Leaf mold of tomato is caused by the biotrophic fungus Cladosporium fulvum which complies with the gene-for-gene system. The disease was first reported in Japan in the 1920s and has since been frequently observed. Initially only race 0 isolates were reported, but since the consecutive introduction of resistance genes Cf-2, Cf-4, Cf-5 and Cf-9 new races have evolved. Here we first determined the virulence spectrum of 133 C. fulvum isolates collected from 22 prefectures in Japan, and subsequently sequenced the avirulence (Avr) genes Avr2, Avr4, Avr4E, Avr5 and Avr9 to determine the molecular basis of overcoming Cf genes. Twelve races of C. fulvum with a different virulence spectrum were identified, of which races 9, 2.9, 4.9, 4.5.9 and 4.9.11 occur only in Japan. The Avr genes in many of these races contain unique mutations not observed in races identified elsewhere in the world including (i) frameshift mutations and (ii) transposon insertions in Avr2, (iii) point mutations in Avr4 and Avr4E, and (iv) deletions of Avr4E, Avr5 and Avr9. New races have developed by selection pressure imposed by consecutive introductions of Cf-2, Cf-4, Cf-5 and Cf-9 genes in commercially grown tomato cultivars. Our study shows that molecular variations to adapt to different Cf genes in an isolated C. fulvum population in Japan are novel but overall follow similar patterns as those observed in populations from other parts of the world. Implications for breeding of more durable C. fulvum resistant varieties are discussed.

We sequenced and compared the genomes of the Dothideomycete fungal plant pathogens Cladosporium fulvum (Cfu) (syn. Passalora fulva) and Dothistroma septosporum (Dse) that are closely related phylogenetically, but have different lifestyles and hosts. Although both fungi grow extracellularly in close contact with host mesophyll cells, Cfu is a biotroph infecting tomato, while Dse is a hemibiotroph infecting pine. The genomes of these fungi have a similar set of genes (70percent of gene content in both genomes are homologs), but differ significantly in size (Cfu >61.1-Mb; Dse 31.2-Mb), which is mainly due to the difference in repeat content (47.2percent in Cfu versus 3.2percent in Dse). Recent adaptation to different lifestyles and hosts is suggested by diverged sets of genes. Cfu contains an tomatinase gene that we predict might be required for detoxification of tomatine, while this gene is absent in Dse. Many genes encoding secreted proteins are unique to each species and the repeat-rich areas in Cfu are enriched for these species-specific genes. In contrast, conserved genes suggest common host ancestry. Homologs of Cfu effector genes, including Ecp2 and Avr4, are present in Dse and induce a Cf-Ecp2- and Cf-4-mediated hypersensitive response, respectively. Strikingly, genes involved in production of the toxin dothistromin, a likely virulence factor for Dse, are conserved in Cfu, but their expression differs markedly with essentially no expression by Cfu in planta. Likewise, Cfu has a carbohydrate-degrading enzyme catalog that is more similar to that of necrotrophs or hemibiotrophs and a larger pectinolytic gene arsenal than Dse, but many of these genes are not expressed in planta or are pseudogenized. Overall, comparison of their genomes suggests that these closely related plant pathogens had a common ancestral host but since adapted to different hosts and lifestyles by a combination of differentiated gene content, pseudogenization, and gene regulation.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.