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Home-Journal Online-2024 No.11

Microbial community structure and diversity of kiwifruit flowers infected by different degrees of bacterial canker

Online:2024/12/11 15:33:40 Browsing times:
Author: LIU Haohao, ZHONG Caihong, LIU Wei, LI Li, HUANG Lili
Keywords: Kiwifruit bacterial canker; Microbiome; Disease severity; Community composition
DOI: 10.13925/j.cnki.gsxb.20240457
Received date: 2024-09-09
Accepted date: 2024-10-16
Online date:
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Abstract: ObjectiveKiwifruit canker is a severe bacterial disease that poses a significant threat to the kiwifruit industry causing by Pseudomonas syringae pv. actinidiae (Psa). This disease affects various parts of kiwifruit, including the vines, leaves, flowers and roots. Among these, the impact on the flowers is particularly critical, as it directly leads to yield loss in the affected year. Psa can damage the buds, petals and peels, causing the buds to fail to bloom, turn brown, and even fall off. Flower organs provide a conducive environment for microbial colonization. Consequently, microorganisms residing inside or on the surface of flower play a protective role against pathogen infection. Numerous studies have shown that environmental factors influence microbial community composition of plants. Among these factors, external pathogen invasion acts as the major selection pressure, significantly affecting floral microbial community structure. This study aimed to investigate changes in microbial diversity and community structure of floral bacteria and fungi under varying canker disease severity conditions, by using high-throughput sequencing. The results laid a theoretical foundation for understanding theinteractive mechanisms between kiwifruit and Psa, as well as for developing biological control strategies for this disease.MethodsIn April and May, 2022, during the flowering period of the Donghong kiwifruit, which was also a highly-infected period for kiwifruit canker disease, samples were collected from a kiwifruit experimental orchard in Xijiadian Town, Danjiangkou City, Hubei province. Three disease severity levels were set as below: healthy, moderate and severe, respectively. High-throughput sequencing technology was employed to analyze the diversity and structure of bacterial and fungal microflora in these samples. Specifically, 16S rRNA and ITS1 gene sequencings were used to study bacterial and fungal communities, respectively.ResultsA total of 584 580 high-quality 16S rRNA sequences and 520 169 high-quality ITS1 sequences were obtained after quality control. Clustering of these sequences revealed 3410 bacterial operational taxonomic units (OTUs) and 12 986 fungal OTUs. Among the samples with three different disease severities, there were 37 common bacterial OTUs and 383 common fungal OTUs. The proportion of unique bacterial OTUs was 37.5% in healthy samples, 27.0% in moderately-diseased samples, and 18.6% in severely-diseased samples. For fungi, the unique OTUs accounted for 18.3%, 25.6% and 19.4%, respectively. Species classification analysis identified as 27 phyla, 55 classes, 134 orders and 210 families in the bacterial community, as well as 16 phyla, 52 classes, 118 orders and 252 families in the fungal community. As disease severity increased, the number of bacterial taxa at each classification level decreased. Conversely, the number of fungal OTUs initially increased and then decreased, which was consistent with the trend of OTUs quantity changes in samples with different degrees of disease incidence. Both the diversity index and the number of OTUs decreased as disease severity increased. Significant differences in bacterial diversity indices were observed among the different disease severities (p<0.05). The diversity index and the number of OTUs initially increased with moderate disease severity and then decreased with severe disease severity. Notably, the fungal diversity index in severely-diseased samples was slightly higher than in healthy ones. Fungal diversity differed significantly between moderately- diseased samples and other levels (healthy and severe) (p< 0.05). In the bacterial community, samples with the same disease severity clustered closely, indicating good repeatability. Samples with different disease severities were significantly separated, suggesting distinct differences existed in bacterial microbial communities (p<0.05). In the fungal community, healthy samples were significantly separated from infected samples (p<0.05). Among diseased samples, those with moderate and severe disease severity showed some overlap but also possessed distinct differences, indicating similarities and differences in their microbial communities. Among the samples with different degrees of severity, the dominant bacteria genera were Pseudomonas and Cyanobacteria; the dominant fungal genera were Mortierella, Alternaria, Cladosporium and Fusarium. The relative abundances of Pseudomonas, Cladosporium, Alternaria and Filobasidium increased with disease severity, while the relative abundances of Cyanobacteria and Mortierella decreased. Significant differences in the relative abundances of Pseudomonas, Alternaria and Mortierella were observed among different samples (p< 0.05).ConclusionThe findings of this study revealed significant changes in the microbial community structure of kiwifruit flowers in response to Psa invasion. The diversity and abundance of specific bacterial and fungal taxa were markedly influenced by the severity degrees of disease. The reduction in bacterial diversity and the initial increase followed by a decrease in fungal diversity suggested that disease degrees of severity exerted selective pressure on microbial communities. This pressure was beneficial for the proliferation of certain pathogenic microorganisms but inhibitive to others. The increased relative abundances of Pseudomonas, Cladosporium, Alternaria and Filobasidium in severely-diseased samples highlighted their potential roles in the disease progression. The significant differences in microbialcommunities between healthy and diseased samples underscore the potential for utilizing microbial indicators as diagnostic tools for early disease detection.