Search

With the release of large-scale genomic resources from ancient and modern populations, advancements in computational biology tools, and the enhancement of data mining capabilities, the field of genomics is undergoing a revolutionary transformation. These advancements and changes have not only significantly deepened our understanding of the complex evolutionary processes of human origins, migration, and admixture but have also unveiled the impact of these processes on human health and disease. They have accelerated research into the genetic basis of human health and disease and provided new avenues for uncovering the evolutionary trajectories recorded in the human genome related to population history and disease genetics. The ancestral recombination graph (ARG) reconstructs the evolutionary relationships between genomic segments by analyzing recombination events and coalescence patterns across different regions of the genome. An ARG provides a record of all coalescence and recombination events since the divergence of the sequences under study and specifies a complete genealogy at each genomic position, which is the ideal data structure for genomic analysis. Here, we review the theoretical foundations and research advancements of the ARG, and explore its translational applications and future prospects across various disciplines, including forensic genomics, population genetics, evolutionary medicine, and medical genomics. Our goal is to promote the application of this technique in genomic research, thereby deepening our understanding of the human genome.

Elucidating the quality markers(Q-markers) of traditional Chinese medicines is essential for understanding the mechanisms of action and promoting the rational use of traditional Chinese medicines as well as for developing traditional Chinese medicine-derived drugs. Studies have shown that surface plasmon resonance(SPR) is promising in this field. This study proposed a method based on pull-down with SPR chips to predict the Q-markers of Angong Niuhuang pills(AGNHP). Firstly, 71 main chemical components of AGNHP were analyzed by UPLC-Q-TOF-MS, and then network pharmacology was employed to predict the potential targets of AGNHP against stroke. Secondly, the STAT3 protein chip was constructed, and the extract of AGNHP was recovered by pull-down of the SPR system for STAT3 ligand. The potential active ingredients were collected, enriched, and identified as coptisine, palmatine, epiberberine, berberine, worenine, demethyleneberberine, jatrorrhizine, tetrahydrocoptisine, baicalein, and baicalin methyl ester. Next, the affinity constants of the 10 active ingredients were determined as 44.7, 44, 58.1, 51.3, 39.7, 32.1, 49.2, 69.1, 19.7, and 24.9 μmol·L~(-1), respectively. The molecular docking results showed that the 10 compounds could compete for binding with STAT3. This is the first report that SPR combined with UPLC-Q-TOF-MS is reliable and feasible for determining the active ingredients of AGNHP at the molecular level from complex systems. STAT3 could be used as a potential target for the biological quality evaluation of AGNHP.