Yi-Ming Weng, Pathour R Shashank, R Keating Godfrey, David Plotkin, Brandon M Parker, Tyler Wist, Akito Y Kawahara
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引用次数: 0
Abstract
Background: Understanding the genotype of pest species provides an important baseline for designing integrated pest management (IPM) strategies. Recently developed long-read sequence technologies make it possible to compare genomic features of nonmodel pest species to disclose the evolutionary path underlying the pest species profiles. Here we sequenced and assembled genomes for 3 agricultural pest gelechiid moths: Phthorimaea absoluta (tomato leafminer), Keiferia lycopersicella (tomato pinworm), and Scrobipalpa atriplicella (goosefoot groundling moth). We also compared genomes of tomato leafminer and tomato pinworm with published genomes of Phthorimaea operculella and Pectinophora gossypiella to investigate the gene family evolution related to the pest species profiles.
Results: We found that the 3 solanaceous feeding species, P. absoluta, K. lycopersicella, and P. operculella, are clustered together. Gene family evolution analyses with the 4 species show clear gene family expansions on host plant-associated genes for the 3 solanaceous feeding species. These genes are involved in host compound sensing (e.g., gustatory receptors), detoxification (e.g., ABC transporter C family, cytochrome P450, glucose-methanol-choline oxidoreductase, insect cuticle proteins, and UDP-glucuronosyl), and digestion (e.g., serine proteases and peptidase family S1). A gene ontology enrichment analysis of rapid evolving genes also suggests enriched functions in host sensing and immunity.
Conclusions: Our results of family evolution analyses indicate that host plant adaptation and pathogen defense could be important drivers in species diversification among gelechiid moths.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.