosen. Through the minigene assay, we can observe exon skipping in BRCA1:c.80 + 3_80 + 5del, intron retention in BRCA1:c.548-15G > A and BRCA2:c.8755-19 A > G. This study performed a minigene assay using the remaining samples from the patients tested at Seoul National University Hospital. Wild-type and mutant type minigene constructs were designed respectively for each variant sample.

Conclusion: Finally aberrant splicing patterns were found in three of the four variants: BRCA1:c.80 + 3_80 + 5del, BRCA1:c.548-15G > A, BRCA2:c.8755-19 A > G that were previously classified as VUS through experimental analysis. As a result, we reclassify BRCA1;c.80 + 3_80 + 5del as LP and we classify BRCA1;c.548-15G > A and BRCA2;c.8755-19 A > G as VUS.

Background: Alzheimer's disease (AD) is the leading cause of dementia affecting 55 million people worldwide. The pathological hallmarks of AD, beta-amyloid (Aβ) plaques and neurofibrillary tangles (NFT), follow distinct stereotypical patterns of progression across brain regions and trigger a multicellular response that ultimately leads to neuronal loss and cognitive decline. Despite the uniform spread of Aβ plaque across the cortex during AD progression, different regions demonstrate varying levels of vulnerability and resilience to temporal Aβ plaque induced changes, such as NFT accumulation. There is a critical gap in our understanding of the cell types and molecular mechanisms that underlie these region-specific differences in resilience to Aβ plaque induced changes. In this study, we hypothesized that brain region and cell type specific transcriptional responses within the Aβ microenvironment, and more broadly within the grey matter, may contribute to this variation.

Results: We carried out matched multi-region spatial transcriptomics and Aβ immunofluorescence staining from the entorhinal, occipito-temporal, dorsolateral prefrontal, and striate cortices from two individuals with Braak III and Thal 4 AD. Spatiotemporal comparisons of cell type proportions, gene expression, and cell-cell communication revealed differences in the vulnerability of somatostatin and somatostatin chondrolectin inhibitory neurons and the expression of endosomal and lysosomal trafficking and metallothionein genes within the Aβ plaque microenvironment. We also observed variations in blood-brain-barrier dysfunction, fibroblast growth factor signaling, and vascular impairment and repair related cell-cell communication networks within the grey matter across the four regions.

Conclusions: Our results demonstrate the value of simultaneously profiling AD-omic and spatial modalities in multiple regions to elucidate how cortical region-specific differences contribute to selective vulnerability and resilience during neurodegeneration. These cortical region and Aβ microenvironment-specific transcriptional changes during AD neurodegeneration highlight the potential for spatially targeted therapeutic approaches.

Background: The causal bridge from environmental exposure to endometriosis (Ems) biology remains incompletely defined. Di(2-ethylhexyl) phthalate (DEHP) is repeatedly implicated in elevated Ems risk, yet actionable molecular anchors linking exposure to phenotype are scarce.

Methods: We established a multi-layered pipeline centered on DEHP. Comprehensive in silico target prediction across ChEMBL, PharmMapper, and SwissTargetPrediction yielded 1364 de-duplicated candidate proteins. Three transcriptomic cohorts (GSE51981, GSE6364, GSE7305) were integrated and analyzed using differential expression and Weighted Gene Co-expression Network Analysis (WGCNA) to derive a 229-gene, high-confidence Ems set. The intersection identified 17 overlapping genes, which were contextualized by protein-protein interaction (PPI) networks and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment. Interpretable machine learning with SHapley Additive exPlanations (SHAP) prioritized a core signature, followed by molecular docking and 100-ns molecular dynamics (MD) simulations to validate binding feasibility and temporal stability.

Results: The 17-gene overlap formed a compact functional subnetwork aligned with a "membrane-lipid homeostasis to vesicular transport to detoxification/de-esterification" axis. Classifiers showed robust discrimination across training and external cohorts (most area under the receiver operating characteristic curve [AUC] > 0.75), while single-gene receiver operating characteristic (ROC) analyses highlighted UGT8 (AUC = 0.869) and EPHX1 (0.853) as highly transferable. SHAP prioritized a seven-gene signature-ELOVL6, LYPLA1, UGT8, SLC1A5, HMGCR, EPHX1, and VAMP2-and revealed non-linear relationships, including ELOVL6-UGT8 synergy, HMGCR-LYPLA1 antagonism, and EPHX1-SLC1A5 context dependence. Docking supported pocket complementarity with ~ 2.2-3.3 Å hydrogen bonds plus extensive hydrophobic/π contacts; MD confirmed stable, compact, and persistent binding for UGT8-DEHP, ELOVL6-DEHP, and HMGCR-DEHP over 100 ns.

Conclusions: This study establishes a comprehensive workflow spanning from chemical exposure identification to target discovery, disease network mapping, interpretable computational modeling, and structural/dynamical validation. We propose a DEHP-Ems regulatory framework underpinned by lipid metabolism, vesicular trafficking, and detoxification pathways. The resulting seven-gene signature provides a clinically applicable panel for diagnostic stratification and highlights potential therapeutic entry points, particularly along the HMGCR axis and via SLC1A5-mediated glutamine uptake. These findings lay the groundwork for future mechanistic studies in primary cell systems, organoid models, in vivo experiments, and prospective clinical validation.

Familial hypercholesterolemia (FH) is a genetic disorder driven in part by mutations in three genes that encode components of the cholesterol pathway: LDLR, APOB, and PCSK9. However, the majority of FH genetics has been performed in individuals of European descent. Here, we leveraged a cohort of 300 patients from the Mexican FH registry to understand how rare, high liability alleles and common variants might contribute to shaping individual risk. Using a combination of whole exome and of short- and long-read whole genome sequencing, we report three key findings. First, we observed that rare pathogenic point mutations and structural variants in all known FH genes, together with variants in APOE, CREB3L3, and PLIN1, contribute to a molecular FH diagnosis in 67% of families, including novel gene-disruptive copy number variants (CNVs) which arose in a native American background. Second, ancestry-adjusted polygenic risk score analysis identified a significant liability for coronary artery disease, hypertension, LDL, HDL, and Type 2 Diabetes. The polygenic signal for LDL was present in patients with rare, pathogenic FH mutations and was more prominent in individuals bereft of a molecular FH diagnosis. Finally, we report both a whole-gene duplication and common, non-coding variants in a novel locus, PDZK1, which contribute to the genetic burden of FH, a finding we replicated in the UK Biobank (UKB). Together, our analyses illustrate the value of genetic studies in non-European populations and reinforce the notion that individual risk to disease can arise from both rare, large effect alleles (alone or in combination across genes) and common variants that increase the mutational burden of a biological system.

Background: Thyroid eye disease (TED) is an autoimmune disorder characterized by persistent inflammation around the periphery and within the orbit, potentially driven by hypoxic conditions. Effective biomarkers and precise predictive models are still lacking for the early diagnosis of TED.

Methods: Bulk RNA sequencing was conducted on peripheral blood samples from TED patients, Graves' hyperthyroidism (GH) patients without ocular involvement, and healthy controls (HC). Differentially expressed genes between TED and HC, hypoxia-related genes and genes identified through weighted gene co-expression network analysis (WGCNA) were intersected to identify candidate biomarkers. Subsequently, nine machine learning algorithms were applied to screen for critical hypoxia-related TED diagnostic genes (HRTDGs). A diagnostic model based on HRTDG score (HRTDGS) was constructed using logistic regression analyses and then evaluated. TED patients were categorized into high and low HRTDGS groups based on the median score. Distinct immunological profiles and underlying pathological functions were investigated between two groups. Single cell RNA sequencing (scRNA-seq) data further explored HRTDGs' roles at cellular level.

Results: Hypoxia was identified as a prominent feature of TED. Among all machine learning algorithms, random forest achieved the highest area under curve (AUC) and was used to identify three key HRTDGs: EGFR, PIK3CB, and CREBBP. The HRTDGS model was then established and found to be an independent predictive factor for TED diagnosis (odds ratio (OR): 2.656, 95% confidence interval (CI): 1.735-4.324, p < 0.001). The model demonstrated high diagnostic accuracy in distinguishing TED from both HC (AUC = 0.785 in training set and 0.905 in testing set) and GH (AUC = 0.935). TED patients with higher HRTDGS exhibited elevated levels of thyrotropin receptor antibodies (TRAb) and abnormal free thyroxine (fT4), along with greater infiltration by activated CD4 + T cells and natural killer (NK) cells. ScRNA-seq revealed elevated expression of HRTDGs in fibroblasts, NK and CD4 + T cells, with enriched EGFR signaling pathway between T/NK cells and fibroblasts in TED compared to HC.

Conclusions: This study presents a novel hypoxia biomarkers-based diagnostic model for TED, facilitating early detection and offering valuable insights into potential therapeutic targets.