Frontiers in Microbiology (Mar 2025)

The relationship between pomegranate root collar rot and the diversity of fungal communities in its rhizosphere

  • Ziqiang Wu,
  • Jianxin Chen,
  • Jie Chen,
  • Jie Chen,
  • Yalin Yang,
  • Aiting Zhou,
  • Jianrong Wu,
  • Jianrong Wu

DOI
https://doi.org/10.3389/fmicb.2025.1573724
Journal volume & issue
Vol. 16

Abstract

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IntroductionThe pomegranate (Punica granatum) is a significant economic tree species. In recent years, the root collar rot has severely affected pomegranates in the dry-hot valley regions of Yunnan Province, China. The rhizosphere microbiome plays a crucial role in plant growth, development, and disease resistance.MethodsThis study utilized Illumina MiSeq sequencing to analyze the fungal communities in the roots and rhizosphere soils of healthy and diseased pomegranates, focusing on the impact of root collar rot disease on the diversity and structural composition of these communities.ResultsThe results indicated that in the unique fungal communities of healthy plant roots, the relative abundance of ectomycorrhizal and arbuscular mycorrhizal functional (AMF) groups was 53.77%, including genera such as Glomus and Septoglomus. After infection with root collar rot disease, the rhizosphere fungal communities became more monotonous, with increased differentiation within sample groups. Fungal groups associated with plant diseases and soil nutrient structures underwent significant changes. The disease altered the composition and functional group proportions of rhizosphere fungal communities, a process linked to soil nutrient structures. And the balance between plant-pathogen-related and saprotrophic functional groups in the rhizosphere was disrupted. Through Koch’s postulates verification, the pathogen was identified as Lauriomyces bellulus.DiscussionThis is the first report of collar rot of pomegranate caused by L. bellulus in China. Studying the differences in rhizosphere fungal community structures and quantities in response to new diseases aids in the rapid prediction of pathogens, effectively saving diagnostic time, and provides theoretical support for disease prediction, diagnosis, and control.

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