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Wild Isolates of Neurospora crassa Reveal Three Conidiophore Architectural Phenotypes.

Emily K KrachYue WuMichael SkaroLeidong MaoJonathan Arnold
Published in: Microorganisms (2020)
The vegetative life cycle in the model filamentous fungus, Neurospora crassa, relies on the development of conidiophores to produce new spores. Environmental, temporal, and genetic components of conidiophore development have been well characterized; however, little is known about their morphological variation. We explored conidiophore architectural variation in a natural population using a wild population collection of 21 strains from Louisiana, United States of America (USA). Our work reveals three novel architectural phenotypes, Wild Type, Bulky, and Wrap, and shows their maintenance throughout the duration of conidiophore development. Furthermore, we present a novel image-classifier using a convolutional neural network specifically developed to assign conidiophore architectural phenotypes in a high-throughput manner. To estimate an inheritance model for this discrete complex trait, crosses between strains of each phenotype were conducted, and conidiophores of subsequent progeny were characterized using the trained classifier. Our model suggests that conidiophore architecture is controlled by at least two genes and has a heritability of 0.23. Additionally, we quantified the number of conidia produced by each conidiophore type and their dispersion distance, suggesting that conidiophore architectural phenotype may impact N. crassa colonization capacity.
Keyphrases
  • genome wide
  • life cycle
  • convolutional neural network
  • high throughput
  • deep learning
  • escherichia coli
  • wild type
  • genetic diversity
  • machine learning
  • climate change
  • resistance training