Login / Signup

Comparative Transcriptome Analysis Identified Candidate Genes Related to Bailinggu Mushroom Formation and Genetic Markers for Genetic Analyses and Breeding.

Yongping FuYueting DaiChentao YangPeng WeiBing SongYang YangLei SunZhi-Wu ZhangYu Li
Published in: Scientific reports (2017)
Bailinggu (Pleurotus tuoliensis) is a major, commercially cultivated mushroom and widely used for nutritional, medicinal, and industrial applications. Yet, the mushroom's genetic architecture and the molecular mechanisms underlying its formation are largely unknown. Here we performed comparative transcriptomic analysis during Bailinggu's mycelia, primordia, and fruiting body stages to identify genes regulating fruiting body development and develop EST-SSR markers assessing the genetic value of breeding materials. The stage-specific and differentially expressed unigenes (DEGs) involved in morphogenesis, primary carbohydrate metabolism, cold stimulation and blue-light response were identified using GO and KEGG databases. These unigenes might help Bailinggu adapt to genetic and environmental factors that influence fructification. The most pronounced change in gene expression occurred during the vegetative-to-reproductive transition, suggesting that is most active and key for Bailinggu development. We then developed 26 polymorphic and informative EST-SSR markers to assess the genetic diversity in 82 strains of Bailinggu breeding materials. These EST-SSRs exhibited high transferability in closely related species P. eryngii var. ferulae and var. eryngii. Genetic population structure analysis indicated that China's Bailinggu has low introgression with these two varieties and likely evolved independently. These findings provide new genes, SSR markers, and germplasm to enhance the breeding of commercially cultivated Bailinggu.
Keyphrases
  • genome wide
  • genetic diversity
  • gene expression
  • copy number
  • dna methylation
  • wastewater treatment
  • transcription factor
  • artificial intelligence
  • genome wide identification
  • deep learning