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. 2023 Sep 26;16(12):2264–2277. doi: 10.1111/1751-7915.14344

Pathogenic fungi shape the fungal community, network complexity, and pathogenesis in kiwifruit

Ke Huang 1, Xiangcheng Sun 2, Xiaojiao Li 3, Xiaoya Huang 4, Zhiqiang Sun 4, Wenhua Li 4, Junkui Wang 4, Dawei Tian 4, Chenglin Lin 4, Xuehong Wu 5, Cailing Miao 1,6, Yujing Li 1,6, Panpan Xu 7, Tianyu Fan 1,6, Shuxin Zhu 1,6, Na Li 1, Li Zeng 1, Jia Liu 1, Yuan Sui 1,✉
PMCID: PMC10686113  PMID: 37750437

Abstract

Kiwifruit decay caused by endophytic fungi is affected by exogenous pathogens that trigger changes in fungal community composition and interact with the endophytic fungal community. Four fungal pathogens of kiwifruit were identified. These were Aspergillus japonicus, Aspergillus flavus, Botryosphaeria dothidea, and Penicillium oxalicum. Except for P. oxalicum, the remaining three species represent newly described pathogens of kiwifruit. All four fungal species caused disease and decay in mature kiwifruit. Results of the fungal community analysis indicated that three pathogens that A. japonicus, A. flavus and P. oxalicum were the most dominant, however, other fungal species that did not cause disease symptoms were also present. Positive interactions between fungal species were found in asymptomatic, symptomatic, and infected kiwifruit. The ability of all four pathogens to infect kiwifruit was confirmed in an inoculation experiment. The presence of any one of the four identified pathogens accelerated decay development and limited the postharvest longevity of harvested kiwifruit. Results of the study identified and confirmed the ability of four fungal species to infect and cause decay in harvested kiwifruit. Changes in the structure and composition of the kiwifruit microbiome during the decay process were also characterized. This provides a foundation for the further study of the microbiome of kiwifruit and their involvement in postharvest diseases.

Four confirmed pathogenic fungi were found to be causal agents of kiwifruit decay. Pathogenic fungi induced changes of in the composition of endophytic fungal community. Four species of pathogenic fungi accelerated the decay process in kiwifruit. Pathogenic fungal pathogens affect the postharvest longevity of kiwifruit.

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INTRODUCTION

Kiwifruit is widely cultivated in China where it is known as the king of fruit. Unfortunately, kiwifruit quality significantly declines due to postharvest rots, which represents a significant problem and causes major economic losses. Previous studies have reported that fungal species in the family Botryosphaeriaceae, including Botryosphaeria dothidea, Lasiodiplodia theobromae, and Neofusicoccum parvum are causal agents of kiwifruit rot (Zhou et al., 2015). Several other fungi, such as Botrytis cinerea, B. prunorum (Riquelme et al., 2021), Penicillium expansum (Prodromou et al., 2018), and Alternaria alternata (Li et al., 2017) are also postharvest pathogens of kiwifruit. Classical symptoms of kiwifruit rot begin with brown spots that enlarge and merge together, resulting in a watery soft rot.

The above‐mentioned fungal species also cause the decay of other fruits. B. dothidea is the causal agent of disease in several fruit species, including white rot of apples (Wang et al., 2022), fruit black spot of pecan (Carya illinoensis) (Wang et al., 2021), fruit rot of pomegranate (Gu et al., 2020) and bot rot of Ficus carica (Wang et al., 2020a, 2020b). L. theobromae causes decay of passion fruit (Passiflora edulis) (Zhang et al., 2020), rot of eggplant fruit (Vieira et al., 2018), and fruit rot of Guava (Psidium guajava) (Zee et al., 2021). N. parvum causes rachis rot in mango (Mangifera indica) (Serrato‐Diaz et al., 2013) and stem end rot (SER) in avocado (Twizeyimana et al., 2013). The grey mould pathogen, B. cinerea, causes postharvest rot in numerous fruit species, such as strawberry (Ismagulova et al., 2020) and pear (Zhang et al., 2014). The blue mould pathogen, P. expansum, is a major postharvest pathogen of pome fruit(Luciano‐Rosario et al., 2020) and plum (Prunus domestica) (Brito et al., 2020). Lastly, A. alternata is the causal agent of fruit rot in Tetradium ruticarpum (Xiang et al., 2020) and fig (Ficus carica) (Alam et al., 2021).

Although several postharvest pathogens of fruit species have been identified, new pathogens and interactions between pathogens that influence pathogenesis are being discovered, especially as fruit production is extended into new areas globally and ongoing climate change alters environmental conditions. It is our hypothesis that many of the mentioned fungal pathogens become established in kiwifruit tissue at an early stage of fruit development as latent infections, and then proliferate and cause decay when the fruit is harvested and begins to senesce or is infected by another pathogen Therefore, identifying new pathogens and examining the interaction between pathogens and host‐microbial communities can contribute to a more comprehensive knowledge of kiwifruit diseases.

Amplicon sequencing of microbial communities utilizing universal primer sets, such as various ITS primer sets, has been used in fruit and vegetables to characterize changes in microbial communities associated with the disease process (McNees et al., 2020). In kiwifruit, high‐throughput, amplicon sequencing has been used to investigate postharvest disease control. Amplicon sequencing also revealed that the application of the biocontrol yeast, Wickerhamomyces anomalus on kiwifruit altered microbial community composition and decreased the abundance of fungal pathogens, resulting in a decreased level of postharvest disease (Huang et al., 2022). In the present study, the epidermal (peel) tissue from symptomatic with brown spot and asymptomatic kiwifruit were used to explore, isolate and identify potential new pathogens. Inoculation experiments using the purified strains obtained from the epidermal tissues were used to confirm the pathogenicity of the isolated fungal species. ITS amplicon sequencing was used to analyse fungal community structure and composition and to identify the fungi correlated with rot symptoms in kiwifruit. The effect of biotic stress on core microbial community structure of kiwifruit tissues was also examined (Sui et al., 2021).

MATERIALS AND METHODS

Fungi isolation and identification

Kiwifruit (Actinidia deliciosa) ‘Hongyang’ was used in this study. Kiwifruit was harvested at 150 days after flowering from an orchard in Yongchuan, Chongqing city, China. Kiwifruit was brought back to the laboratory and their fruit surfaces were sterilized with 2% (v/v) sodium hypochlorite for 2 min, and then rinsed with sterile water three times, and air‐dried. Fungi were isolated from areas of kiwifruit with obvious symptoms of brown soft rot as previously reported (Zhao et al., 2023). Brown spot areas about 3 × 3 mm were removed and placed in petri dishes containing Potato Dextrose Agar (PDA) medium. Four isolates were successively obtained in pure culture. DNA was extracted from each of the purified strains, and internal transcribed spacer (ITS), as well as large subunit nuclear ribosomal RNA (nLSU) sequences were obtained and used to predict the species of each of the purified strains. Briefly, genomic DNA was extracted from each strain using a genomic DNA isolation kit (E.Z.N.A.® Soil DNA Kit; Omega Bio‐Tek), and the extracted DNA was amplifed using the primer sets ITS1: (5′‐TCCGTAGGTGAACCTGCGG‐3′), ITS4: (5′‐TCCTCCGCTTATTGATATGC‐3′) and LOR3: (5′‐GTACCCGCTGAACTTAAGC‐3′), LROR: (5′‐TACTACCACCAAGATCT‐3′). Different PCR parameters were used for the ITS and nLSU amplification. The reaction mixture contained 10 μL Taq polymerase, 1 μL upstream primer, 1 μL downstream primer, 7 μL ddH2O, and 1 μL DNA. The PCR reaction was performed after thorough mixing. PCR protocol included pre‐denaturation (94°C, 5 min), denaturation (94°C 30 s), annealing (ITS1, ITS4 annealing at 47°C, LOR3, LROR annealing at 43°C), extension (72°C, 1 min) for 30 cycles.

The ‘G‐INS‐i’ strategy was used to align the sequences in the online mode of MAFFT 7 (http://mafft.cbrc.jp/alignment/server/), and Clustalx2 was used to manually adjust the alignment (Larkin et al., 2007). The online version of Gblocks (Talavera & Castresana, 2007) was used to eliminate ambiguous positions using the least stringent settings (allowing smaller final blocks, gap positions, and less stringent flanking positions within the final blocks). The sequences obtained from the ITS+nLSU datasets were subjected to maximum parsimony analysis. The procedure for building the trees was carried out using PAUP*v4.0a169 (Swofford, 2002). All characters were given equal weight, and gaps were considered as missing data. The heuristic search option was used to infer trees using 1000 random sequence additions and tree bisection and reconnection (TBR) branch swapping. The maximum number of trees was set at 5000, the shortest branches were torn off, and only sparing trees were saved. A 1000‐replicate bootstrap (BP) analysis was used to assess clade robustness (Felsenstein, 1985). Descriptive tree statistics, including tree length (TL), consistency index (CI), retention index (RI), rescaled consistency index (RC), and homoplasy index (HI) were calculated for each maximum parsimonious tree (MPT). The sequences were also analysed using the maximum likelihood (ML) method with RAxML8.1.11 (Stamatakis, 2014), using 1000 bootstrap replicates to determine branch support (BS). The best‐fit evolution model for each dataset was determined using MrModeltest v2.3 (Nylander, 2004) for Bayesian inference (BI). A general time reversible (GTR + I + G) model of DNA substitution and gamma distribution rate variation across sites were used to calculate BI using Mr. Bayes 3.2.7a (Ronquist et al., 2012), Branches were considered as significantly supported if they received maximum likelihood bootstrap value (BS) > 75%, maximum parsimony bootstrap value (MP) > 75%, or Bayesian posterior probability (BPP) > 0.95 (Dong et al., 2021). Phylogenetic tree graphics and annotation were constructed using ITOL6.6 (https://itol.embl.de/) (Letunic & Bork, 2021).

Pathogenicity assays

Pathogenicity tests were conducted as previously described (Sui et al., 2021). Briefly, the four purified isolates were cultured on PDA medium at 25°C for 5 days and spores were eluted from the resulting fungal colonies using 2 mL of sterile distilled water. The spores were collected and a spore count was determined under a microscope with the aid of a haemocytometer. Spore suspensions were adjusted to 107 spores/mL. Healthy kiwifruit (Actinidia chinensis) harvested at 80 days after flowering were defined as developing kiwifruit and healthy kiwifruit harvested at 150 days after flowering were defined as mature kiwifruit and used to investigate the pathogenicity of the four fungal isolates on mature kiwifruit. Fruit was surface sterilized with 2% (v/v) sodium hypochlorite for 2 min, rinsed 3× with sterile distilled water, and then one small wound (10 mm deep) was administered on the equator of each developing fruit. Mature fruit were treated with three wounds. Each wound was then inoculated with 10 uL of the prepared spore suspension. Every treatment included 15 fruit, and the assay was repeated three times. The inoculated fruit were placed at 25°C and observed daily for evidence of infection. Percentage disease incidence was calculated daily using the following formula: IC = (n/N) × 100; where IC = incidence; n = number of lesions on kiwifruit; and N = the total number of wounds. Disease severity was determined according to the following equation: disease severity index (DSI) [∑ (number of decay fruit at each level × representative value of each level) / (total number of fruit surveyed ×representative value of the highest level)] × 100. Fruit disease lesion is classified into five grades, where 0 indicates no disease symptoms of fruit, 1 indicates a lesion size covering 0 to 25% of the fruit, 2 indicates a lesion size covering 25% to 50%, 3 indicates a lesion size covering 50% to 75%, and 4 indicates a lesion size covering 75% to 100%.

ITS amplicon sequencing

Tissue samples were collected from treated asymptomatic and symptomatic fruit to characterize the role of exogenous pathogens in the infection process of developing fruit. Epidermal samples were collected from rot lesions caused by the four fungal isolates after 10 days and from asymptomatic kiwifruit (CK) that served as a control. Samples from lesions on naturally‐infected kiwifruit (ND) in the control group (CK) were also collected. Four biological replicates were collected from asymptomatic and symptomatic fruit each. Approximately 1.0 g of tissue from each sample was put into 1.5 mL sterile Eppendorf tubes and sent to a commercial firm (MajorBio) for DNA extraction, library preparation, and sequencing. Tissue samples were ground in a mortar and pestle in liquid nitrogen and DNA was extracted using a genomic DNA isolation kit (E.Z.N.A.® Soil DNA Kit; Omega Bio‐Tek). DNA quality and quantity were assessed with a spectrophotometer (NanoDrop2000; Thermo Fisher Scientific) and by DNA agarose gel electrophoresis.

A portion of the ITS gene was amplified for fungal community analysis using the ITS universal primers, ITS1F CTTGGTCATTTAGAGGAAGTAA and ITS2R GCTGCGTTCTTCATCGATGC that amplify a 350 bp portion of the ITS rRNA gene.

Bioinformatic analysis

The ITS sequencing dataset was processed using Quantitative Insights into Microbial Ecology (QIIME2) software (Bolyen et al., 2019). Primers and low‐quality sequences were first removed using the cutadapt plug‐in to obtain clean reads, double‐ended sequences were merged using VSEARCH v11 (Rognes et al., 2016), and further amplicon sequence variants (ASVs) were degraded using the deblur pipeline. The sequences were aligned with the fungal ITS database dataset from Unite v9.0 (http://unite.ut.ee/index.php) to complete the taxonomic assignment. The beta diversity distance matrix was calculated using Qiime2, to calculate the relative abundance of differences the R v4.0.2 stats package was used, and the graphs were drawn using R studio software.

Correlation analysis

Co‐occurrence network analysis can be used to show the distribution between samples and species. Species abundance information between different samples can be used to determine the co‐occurrence relationship of species, which can highlight the similarities and differences between samples in large and complex datasets. Correlation analysis generates clusters based on species shared between samples. The more species shared, the closer the relationship between samples. There are two types of nodes in a network graph, a species‐node and a sample‐node, and if a species is present in a sample, then there is a linkage (edge) between that species and the sample. The established biological correlation network was analysed using graph theory with NetworkX (Hagberg et al., 2008), a complex network analysis toolkit, by calculating the node degree distribution of the network, the diameter of the network, the average shortest path of the network, and properties such as node connectivity (Degree), closeness Centrality, and Betweenness Centrality. Betweenness Centrality was calculated to obtain intra‐group or inter‐group correlation information on species and samples and to efficiently mine the information contained in complex data. Single‐factor network, i.e., a species correlation network, was constructed by calculating the correlation between species. The nodes in the network graph are all species‐node nodes with a connecting line between species. Finally, the established biological correlation network was analysed using NetworkX, a complex network analysis toolkit, by calculating the node degree distribution, the diameter of the network, the average shortest path of the network, and the properties of node connectivity (Degree), Closeness Centrality, and betweenness centrality. The network analysis toolkit is designed to obtain intra‐ and inter‐group correlation information on species and samples by calculating properties such as Degree, Diameter, Average Shortest Path of the network, and Degree, Closeness Centrality, Betweenness Centrality, etc., in order to comprehensively mine information contained in complex datasets. Cystoscope (Shannon et al., 2003) was used to display the results of the correlation and network analyses.

Function prediction

FUNGuild (Fungi Functional Guild) (Nguyen et al., 2016) is a tool for the classification and analysis of fungal communities. It utilizes microecological guilds, a concept in microecology that involves species that can utilize similar environmental resources in similar ways regardless if they are closely related to each other. Based on the available published literature and website data, fungi are first divided into three categories based on their mode of nutrition. In this regard, pathotrophs obtain nutrition through injury to host cells.

RESULTS

Identification of four fungal isolates

Four fungal strains were isolated from kiwifruit with brown soft rot symptoms and subsequently purified. Eighty sequence datasets of fungal specimens representing the four putative taxa were used to identify the isolated strains, including PCR‐amplified sequences obtained using ITS and nLSU primers, and NCBl downloaded sequences. The sequence comparison length used for parsing was 1526 bp, in which 853 bp were constants, 71 variables were not rich in parsing information, and 602 bp contained parsing information. Maximum parsing analysis converged to a parse tree (TL = 1744, C = 0.550, Hl = 0.911, RI = 0.501, RC = 0.405). In the best model of the dataset esticqpr = dirichlet (1,1,1,1), Bayesian and ML analyses yielded topologies similar to the MP analysis, with a mean standard deviation of 0.008612 for split frequencies (Bl). Further results inferred from the phylogeny of ITS + nLSU sequences (Figure 1) indicated that the four isolates were Aspergillus_japonicus (82% BS, 100% BP and 1.00 BPP), Aspergillus_flavus (100% BS, 100% BP and 1.00 BPP), Botryosphaeria_dothidea (100% BS, 86% BP and 1.00 BPP), and Penicillium_oxalum (100% BS, 100% BP and 1.00 BPP).

FIGURE 1.

FIGURE 1

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Maximum parsimony strict consensus tree based on ITS+nLSU sequences described the phylogeny of the four strains of fungi isolated from soft rot lesions in kiwifruit and their close relatives. Branches are labelled with maximum likelihood bootstrap values (BS) > 70%, parsimony bootstrap values (BP) > 50%, and a Bayesian posterior probability (BPP) > 0.95. Adaxial and abaxial morphology of the purified strains, as well as the morphology of mycelia and spores. Each colony was observed at 400× under a light microscope.

A. japonicus appeared brown in culture with opaque colonies on the adaxial surface and creamy white on the abaxial surface and exhibited branching mycelia and round spores. A. flavus appeared green in culture. The adaxial surface of the colonies was opaque and the abaxial surface appeared greyish yellow. It had broom‐like mycelia and round spores. The adaxial surface of B. dothidea colonies was grey‐black in the middle and grey‐white around the edges, while the abaxial surface was dark green. It had branching, septate mycelia, and produced round spores. The adaxial surface of P. oxalicum was grey‐green and opaque, while the abaxial surface was creamy white. It exhibited umbrella‐shaped mycelia and oval spores. The colonies produced reticulate mycelia and round spores (Figure 1).

Pathogenicity

Representative photos of kiwifruit inoculated with a 107 spore/mL spore suspension of the different isolated species of fungi at 10 days post inoculation (dpi) are presented in Figure 2A. Kiwifruit inoculated with A. japonicus initially exhibited brown mycelia and the tissue around the wound became soft. Subsequently, the area of brown mycelia enlarged and the entire kiwifruit rotted. Fruit inoculated with A. flavus initially exhibited white hyphae in the infection site. Subsequently, the mycelia spread and turned green as the mycelia proliferated, resulting in the appearance of green mycelia in and around the infection site. Fruit inoculated with P. oxalicum also initially exhibited white hyphae in the infection site which subsequently spread and turned dark green as the mycelia proliferated, resulting in the appearance of dark green mycelia in and around the lesion site. Fruit inoculated with B. dothidea initially exhibited white hyphae that subsequently turned grey as the mycelia proliferated, resulting in the appearance of grey mycelia in and around the infection site. The disease incidence and index over a 10 days period are presented in Figure 2B,C. Results indicated that the disease index for all four fungal isolates was more than 40 at 10 dpi. Statistical analysis indicated that the index for all four isolates was significantly greater (p < 0.05 or p < 0.01) than the control group beginning at 2 dpi. The highest percentage disease index was observed for A. japonicus (59.17), followed by B. dothidea (56.94), A. flavus (54.99), and P. oxalicum (48.89). In contrast, the percentage disease index in the control group (CK) was 13.72. These results demonstrated that the four isolated fungal species could infect kiwifruit and cause a significantly higher (p < 0.05) level of disease, as indicated by the higher disease index, compared to the disease level that occurs on naturally infected fruit. Notably, however, even the non‐inoculated, surface‐sterilized kiwifruit (CK) exhibited decay. In this regard, changes in the microbial community of postharvest fruit and vegetable have been suggested as the basis of disease occurrence (Bill et al., 2022; Kusstatscher et al., 2019). Therefore, high‐throughput sequencing of ITS amplicons was used to examine changes in the endophytic fungi community of kiwifruit in response to biotic stress.

FIGURE 2.

FIGURE 2

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Representative photographs of lesion development and the disease index of the Four newly identified pathogens (including A. japonicus (AJ), A. flavus (AF), B. dothidea (BD), and P. oxalicum (PO)) of kiwifruit at 10 days post‐inoculation (dpi)，pre‐infection at the top, 10 days of infection at the bottom (A). Disease incidence and index rating for the four pathogens and the control group fruit. Days post inoculation (dpi) is on the X‐axis and the percentage disease incidence (B) and index (C) are on the Y‐axis. The error bars show the Standard Deviation (SD) of the statistical sample. The colours and asterisks in the boxes show the corresponding samples and the differences with the control group fruit. Asterisks *, ** and *** indicate a significant difference at p < 0.05, p < 0.01, and p < 0.001 between the pathogen inoculated and the control group fruit according to a Student's t‐test.

Effect of pathogenic fungi on the composition of the fruit microbiome

Sequences of ITS amplicons obtained from infected kiwifruit tissues that had been inoculated with each of the four fungal isolates, naturally‐infected tissues, and a non‐infected tissue sample were analysed. The analysis indicated that the fungal diversity of communities in healthy samples was lower than in both naturally decayed and inoculated samples (Figure 3A). Fusarium_concentricum (43%) was the dominant fungal species in the healthy samples, while Cladosporium (45%) was the dominant fungal species in naturally decayed samples. Notably, in inoculated samples, A. japonicus, P. oxalicum, and A. flavus were the dominant fungal species in all samples inoculated with either A. japonicus (AJ), P. oxalicum (PO), or A. flavus (AF). In contrast, B. dothidea were not the dominant fungi in samples inoculated with B. dothidea (BD)，instead, F. concentricum was the dominant species. Abundance analysis tissues from healthy (CK) and naturally decayed (ND) samples indicated that the relative abundance of A. japonicus and A. flavus was higher in ND than in CK samples. In contrast, the highest relative abundance was exhibited by B. dothidea and P. oxalum in CK samples. The results indicate that the dominant fungal communities in each group were diverse, with other predominant fungal species varying across groups. B. dothidea and A. Alternata were the primary species in the AJ group, while A. Alternata and D. masirevicii were the major species in the AF group; B. dothidea and A. japonicus were the main species in the BD group, and D. arengae and D. nobilis were the dominant species in the PO group. Furthermore, A. Alternata and D. masirevicii were the predominant species in the CK group, while D. masirevicii and P. spinulosum were the primary species in the ND group. (Figure 3A). Species compositional similarity and overlap were analysed by determining the number of common and unique species in the samples. ND had the highest number of unique species, followed by healthy samples (CK), while no unique species were observed in AJ and BD samples. CK and infected samples had the fewest species in common (Figure 3B). Principal component analysis of Bray‐Curtis distances between samples revealed stronger clustering of fungal communities based on the relative abundance of pathogenic fungi rather than the composition of endophytic fungi (Figure 3C). Collectively, these results indicate that kiwifruit decay is caused by the relative abundance and number of pathogenic fungal species. Moreover, it is suggested that pathogenic fungi play a crucial role in shaping the composition of endogenous fungal communities within kiwifruits.

FIGURE 3.

FIGURE 3

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Composition structure and abundance of endophytic fungi in kiwifruit infected with four pathogens, natural decay (ND), and uninoculated (CK) kiwifruit. The x‐coordinate is the sample type, the y‐coordinate is the proportion of each species in the sample (A). Venn diagram of the number of fungal species in different sample types and sample type comparisons. Numbers in the overlapping part of a Venn diagram represent the number of species common to multiple groups, and the numbers in the non‐overlapping part represent the number of species unique to the corresponding group (B). Principal component analysis of the different sample types. The X‐axis and Y‐axis represent the two selected principal axes, and the percentage represents the explanatory value of the principal axes for the variability in sample composition. The closer the two sample points are, the more similar the species composition in the two samples is (C).

Co‐occurrence and interaction modules

The co‐occurrence of species in the different kiwifruit samples can be illustrated in a co‐occurrence network diagram (Figure 4A). which shows that the ND group had the most co‐occurring species, including the most unique co‐occurring species, and the same co‐occurring species found in the other 6 subgroups. Interestingly, P. oxalicum only co‐occurred in naturally decayed (ND) samples and the only co‐occurring species in AJ samples was A. japonicus. It is evident from the data that pathogenic fungi play a key role in the decay of the different types of samples. Species‐to‐species correlations were illustrated as a network to reflect interactions between species in the samples (Figure 4B). P. oxalicum exhibited a positive correlation with Didymella_rosea and D. nobilis that were negatively correlated with B. dothidea. A. flavus only exhibited a positive correlation with D. masirevicii. A. japonicus and B.dothidea were the only fungal saprophytes that were positively correlated with the presence of each other among the four species of identified pathogens of kiwifruit. Collectively, the co‐occurrence and interaction results obtained in our study confirm that the four new fungal pathogens of kiwifruit exhibit a positive interaction with other fungi and that A. japonicus and B. dothidea exhibit a positive interaction with each other.

FIGURE 4.

FIGURE 4

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Co‐occurrence network diagram illustrating the co‐occurrence relationship of different species in different samples. Nodes in the network are sample nodes or species nodes, and the lines between sample nodes and species nodes represent the species contained in the samples. Species with abundance (number of sequences) greater than 50 are displayed by default (A). Species correlation network map illustrating correlated species. The presence of the four pathogenic fungi was selected to calculate the correlation coefficients such as Spearman's rank among species to reflect the correlation between species. The figure shows species with a p‐value < 0.05. Red lines represent a positive correlation and green lines represent a negative correlation. Line thickness indicates the value of the correlation coefficient, the thicker the line, the higher the correlation between species. The more lines there are, the more closely related a species is to other species (B).

Functional classification of the fungal microbiome following inoculation and infection with the four newly identified fungal pathogens of kiwifruit

FUNGuild was used for functional Guild annotation of the fungal communities present in the collected samples. It classifies fungal OTUs into specific trophic groups and further subdivides them into specific ecological guilds. Guilds with high abundance are shown in Figure 5. Saprotrophs accounted for about 97% of all the Guilds in three infected samples (AJ, AF and PO). The relative abundance of the Wood Saprotroph Guild was approximately 90% in BD samples. The Pathogen‐Endophyte‐Plant Pathogen‐Wood Saprotroph Guild was present at an extremely low percentage in samples except ND samples. Functional enrichment analysis revealed that four of the four fungal species were enriched in the saprotroph fungal designation, while B. dothidea was interestingly enriched in both the saprotroph and symbiotroph fungal designations (Figure 5B).

FIGURE 5.

FIGURE 5

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X axis is different samples, Y axis is the proportion of classification abundance of Guild in different samples (A), Functional classification of four pathogenic fungi (B).

Effect of pathogenic fungi on the health of mature fruit

The identified fungi caused lesions on developing kiwifruit, but their impact on mature fruit needed to be determined. Therefore, mature fruit were inoculated with each of the four newly identified fungal pathogens and the decay process was monitored for 10 dpi (Figure 6A–E). Results indicated that lesions caused by P. oxalicum were readily evident at 4 dpi at which time disease incidence was 8%, A. japonicus exhibited a disease incidence of 48% at 4 dpi and A. flavus had the highest disease incidence (92%) at 4 dpi. Disease incidence for all four fungi was 100% at 10 dpi. A. japonicus infection resulted in evident rot and necrosis at 9 dpi and all of the kiwifruit infected with the other four fungi exhibited rot and necrosis symptoms at 10 dpi (Figure 6F). These data indicate the four newly identified fungal pathogens of kiwifruit were readily able to infect and cause decay in mature kiwifruit.

FIGURE 6.

FIGURE 6

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Representative photographs of decay development in mature kiwi inoculated with the four identified species of fungal pathogens and disease incidence. Whole (top) and cross sections (down) of kiwifruit inoculated with A. japonicus (AJ) (A), A. flavus (AF) (B), B. dothidea (BD) (C), P. oxalicum (PO) (D), and uninoculated control (CK) (E). Photos illustrate decay development at 0, 2, 4, 6, and 10 days post‐inoculation (pdi). Plot of disease incidence in mature kiwifruit fruit inoculated with each of the four fungal pathogens (F).

DISCUSSION

Many species of filamentous fungi and yeast inhabit the surface and interior of kiwifruit. Once surface pathogens infect fruit or fruit and experience a dramatic change in environmental conditions, the delicate balance in the microecology of fruit will be altered, potentially resulting in disease. Previous studies have reported several species of filamentous fungi, including Botrytis cinerea, P. expansum, A. alternata, B. dothidea, and Diaporthe spp. as the main postharvest pathogens of kiwifruit (Trivedi et al., 2020). Deciphering the associations of key microbial taxa and pathogens is essential for using plant microbiota to promote plant growth and health (Toju et al., 2018). In our study, four pathogenic fungi were identified through the isolation and purification of endophytic fungi from kiwifruit with brown spot symptoms. The four pathogenic fungi were identified as A. japonicus, A. flavus (Zhu et al., 2022), B. dothidea, and P. oxalum based on morphological and molecular data and were confirmed to be pathogens through inoculation of developing and mature kiwifruit (Figure 1). The identified pathogenic fungi represent new postharvest pathogens of kiwifruit that have not been previously reported. The disease index for the four pathogens was calculated on kiwifruit and results indicated that the disease index was >40% at 10 dpi (Figure 2). Notably, the appearance and development of the decay lesions differed among the four different processes, which is consistent with previous studies with other postharvest pathogens (Toju et al., 2018; Wang et al., 2020a, 2020b; Zhu et al., 2022). Meanwhile, our result also revealed that the four identified pathogens also infected mature kiwifruit and the infection time and disease incidence were much faster and higher than that during in infection process of development of kiwifruit (Figure 2B and Figure 6F). This result was consistent with previously reported that maturity‐stage fruits were associated with susceptibility to suffering abiotic and biotic influence (Buron‐Moles et al., 2015).

The importance of biodiversity to ecosystem functioning is well recognized (Wang et al., 2020a, 2020b). Changes that occur in the microbial community of host tissues can potentially have a significant effect on the health of the host plant (Wisniewski & Droby, 2019). The interaction between a host and its endophytic microbial community is also affected by biological or environmental factors (Brader et al., 2017). Our previous study demonstrated that different environmental conditions can result in a shift in the structure of the endophytic microbial community of kiwifruit that either prevents (Sui et al., 2021) or promotes disease (Huang et al., 2022). The introduction of fungal pathogens through natural or artificial (inoculation) means could greatly increase the potential for a shift in the endophytic fungal community of fruit (Yin et al., 2023). Low modularity in fungal networks may exacerbate instability because cross‐modularity correlations between taxa are higher (Hector & Bagchi, 2007). Results of the present study indicated that fungal connectivity and diversity increased in diseased fruit, thus, increasing the functional importance of specific fungal species (Delgado‐Baquerizo et al., 2016). Our analysis revealed that the largest number of co‐occurring species occurred in ND samples and the highest number of species were common to the co‐occurring species in other sample groups. Similarly, a previous study reported on the co‐occurrence of different filamentous fungi, including Aspergillus and Penicillium, in hazelnuts (Corylus avellana) (Lombardi et al., 2022) and another study reported that Pseudopithomyces chartarum, Cladosporium allicinum, and Alternaria sect co‐occurred together with Botrytis spp., P. digitatum, and P. glabrum in the indoor environment of homes in Denmark depending on moisture conditions (Andersen et al., 2021). The newly identified pathogens in our study play a key role in the process of fruit decay in kiwifruit through their interactions. In the present study, co‐occurrence and interaction analysis revealed that the four identified fungi exhibited positive interactions with other fungal species present in kiwifruit. The pathogens A. japonicus and B.dothidea also exhibited a positive correlation in their presence in decayed tissues. Other studies have demonstrated that the co‐occurrence of P. spinulosum (Rundberget et al., 2004) and Phomopsis_prunorum (Adaskaveg et al., 1999), or their mycotoxins, fosters the development of fruit rot. Colletotrichum is also recognized as a major postharvest pathogen (Dai et al., 2022; Liu et al., 2022).

Functional analysis of our dataset indicated that B._dothidea was unique, belonging both to saprotroph and symbiotroph fungi guilds. Therefore, we hypothesize that endophytic fungi inhabiting kiwifruit can recruit other fungi and promote kiwifruit disease through synergistic interactions. The dominant community abundance (45%) of F. concentricum in CK (immature kiwifruit) did not cause disease. This may be because this pathogen was dormant in immature kiwifruit or that the fruit was tolerant of the pathogen (Nagpala et al., 2016; Tadych et al., 2015). The fungal flora was more diverse in ND samples than in asymptomatic fruit samples. This finding is similar to the results in our previous that found that during the process of decay development in ginger, the diversity of bacterial communities increased in symptomatic samples (Huang et al., 2022). The ability of the four newly identified pathogens to infect mature kiwifruit was also investigated. Results indicated that all four pathogenic fungi could infect and promote decay in mature fruit, which reduced both the postharvest longevity and quality of kiwifruit.

CONCLUSION

Microbial communities of fruit and their metabolites have a significant impact on the health of their plant hosts. The infection of kiwifruit by pathogenic fungi may be accompanied by the secretion of toxins, resulting in potential food safety problems. The present study identified four postharvest pathogens of kiwifruit, confirmed their pathogenicity, and provided new insights into the impact of introduced pathogens on the composition of the microbiome of kiwifruit and their role in decay development. Our studs not only contributes to our understanding of postharvest diseases of kiwifruit but provides information that can be potentially used to develop new strategies for the prevention of soft rot in harvested kiwifruit that will reduce economic losses attributed to postharvest pathogens and ensure the sale of high‐quality, safe fruit.

AUTHOR CONTRIBUTIONS

Ke Huang: Formal analysis (lead); methodology (lead); project administration (lead); supervision (lead); writing – review and editing (lead). Xiangcheng Sun: Conceptualization (equal); data curation (lead); methodology (lead); software (lead); validation (lead); visualization (lead); writing – original draft (lead). Xiaojiao Li: Data curation (equal); investigation (equal); resources (equal); supervision (equal). Xiaoya Huang: Formal analysis (equal); investigation (equal); validation (equal). Zhiqiang Sun: Formal analysis (equal); supervision (equal); visualization (equal). Wenhua Li: Project administration (equal); resources (equal); validation (equal). Junkui Wang: Conceptualization (equal); formal analysis (equal); supervision (supporting). Dawei Tian: Formal analysis (equal); supervision (equal). Chenglin Lin: Data curation (equal); formal analysis (equal); investigation (equal); validation (supporting). Xuehong Wu: Conceptualization (equal); formal analysis (equal); resources (equal). Cailing Miao: Data curation (supporting); formal analysis (supporting); methodology (supporting). Yujing Li: Formal analysis (supporting); investigation (supporting); resources (supporting). Panpan Xu: Data curation (equal); software (equal); supervision (equal); validation (equal); visualization (equal). Tianyu Fan: Conceptualization (equal); investigation (equal); resources (equal). Shuxin Zhu: Formal analysis (supporting); investigation (supporting); methodology (supporting). Na Li: Methodology (equal); validation (equal); visualization (equal). Li Zeng: Conceptualization (equal); data curation (equal); investigation (equal); validation (equal). Jia Liu: Data curation (equal); investigation (equal); methodology (equal); project administration (equal); supervision (equal); writing – review and editing (equal). Yuan Sui: Conceptualization (equal); formal analysis (equal); funding acquisition (equal); investigation (equal); project administration (equal); resources (equal); supervision (equal); writing – review and editing (equal).

FUNDING INFORMATION

No funding information provided.

CONFLICT OF INTEREST STATEMENT

The authors declare no financial or other competing conflicts of interest.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (grant no. 31972133 & 32360411), Natural Science Foundation of Chongqing Science and Technology Bureau (grant no. CSTB2022NSCQ‐MSX1317), Chongqing Talents – Innovation Leader Project to YS.

Huang, Ke , Sun, X. , Li, X. , Huang, X. , Sun, Z. , Li, W. et al. (2023) Pathogenic fungi shape the fungal community, network complexity, and pathogenesis in kiwifruit. Microbial Biotechnology, 16, 2264–2277. Available from: 10.1111/1751-7915.14344

Ke Huang, Xiangcheng Sun, and Xiaojiao Li authors contributed equally to this work.

DATA AVAILABILITY STATEMENT

ITS Sequencing data sets are deposited in the NCBI SRA database with the accession number PRJNA924070. The ITS and nLSU sequencing data set are deposited in the NCBI GenBank with assigned accession numbers as follows: Aspergillus_japonicus in OP941581 and OP941634, Aspergillus_flavus in OP941583 and OP941633, Botryosphaeria_dothidea in OP941584 and OP941635, Penicillium_oxalicum in OP941587 and OP941591.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

ITS Sequencing data sets are deposited in the NCBI SRA database with the accession number PRJNA924070. The ITS and nLSU sequencing data set are deposited in the NCBI GenBank with assigned accession numbers as follows: Aspergillus_japonicus in OP941581 and OP941634, Aspergillus_flavus in OP941583 and OP941633, Botryosphaeria_dothidea in OP941584 and OP941635, Penicillium_oxalicum in OP941587 and OP941591.

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