NLR-Annotator: A Tool for De Novo Annotation of Intracellular Immune Receptor Repertoire

Chromosome-scale genome assembly-assisted identification of Mi-9 gene in Solanum arcanum accession LA2157, conferring heat-stable resistance to Meloidogyne incognita

Root-knot nematodes (RKNs) are infamous plant pathogens in tomato production, causing considerable losses in agriculture worldwide. Mi-1 is the only commercially available RKN-resistance gene; however, the resistance is inactivated when the soil temperature is over 28 °C. Mi-9 in wild tomato (Solanum arcanum LA2157) has stable resistance to RKNs under high temperature but has not been cloned and applied. In this study, a chromosome-scale genome assembly of S. arcanum LA2157 was constructed through Nanopore and Hi-C sequencing. Based on molecular markers of Mi-9 and comparative genomic analysis, the localization region and candidate Mi-9 genes cluster consisting of seven nucleotide-binding sites and leucine-rich repeat (NBS-LRR) genes were located. Transcriptional expression profiles confirmed that five of the seven candidate genes were expressed in root tissue. Moreover, virus-induced gene silencing of the Sarc_034200 gene resulted in increased susceptibility of S. arcanum LA2157 to Meloidogyne incognita, and genetic transformation of the Sarc_034200 gene in susceptible Solanum pimpinellifolium conferred significant resistance to M. incognita at 25 °C and 30 °C and showed hypersensitive responses at nematode infection sites. This suggested that Sarc_034200 is the Mi-9 gene. In summary, we cloned, confirmed and applied the heat-stable RKN-resistance gene Mi-9, which is of great significance to tomato breeding for nematode resistance.

Genome sequences of five Sitopsis species of Aegilops and the origin of polyploid wheat B subgenome

Common wheat (Triticum aestivum, BBAADD) is a major staple food crop worldwide. The diploid progenitors of the A and D subgenomes have been unequivocally identified; that of B, however, remains ambiguous and controversial but is suspected to be related to species of Aegilops, section Sitopsis. Here, we report the assembly of chromosome-level genome sequences of all five Sitopsis species, namely Aegilops bicornis, Ae. longissima, Ae. searsii, Ae. sharonensis, and Ae. speltoides, as well as the partial assembly of the Amblyopyrum muticum (synonym Aegilops mutica) genome for phylogenetic analysis. Our results reveal that the donor of the common wheat B subgenome is a distinct, and most probably extinct, diploid species that diverged from an ancestral progenitor of the B lineage to which the still extant Ae. speltoides and Am. muticum belong. In addition, we identified interspecific genetic introgressions throughout the evolution of the Triticum/Aegilops species complex. The five Sitopsis species have various assembled genome sizes (4.11-5.89 Gb) with high proportions of repetitive sequences (85.99%-89.81%); nonetheless, they retain high collinearity with other genomes or subgenomes of species in the Triticum/Aegilops complex. Differences in genome size were primarily due to independent post-speciation amplification of transposons. We also identified a set of Sitopsis genes pertinent to important agronomic traits that can be harnessed for wheat breeding. These newly assembled genome resources provide a new roadmap for evolutionary and genetic studies of the Triticum/Aegilops complex, as well as for wheat improvement.

Bioinformatic tools support decision-making in plant disease management

Food loss due to pathogens is a major concern in agriculture, requiring the need for advanced disease detection and prevention measures to minimize pathogen damage to plants. Novel bioinformatic tools have opened doors for the low-cost rapid identification of pathogens and prevention of disease. The number of these tools is growing fast and a comprehensive and comparative summary of these resources is currently lacking. Here, we review all current bioinformatic tools used to identify the mechanisms of pathogen pathogenicity, plant resistance protein identification, and the detection and treatment of plant disease. We compare functionality, data volume, data sources, performance, and applicability of all tools to provide a comprehensive toolbox for researchers in plant disease management.