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Nature Papers Examine Taxonomic Gaps in Plant Sequencing, SARS-CoV-2-Human Interactome

While the pace and quality of plant genome sequencing has increased significantly over the past 20 years, major taxonomic gaps exist and the field continues to be dominated by affluent nations in the Global North and China, scientists from Michigan State University and elsewhere write in Nature Plants this week. Since the genome assembly of Arabidopsis thaliana — the first for any land plant — was published in 2000, hundreds of geographically diverse plant genomes have been sequenced, assembled, and made publicly available. Yet there are numerous disconnects between the native range of focal species and the national affiliation of the researchers studying them, according to the researchers. "These dynamics are rooted in historical colonialism and economic barriers to entry and are being perpetuated by contemporary 'parachute science,'" they write, recommending that local communities and indigenous knowledge associated with the global reservoir of plant diversity form the backbone of plant genome collaborations. "Ultimately, a diverse, thriving discipline with empowered researchers across continents, regardless of socioeconomic status, will yield the greatest potential to meet the economic, social and evolutionary challenges facing twenty-first-century plant science," they add.

A three-dimensional SARS-CoV-2-human interactome is reported by Cornell University scientists in this week's Nature Methods, providing a resource to inform hypothesis-driven exploration of the mechanisms of SARS-CoV-2 pathology and host response. The emergence of new viral agents is driven by the evolution of interactions between viral proteins and host targets. In the case of SARS-CoV-2, its greater infectivity as compared to SARS-CoV-1 is partly due to rapid evolution along the interface between the spike protein and the human receptor ACE2, leading to increased binding affinity. To help explore how pathogen-host interactions might affect transmission and virulence in the COVID-19 pandemic, the researchers used interface prediction and molecular docking to build a model of human and SARS-CoV-2 interactions. They then performed downstream meta-analyses to, among other things, investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces and predict changes in binding affinity by these mutations or variants.