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Exophiala chapopotensis sp. nov., an extremotolerant black yeast from an oil-polluted soil in Mexico; phylophenetic approach to species hypothesis in the Herpotrichiellaceae family.

Martín R Ide-PérezAyixon Sánchez-ReyesJorge Luis Folch-MallolMaría Del Rayo Sánchez-Carbente
Published in: PloS one (2024)
Exophiala is a black fungi of the family Herpotrichiellaceae that can be found in a wide range of environments like soil, water and the human body as potential opportunistic pathogen. Some species are known to be extremophiles, thriving in harsh conditions such as deserts, glaciers, and polluted habitats. The identification of novel Exophiala species across diverse environments underlines the remarkable biodiversity within the genus. However, its classification using traditional phenotypic and phylogenetic analyses has posed a challenges. Here we describe a novel taxon, Exophiala chapopotensis sp. nov., strain LBMH1013, isolated from oil-polluted soil in Mexico, delimited according to combined morphological, molecular, evolutionary and statistics criteria. This species possesses the characteristic dark mycelia growing on PDA and tends to be darker in the presence of hydrocarbons. Its growth is dual with both yeast-like and hyphal forms. LBMH1013 differs from closely related species such as E. nidicola due to its larger aseptate conidia and could be distinguished from E. dermatitidis and E. heteromorpha by its inability to thrive above 37°C or 10% of NaCl. A comprehensive genomic analyses using up-to-date overall genome relatedness indices, several multigene phylogenies and molecular evolutionary analyzes using Bayesian speciation models, further validate its species-specific transition from all current Exophiala/Capronia species. Additionally, we applied the phylophenetic conceptual framework to delineate the species-specific hypothesis in order to incorporate this proposal within an integrative taxonomic framework. We believe that this approach to delimit fungal species will also be useful to our peers.
Keyphrases
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  • deep learning
  • dna methylation
  • climate change
  • single molecule
  • copy number
  • induced pluripotent stem cells
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