Summary of Science publications, focusing on contents relevant to AI and life sciences
Researchers investigated how *Brassica rapa* — a species spanning vegetables as different as Chinese cabbage, turnip, and pak choi — diversified so rapidly into distinct subspecies and growth forms. They sequenced 1,720 accessions and built 11 complete, gap-free telomere-to-telomere genome assemblies representing seven key subspecies, then integrated these with 20 previously published genomes into a pangenome. This effort uncovered nearly 7,000 previously unknown genes, 110 complete centromere and pericentromere sequences, and five new repetitive satellite elements linked to specific morphotypes and broader *Brassica* evolution. Pangenome-wide association analysis pinpointed a gene, *BrLH1*, as a driver of leafy-head formation. The study shows that structural rearrangements in centromeres, satellites, and functional genes have fueled rapid morphological divergence over a surprisingly short cultivation history, offering breeders a detailed genomic toolkit for improving *Brassica* crops.
Researchers used DNA origami structures to arrange HIV antigens in precise geometric patterns, testing whether this spatial organization could direct the immune system toward producing more targeted antibodies. When mice were immunized with these scaffolded antigen displays, the animals mounted antibody responses that were more focused on specific viral epitopes compared with unscaffolded controls. The approach exploits DNA origami's ability to position proteins at defined locations and orientations on a nanoscale scaffold, giving immunologists fine control over how antigens are presented to immune cells. Generating focused, site-directed antibody responses is a long-standing challenge in HIV vaccine development, since the virus's envelope protein conceals its most vulnerable regions. This work suggests that programmable nanoscale scaffolds could be a practical platform for steering immune responses toward conserved, functionally important sites on HIV—and potentially on other difficult vaccine targets.
Plant genomes vary considerably even within a single species, meaning that one reference genome cannot capture the full picture. Pangenomics addresses this by integrating multiple genomes to reveal the complete spectrum of genetic variation across a population or species. This review surveys the growing landscape of plant pangenome studies, examining how different construction strategies — from graph-based approaches to iterative assemblies — are being applied across crops and wild relatives. Key findings highlight how pangenomes uncover presence-absence variants, structural differences, and rare alleles invisible to single-reference analyses, with direct implications for understanding domestication, adaptation, and disease resistance. The authors discuss how these resources are accelerating breeding by pinpointing functionally important variants that conventional genomics would miss. As sequencing costs fall and assembly quality improves, plant pangenomes are becoming practical tools for connecting genomic diversity to agronomically and evolutionarily meaningful traits.
Sugarcane (Saccharum) has one of the most complex plant genomes, with multiple sets of chromosomes at varying ploidy levels, making standard genomic analysis extremely difficult. Researchers built a multiscale pangenome graph that merges nine sugarcane genome assemblies—covering modern cultivars and ancestral founding species—into a single reference structure. Each chromosome group captures dozens to hundreds of haplotypes and tens to hundreds of thousands of gene variants. Using this framework alongside data from 417 Saccharum accessions, the team detected signatures of convergent selection across the genus and pinpointed a homolog of the Andropogoneae gene TB1, which controls shoot branching, as a candidate for gene editing to increase cane yield. They also performed dosage-aware genome-wide association studies, improving heritability estimates and uncovering loci tied to sugar content and leaf angle. The approach provides a reusable blueprint for graph-based genomics in sugarcane and other polyploid crops.
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