Unzipped chromosome-level genomes reveal allopolyploid nematode origin pattern as unreduced gamete hybridization.

The formation and consequences of polyploidization in animals with clonal reproduction remain largely unknown. Clade I root-knot nematodes (RKNs), characterized by parthenogenesis and allopolyploidy, show a widespread geographical distribution and extensive agricultural destruction. Here, we generated 4 unzipped polyploid RKN genomes and identified a putative novel alternative telomeric element. Then we reconstructed 4 chromosome-level assemblies and resolved their genome structures as AAB for triploid and AABB for tetraploid. The phylogeny of subgenomes revealed polyploid RKN origin patterns as hybridization between haploid and unreduced gametes. We also observed extensive chromosomal fusions and homologous gene expression decrease after polyploidization, which might offset the disadvantages of clonal reproduction and increase fitness in polyploid RKNs. Our results reveal a rare pathway of polyploidization in parthenogenic polyploid animals and provide a large number of high-precision genetic resources that could be used for RKN prevention and control.

The Spatial Distribution and Genetic Diversity of the Soybean Cyst Nematode, Heterodera glycines, in China: It Is Time to Take Measures to Control Soybean Cyst Nematode.

The continuous evolution and spread of virulent forms of the soybean cyst nematode (SCN) driven by the environment and anthropogenic intervention is a serious threat to the soybean production worldwide, including China. Especially in China, the implemented measures to control SCN are insufficient for sustainable agricultural development yet. We summarized our knowledge about the spread and spatial distribution of SCN in China and the virulence diversity in the main soybean growing areas. To reveal the genetic relatedness and diversity of SCN populations, we re-sequenced 53 SCN genomes from the Huang-Huai Valleys, one of the two main soybean growing areas in China. We identified spreading patterns linked to the local agroecosystems and topographies. Moreover, we disclosed the first evidence for the selection of complex virulence in the field even under low selection pressure in an example from North Shanxi. SCN is present in all soybean growing areas in China but SCN susceptible cultivars are still largely grown indicating that SCN-related damage and financial loss have not received the attention they deserve yet. To prevent increasing yield losses and to improve the acceptance of resistant cultivars by the growers, we emphasized that it is time to accelerate SCN resistance breeding, planting resistant cultivars to a larger extent, and to support farmers to implement a wider crop rotation for sustainable development of the soybean production in China.

BtToxin_Digger: a comprehensive and high-throughput pipeline for mining toxin protein genes from Bacillus thuringiensis.

SUMMARY: Bacillus thuringiensis (Bt) has been used as the most successful microbial pesticide for decades. Its toxin genes are used for the development of genetically modified crops against pests. We previously developed a web-based insecticidal gene mining tool BtToxin_scanner. It has been frequently used by many researchers worldwide. However, it can only handle the genome one by one online. To facilitate efficiently mining toxin genes from large-scale sequence data, we re-designed this tool with a new workflow and the novel bacterial pesticidal protein database. Here, we present BtToxin_Digger, a comprehensive and high-throughput Bt toxin mining tool. It can be used to predict Bt toxin genes from thousands of raw genome and metagenome data, and provides accurate results for downstream analysis and experiment testing. Moreover, it can also be used to mine other targeting genes from large-scale genome and metagenome data with the replacement of the database. AVAILABILITY AND IMPLEMENTATION: The BtToxin_Digger codes and web services are freely available at https://github.com/BMBGenomics/BtToxin_Digger and https://bcam.hzau.edu.cn/BtToxin_Digger, respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.