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The live poultry trade is thought to play an important role in the spread and maintenance of highly pathogenic avian influenza A viruses (HP AIVs) in Asia. Despite an abundance of small-scale observational studies, the role of the poultry trade in disseminating AIV over large geographic areas is still unclear, especially for developing countries with complex poultry production systems. Here we combine virus genomes and reconstructed poultry transportation data to measure and compare the spatial spread in China of three key subtypes of AIV: H5N1, H7N9, and H5N6. Although it is difficult to disentangle the contribution of confounding factors, such as bird migration and spatial distance, we find evidence that the dissemination of these subtypes among domestic poultry is geographically continuous and likely associated with the intensity of the live poultry trade in China. Using two independent data sources and network analysis methods, we report a regional-scale community structure in China that might explain the spread of AIV subtypes in the country. The identification of this structure has the potential to inform more targeted strategies for the prevention and control of AIV in China.
Despite contingency in life's history, the similarity of evolutionarily convergent traits may represent predictable solutions to common conditions. However, the extent to which overall gene expression levels (transcriptomes) underlying convergent traits are themselves convergent remains largely unexplored. Here, we show strong statistical support for convergent evolutionary origins and massively parallel evolution of the entire transcriptomes in symbiotic bioluminescent organs (bacterial photophores) from two divergent squid species. The gene expression similarities are so strong that regression models of one species' photophore can predict organ identity of a distantly related photophore from gene expression levels alone. Our results point to widespread parallel changes in gene expression evolution associated with convergent origins of complex organs. Therefore, predictable solutions may drive not only the evolution of novel, complex organs but also the evolution of overall gene expression levels that underlie them.
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