As the global population ages, the development of methods that can accurately quantify individual aging status and overcome the limitations of chronological age assessment has become an important need in aging research and precision medicine. Biological age serves as an important tool for assessing an individual's true physiological aging process and disease risk by integrating multi-omics data and organ-specific biomarkers. In this review, we systematically summarize the development of methods for assessing aging, with a focus on the construction strategies and evolution of aging clocks based on epigenetic, transcriptomic, proteomic, and metabolomic markers. We also discuss emerging approaches for organ-specific aging assessment based on imaging data and the integration of multi-omics data. By synthesizing evidence for the clinical applications of these approaches in the early screening and risk stratification of aging-related diseases, personalized interventions, and health management, we further highlight the importance and translational potential of biological age in precision aging assessment and intervention.
Vascular development begins with vasculogenesis during early embryogenesis. This process is controlled by a highly integrated, multi-layered molecular network. However, the role of post-transcriptional regulation in this context remains poorly understood. Cytoplasmic poly(A) binding protein 1a (Pabpc1a) is a key RNA-binding protein that regulates gene expression at the post-transcriptional level and participates in many physiological and pathological processes. Its role in vascular development, however, is still unclear. In this study, we first found that pabpc1a is highly expressed in endothelial cells during the critical window of vasculogenesis. We then generated a pabpc1a knockout model using CRISPR/Cas9. Functional analyses showed that loss of pabpc1a disrupted the expression of genes involved in arterial-venous specification and altered the arterial-to-venous diameter ratio. In contrast, overexpression of pabpc1a effectively rescued the vascular defects. Mechanistically, transcriptomic analysis revealed widespread gene expression changes in endothelial cells lacking pabpc1a. Pathways related to translation, ribosome biogenesis, and energy metabolism were significantly downregulated. These findings suggest that Pabpc1a regulates vasculogenesis by coordinating protein synthesis and metabolic homeostasis. In summary, our study demonstrates Pabpc1a as an essential post-transcriptional regulator during vasculogenesis and provides new insight into the regulatory network governing vascular development.
As the largest agricultural producer in the European Union, France has established a mature and systematic livestock and poultry genetic resources management system. Based on comprehensive domestic legislation and effectively aligned with EU regulations, this system forms a governance structure characterized by clear responsibilities and multi-stakeholder collaboration. National regulatory authorities, public research institutions, industry organizations, and breeding service institutions work in a coordinated manner, with close linkages among the government, research community, and industry to ensure the conservation, dynamic monitoring, and sustainable utilization of genetic resources. For different livestock and poultry species such as ruminants, pigs, and poultry, France has developed differentiated genetic improvement pathways, supported by quality assurance and lifetime animal traceability mechanisms. This paper puts forward targeted suggestions for the conservation of livestock and poultry genetic resources in China, focusing on key areas such as forming a multi-stakeholder collaborative governance pattern, improving conservation mechanisms, promoting the layout of genetic improvement adapted to different livestock and poultry species, and ensuring quality traceability.
Suicide is defined as an intentional act of ending one's own life. Suicide attempt (SA) is a significant risk factor for suicide death. Research on SA has progressed from socio-psychological perspectives to the molecular and genetic levels. While the biological mechanisms underlying genome-wide association studies (GWAS) identified risk loci remain largely unclear. To investigate the potential risk mechanisms, we constructed a systematic analytic pipeline using brain protein quantitative trait locus (pQTL) datasets (Banner, N=152; ROSMAP, N=376), a brain expression quantitative trait locus (eQTL) datasets (N=452), and SA GWAS summary statistics (Ncase=35,786, Ncontrol=779,392). We performed proteome-wide association study (PWAS), Mendelian randomization (MR), Bayesian colocalization analysis, transcriptome-wide association study (TWAS), and multi-marker analysis of genomic annotation (MAGMA) to systematically identify and screen for novel genetically supported candidate proteins related to the biological mechanism of SA in the brain. For functional annotation, we used the GeneMANIA to bulid a functional prediction network integrating co-expression, physical interactions, and pathway colocalization to identify core proteins. PWAS identified three brain proteins whose genetically predicted abundance was significantly associated with SA. Among them, GMPPB was prioritized as putative causal protein, supported by MR analysis (false discovery rate, FDR<0.05) and Bayesian colocalization analysis (posterior probability PPH4≥0.8). Specifically, higher genetically predicted GMPPB protein levels were associated with increased risk of SA. Although our analyses primarily relied on datasets from European-ancestry populations, the shared genetic architecture across populations and the generalizability of genome-wide data analytical approaches suggest that our findings may still provide useful insights into the biological mechanisms underlying SA and help inform the development of intervention strategies and genetic counseling in Chinese populations.
The Han Chinese population exhibits a complex genetic structure characterized by subtle yet discernible regional differentiation. Elucidating this fine-scale population structure and developing robust models for biogeographical ancestry inference are of great significance for revealing population evolutionary patterns and achieving precise ancestry inference. However, ancestry inference models specifically tailored to the genetic diversity within the domestic Han Chinese population remain scarce. In this study, we analyzed high-density SNP data from 1,229 Han Chinese individuals across eight provinces to investigate the correlation between genetic variation and geographic distribution, and to construct a machine learning-based model for regional ancestry prediction. After stringent quality control (including linkage disequilibrium pruning), we retained 208,193 SNPs for downstream analysis. Principal component analysis (PCA) and ADMIXTURE clustering revealed measurable genetic stratification corresponding to geography, supporting the delineation of seven distinct genetic clusters within the Han population. Leveraging the top principal components as features, we trained and compared multiple classifiers-XGBoost, random forest, and K-nearest neighbors-via five-fold cross-validation on the reference set, with model performance evaluated using both top-rank prediction accuracy and likelihood ratio (LR)-based metrics. The results showed that the PCA-XGBoost model achieved the optimal prediction performance in the reference set, with a first-rank prediction accuracy of 87.66% and an LR-based accuracy of 96.87%. In independent test sets, the PCA-XGBoost model maintained strong performance (first-rank prediction accuracy >85%; LR-based accuracy >95%), demonstrating excellent generalizability and stability. In summary, the PCA-XGBoost predictive model developed in this study demonstrates high efficiency, robustness, and accuracy, offering a reliable methodological tool for research in population genetics and forensic genetics.
