Periconia is a polyphyletic and asexual morphic genus within the family Periconiaceae (Pleosporales). The genus is characterized by a pale to dark brown stipe with an apical conidial head and ellipsoidal to oblong conidia. Species of Periconia are widely distributed throughout the world in various hosts, while most species are isolated from graminaceous plants. During our investigations of microfungal in Sichuan Province, China, 26 Periconia isolates were collected from a wide variety of graminaceous plants. These isolates corresponded to 11 species based on the examination of morphology and multi-locus phylogenetic analysis (SSU, ITS, LSU, TEF1, RPB2). This includes six new species (P. chengduensis, P. cynodontis, P. festucae, P. imperatae, P. penniseti, and P. spodiopogonis) and five new records (P. byssoides, P. chimonanthi, P. cookie, P. pseudobyssoides, and P. verrucosa). A comprehensive description and illustrations of the new species are provided and discussed with comparable taxa. These discoveries expand our knowledge of the species diversity of Periconia taxa in graminaceous plants in China.
Torula is an asexual and hyphomycetous genus in the family Torulaceae. Torula species are generally saprophytic. They have a worldwide distribution and abound in humid or freshwater habitats. In order to better understand this genus, we carried out several field collections from Sichuan, China. As a result, we obtained nine Torula isolates from dead woody substrates in terrestrial and freshwater habitats. Based on a biphasic approach of morphological examination and multi-locus phylogenetic analyses (ITS, SSU, LSU, TEF, RPB2), these collections were identified as belonging to seven Torula species. Four of them were new species (Torula chinensis, T. longiconidiophora, T. sichuanensis and T. submersa), and the other three belonged to existing species, though one was found for the first time in China (T. masonii). Morphological and updated phylogenetic delamination of the new discoveries is also discussed. This study provides further insights into our understanding of wood-based Torula species in China.
Economically and agriculturally important fungal species have various lifestyles, and they may shift from mutualistic or saprobic to pathogenic depending on the habitat, host tolerance, and resource availability. Traditionally, the determination of fungal lifestyles has been based on observation at a particular host or habitat. Therefore, potential fungal pathogens have been neglected until they cause devastating impacts on human health, food security, and ecosystem stability. This study focused on the class Sordariomycetes to explore the genomic traits that could be used to determine the lifestyles of fungi and the possibility of predicting fungal lifestyles using machine learning algorithms. A total of 638 representative genomes covering five subclasses, 17 orders and 50 families were selected and annotated. Through an extensive literature survey, the lifestyles of 555 genomes were determined, including plant pathogens, saprotrophs, entomopathogens, mycoparasites, endophytes, human pathogens and nematophagous fungi. We evaluated the influence of sequencing technologies and concluded that second sequencing technologies have no influence on genome completeness but tend to generate a reduced size of transposable elements. We constructed three numerical matrices: a basic genomic feature matrix including 25 features; a functional protein matrix including 24 features; and a combined matrix. The most comprehensively comparative analysis to date across multiple lifestyles was conducted based on these matrices. Results indicate that basic genomic features reflect more on phylogeny rather than lifestyle, but the abundance of functional proteins displays relatively high discrimination not only in differentiating taxonomic groups at the higher levels but also in differentiating lifestyles. Genome size, GC content and gene number showed powerful discrimination for differentiating higher ranks, especially at the subclass level. Plant pathogens have the largest secretome; whereas entomopathogens have the smallest secretome; and the abundance of secretomes is a useful indicator to clearly differentiate plant pathogens from entomopathogens, mycoparasites, saprotrophs and entomopathogens, and as well as differentiate entophytes from entomopathogens. Effectors have long been considered as disease determinants, and we did observe that plant pathogens have more effectors than saprotrophs and entomopathogens. However, we also observed a similar abundance of effectors in endophytes, suggesting that effectors maybe not a reliable indicator for pathogenic fungi. Single functional protein could not differentiate all lifestyles, but combinations of multiple numerical features of functional proteins result in accurate differentiation for most lifestyles. Furthermore, models of six machine learning algorithms were trained, optimized and evaluated, and the best-performance model was used to predict the lifestyle of 83 unlabeled genomes. Although the accuracy of the best machine learning model was limited by the inadequate genome number of several lifestyles and the inaccurate lifestyle assignments for some genomes, the predictive model still obtained a high degree of accuracy in differentiating plant pathogens. The predictive model can be further optimized with more sequenced genomes in the future, and provide a more reliable prediction. This can be used as an early warning system to identify potentially devastating fungi and take appropriate measures to prevent their spread.
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