Introduction

Chinese hickory (Carya cathayensis) is primarily distributed in Shexian and Ningguo in Anhui Province, as well as in Lin’an and Chun’an in Zhejiang Province1. The hickory kernel, a key product of the hickory tree, is prized for its high oil content, significant protein levels, and essential minerals that support human health2. Due to its unique flavour and excellent nutritional value, the hickory kernel is highly popular among consumers and holds substantial economic value3. As a result, the Chinese hickory industry has become a significant economic contributor in these regions. However, the increasing economic value of Chinese hickory has led to overreliance on chemical fertilizers and herbicides by farmers seeking higher yields. This has resulted in nutrient imbalances in the soil, increased soil acidification, and a decline in biodiversity within Chinese hickory forests. Consequently, the ecosystem of these forests has degraded, negatively impacting the broader regional forest ecosystem.

Recently, an unknown root rot disease has emerged in Chinese hickory forests, exhibiting typical soil-borne characteristics. The pathogen invades through the fine roots and progressively spreads to the lateral and main roots4,5,6. Affected mature trees exhibit symptoms such as chlorosis, yellowing leaves, reduced leaf size, and premature defoliation. Under high temperatures and drought conditions during the summer, the leaves tend to wilt. Anatomical analysis of the diseased roots reveals that the fine root cortex loses its luster and can be easily peeled off, while the xylem darkens and turns yellow. As the disease progresses, root rot intensifies, eventually leading to the death of the entire tree5. This root rot has spread widely across major hickory kernel production areas, with some regions reporting an incidence rate as high as 90%6. The disease has caused substantial tree mortality, resulting in significant economic losses for farmers and posing a serious threat to the sustainability of the Chinese hickory industry and regional ecological stability. Thus, it is critical to identify the underlying cause of C. cathayensis tree root rot and develop efficient control strategies.

The composition of pathogens responsible for root rot varies significantly by region7. For instance, in Heilongjiang Province, Fusarium oxysporum is the primary pathogen causing soybean root rot8, while in Sichuan Province, the main isolates of soybean root rot include F. oxysporum, F. graminearum, and F. equiseti9. In North America, including Canada, F. oxysporum and F. solani are the predominant pathogens10. These regional variations in dominant pathogens may help explain why targeted chemical treatments often yield limited results, despite F. oxysporum being frequently reported as the causative agent of C. cathayensis root rot11. Additionally, several studies have emphasized that the role of imbalances in soil microbial communities in the development of root rot disease12,13,14,15. Therefore, understanding the differences in microbial community composition between the soil and root tissues of healthy and diseased C. cathayensis trees is crucial for developing more effective disease control strategies.

Root rot, caused by bacteria, viruses, oomycetes, and fungi, is one of the most significant plant diseases worldwide16,17. Previous studies utilized high-throughput sequencing to analyze microbial composition differences in the rhizosphere of C. cathayensis trees, revealing disruptions in the balanced microbial state under various health conditions5. However, the specific composition of the fungal community and its dynamic changes during the transition from health to disease remain unclear. In this study, we systematically collected root tissue, rhizosphere soil, and bulk soil samples from both healthy and diseased C. cathayensis trees. Using high-throughput sequencing, we analyzed the structure and dynamics of the fungal community to: (1) identify characteristic differences in fungal communities between healthy and diseased trees; (2) provide a foundation for isolating and identifying potential pathogenic fungi; and (3) inform the development of ecological control strategies for root rot disease.

Materials and methods

Experimental design and sample collection

To reveal the difference in microbial community composition of C. cathayensis trees with varying health status, we divided the trees within the same plantations into three groups: dead plants (DP group), diseased plants (SP group), and healthy plants (NP group). C. cathayensis trees with heights ranging from 7.5 to 8.5 m and spaced 30–50 m apart were randomly selected as samples, based on criteria from the previous study5. Rhizosphere soil (RS), bulk soil (BS), and root tissues (RT) were collected from the DP, SP, and NP groups of C. cathayensis trees as described previously18. The fungal community composition of these samples was determined using ITS high-throughput sequencing, enabling a precise comparison of microbial community differences across the DP, SP, and NP groups. Detailed protocols for sample collection and processing can be found in our previous report5.

Extraction of the microbial genomic DNA

Microbial genomic DNA was extracted from root tissues and soil using CTAB method, as previously described19. Briefly, 0.5 g of root tissues or soil was treated with 1 ml of PBS buffer (pH 7.4, 10 mM Na2HPO4, 1.8 mM KH2PO4, 2.7 mM KCl, and 200 mM NaCl) and ground thoroughly with liquid nitrogen. Subsequently, 1 ml of CTAB lysate and 20 µl 10 mg/ml lysozyme were added, and the mixture was incubated in a water bath at 65 °C for 3 h. After centrifugation at 12, 000 rpm for 10 min, the suspension was treated sequentially with an equal volume of phenol: chloroform: isoamyl alcohol (25:24:1) and chloroform: isoamyl alcohol (24:1). The genomic DNA was then precipitated by adding 3/4 volume of ice-cold isopropyl alcohol. Finally, the DNA precipitate was washed three times with 75% ethanol and then resuspended with 60 µl of elution buffer (10 mM Tris-HCl pH 8.5, 0.2 mM EDTA).

Internal transcribed spacer 1 region amplification, quantitation, and sequencing

The internal transcribed spacer 1 (ITS1) region of each sample was amplified for sequencing using the primer pair (ITS5: 5´-GGAAGTAAAAGTCGTAACAAGG-3´, ITS2: GCTGCGTTCTTCATCGATGC-3´) containing a barcode20. PCR amplification was performed using the Phusion High-Fidelity PCR Master Mix kit (New England Biolabs, USA) according to the manufacturer’s instructions. Amplified products were detected by 2% agarose gel electrophoresis. Samples with bands between 200 and 400 bp were extracted using the QIAquick Gel Extraction Kit (Qiagen, Germany) for sequencing. The library was then constructed using the TruSeq DNA PCR-Free Sample Preparation Kit (Illumina, USA). After quantitative analysis, the constructed library was sequenced on the Illumina HiSeq2500 platform.

Sequence processing

Raw data obtained from the HiSeq2500 runs were processed using the the EasyAmplicon pipeline21 with default settings, based on the barcode sequences. High-quality filtering was applied to remove reads with ambiguous or homologous sequences, as well as those shorter than 200 bp, from the raw tags in order to obtain clean tags. Taxonomic identification of fungal amplicon sequence variants (ASVs) was performed using the UNITE database22. Unique sequences were processed using unoise323 with a minimum size of 10 to reduce the dataset size and achieve single-base ASV accuracy. Meanwhile, sequences assigned to chloroplasts and mitochondria were excluded. The EasyAmplicon pipeline21 was used to evaluate richness and diversity indices including ACE, Chao1, Richness, Simpson, and Shannon. Additionally, the pipeline generated feature tables and phylogenetic trees.

Statistical analysis

Statistical analysis was performed using R software (version 4.2.2), as previously described5. Briefly, ANOVA with Tukey HSD was used to compare the statistical significance of the α-diversity between different groups (P < 0.05). β-diversity was performed using the permutational multivariate analysis of variance (PERMANOVA). The edgeR package24 was employed to compare the differences in ASV abundance between different groups, with the Benjamini–Hochberg method applied to control the false discovery rate.

Network analysis and visualization were performed using the ggClusterNet package (version 0.1.73)25, with a correlation coefficient (R) greater than 0.8 and a P-value less than 0.05. ASVs with a relative abundance > 0.01% were selected for network relationship exploration, and the topological features of the network were assessed. The network graph was color-coded according to modules, and network vulnerability was calculated using the ggClusterNet package, with default thresholds of R > 0.8 and P < 0.05. For cross-network analysis between bacteria and fungi, the corBionetwork function was used, with the same thresholds of R > 0.8 and P < 0.05.

Results

Sequencing data characterisation

A total of 1,695,573 ITS1 amplicon sequence reads were obtained from 27 samples, with an average of 62,799 reads per sample. After quality control using the EasyAmplicon pipeline, an average of 61,425 clean reads were retained. The sequences were taxonomically classified into fungal ASVs based on the UNITE database, resulting in 879 ASVs for RT, 978 ASVs for RS, and 996 ASVs for BS, respectively. Six fungal ASVs were identified as contaminants using MicroDecon26 and were removed from the dataset. Sequentially, the rarefaction curves were drawn to analyze the sequencing depth. Our results showed that as the sequencing depth gradually deepened, the species richness curve gradually flattened, indicating that the sequencing depth could truly reflect the composition of the entire fungal microbiome (Supplementary Fig. 1).

Alpha diversity

Species richness and Shannon indices were used to analyze the alpha diversity of RT, RS, and BS in C. cathayensis trees. In RT, the species richness indices for the DP, NP, and SP were 278, 236, and 337, respectively (Fig. 1A). The Shannon indices for the DP, NP, and SP were 2.88, 3.02, and 3.42, respectively (Fig. 1A). No significant difference were observed between the species richness and Shannon indices of the DP, NP, and SP groups (Fig. 1A). In RS, both the species richness and Shannon indices followed a same trend. The richness indices for the DP, NP, and SP were 334, 264, and 280, respectively (Fig. 1B), and the Shannon indices were 3.49, 2.76, and 3.05, respectively (Fig. 1B). In BS, the richness indices for the DP, NP, and SP were 349, 248, and 359, respectively (Fig. 1C), and the Shannon indices were 3.51, 2.51, and 3.81, respectively. Significant differences were found between these groups (P < 0.05) (Fig. 1C).

Fig. 1
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The Richness and Shannon indices in (A) RT, (B) RS, and (C) BS samples. RT, RS, and BS are root tissue, rhizosphere soil, and bulk soil, respectively. DP, NP, and SP represent dead, healthy, and diseased C. cathayensis trees, respectively. The first (25%), third (75%) and median quartiles of each data set are shown in the box plots. Significant differences (P < 0.05) between datasets are indicated in lowercase.

Beta diversity

Beta diversity of RT, RS, and BS in C. cathayensis trees was analyzed using Constrained PCoA based on Bray–Curtis distance. In RT, the results indicated differences in fungal community beta diversity among the DP, NP, and SP groups (P = 0.06), explaining 26.8% of the variance (Fig. 2A). In RS, fungal communities also exhibited differences between the DP, NP, and SP groups (P = 0.1), accounting for 26.4% of the variation, although the difference was not statistically significant (Fig. 2B). Similarly, differences were observed in fungal communities between the DP, NP, and SP groups of BS (P = 0.18), which accounted for 26.5% of the variation (Fig. 2C).

Fig. 2
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Bray-Curtis distances of (A) root tissue, (B) rhizosphere soil, and (C) bulk soil plotted using constrained PCoA. Each point is coloured by the different group of dead, healthy, and diseased trees. Dead, healthy, and diseased trees are abbreviated DP, NP, and SP, respectively.

Composition of the fungal community in NP, SP, and DP groups

The healthy (NP), diseased (SP), and dead (DP) C. cathayensis trees were further divided into RT, RS, and BS groups to better analyze differences in fungal communities. The top 10 fungi at the phylum and genus level are shown in Fig. 3. In the NP group, the top 10 abundant fungal phyla across RT, RS, and BS specimens were Basidiomycota (36.97–78.80%), Ascomycota (14.73–55.63%), Rozellomycota (0.37–1.94%), Mortierellomycota (1.05–1.20%), Chytri-diomycota (0.09–0.15%), Mucoromycota (0.00–0.09%), Glomeromycota (0.00–0.09%), Aphelidiomycota (0.01–0.04%), and Zoopagomycota (0.00–0.01%) (Fig. 3A). Although the ranking of the top 10 fungal communities was basically the same across RT, RS, and BS groups, their relative abundances varied significantly. For example, Basidiomycota was most abundant in the RS and BS groups, whereas Ascomycota dominated the RT group. Interestingly, the combined proportion of Basidiomycota and Ascomycota in RT, RS, and BS groups was 92.59,93.53, and 93.63%, respectively, making them the two largest fungal phyla (Fig. 3A). The top 10 fungal genera in the NP group for RT, RS, and BS specimens included Scleroderma (5.16–16.83%), Russula (6.11–11.33%), Lactarius (1.99–11.24%), Laccaria (0.11–10.83%), Phlyctis (2.76–8.63%), Inocybe (0.75–6.51%), Arxiella (0.05–10.36%), Tomentella (2.04–3.81%), and Hymenogaster (1.04–3.34%) (Fig. 3B).

Fig. 3
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Top 10 dominant fungi relative abundance in different groups of C. cathayensis trees. Comparison of the 10 most abundant fungi at the (A) phylum level and (B) genus level in healthy, diseased, and dead trees. RT, RS, and BS refer to root tissue, rhizosphere soil, and bulk soil, respectively.

The top 10 fungal phyla in RT, RS, and BS specimens from the SP group were Ascomycota (57.77–76.23%), Basidiomycota (8.76–14.69%), Mortierellomycota (1.42–8.67%), Rozellomycota (0.35–1.62%), Chytri-diomycota (0.39–0.85%), Mucoromycota (0.07–0.25%), Zoopagomycota (0.02–0.19%), Aphelidiomycota (0.06–0.08%), and Glomeromycota (0.03–0.06%) (Fig. 3A). Similarly, the top 10 fungal genera in these samples were Nadsonia (4.82–11.67%), Solicoccozyma (1.29–8.19%), Coniosporium (0.00–13.05%), Mortierella (1.41–8.65%), Chaetomium (1.42–7.32%), Pezicula (0.46–6.18%), Russula (2.18–5.07%), Cistella (0.55–5.77%), and Trichoderma (1.99–3.10%) (Fig. 3B).

In the DP group, the top 10 dominant fungal phyla in RT, RS, and BS specimens were Ascomycota (55.20–79.10%), Basidiomycota (7.55–30.73%), Mortierellomycota (1.40–14.25%), Chytridiomycota (0.98–1.70%), Mucoromycota (0.02–2.20%), Rozellomycota (0.32–1.39%), Aphelidiomycota (0.06–0.38%), Glomeromycota (0.04–0.25%), and Zoopagomycota (0.00–0.31%) (Fig. 3A). The top ten fungal genera in these samples were Ganoderma (0.16–20.81%), Mortierella (1.32–14.01%), Gliocladiopsis (0.16–14.91%), Trichoderma (2.74–9.77%), Chaetomium (2.10–7.27%), Dictyochaeta (0.92–9.76%), Nadsonia (0.34–11.29%), Paracremonium (0.18–4.08%), and Solicoccozyma (1.86–3.34%) (Fig. 3B).

Fungal community composition comparison

After identifying the fungal community composition in healthy, diseased, and dead C. cathayensis trees, we compared the fungal community composition of healthy, diseased, and dead C. cathayensis across the RT, RS, and BS groups to accurately analyze the presence of potentially pathogenic microorganisms (Fig. 4). In the RT group, the top 10 fungal phyla in the dead, diseased, and healthy trees were Ascomycota, Basidiomycota, Mortierellomycota, Chytridiomycota, Rozellomycota, Glomeromycota, Aphelidiomycota, Mucoromycota, and Zoopagomycota (Fig. 4A). Notably, the relative abundance of the top nine fungal phyla in diseased trees (77.83%) was lower than that in healthy (94.81%) and dead trees (93.32%) (Fig. 4A). The dominant fungal genera in these samples were Ganoderma, Russula, Laccaria, Arxiella, Phlyctis, Chaetomium, Pezicula, Nadsonia, and Mortierella (Fig. 4B). Interestingly, the total relative abundance and fungal composition of the top 9 fungal genera differed between healthy, diseased, and dead trees in the RT group (Fig. 4B).

Fig. 4
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Top 10 dominant fungi relative abundance across different ecological niches. Comparison of the 10 most abundant fungi at the (A) phylum level and (B) genus level in root tissue, rhizosphere soil, and bulk soil. Dead, healthy, and diseased trees are abbreviated as DP, NP, and SP, respectively.

In the RS group, the top ten fungal phyla in dead, diseased, and healthy trees were also dominated by Ascomycota, Basidiomycota, Mortierellomycota, Rozellomycota, Chytridiomycota, Aphelidiomycota, Mucoromycota, Zoopagomycota, and Glomeromycota (Fig. 4A). It is noteworthy that the total relative abundances and fungal compositions of the top 9 fungal genera in dead and diseased trees in the RS group were similar, but significantly different from those in healthy trees (Fig. 4A). Furthermore, the fungal community compositions in healthy, diseased, and dead trees in the RS group differed in terms of the top 9 fungal genera (Fig. 4B).

A similar trend was also found in the BS group as well. The total relative abundances and fungal compositions of the top 9 fungal genera in dead and diseased trees in the BS group were similar but different from those in healthy trees (Fig. 4A). Additionally, there were significant differences in the total relative abundances and fungal compositions among healthy, diseased, and dead trees in the BS group (Fig. 4B).

Variance of fungal communities

To reveal the effects of fungal communities on the health status of C. cathayensis trees, we compared the variation of fungal communities across healthy, diseased, and dead trees at the RT, RS, and BS levels (Fig. 5 and Supplementary Tables 1, 2, 3). In the root tissue group, the abundance of 29 fungal genera was significantly depleted, while 28 fungal genera were enriched in the DP group compared to the NP group (Fig. 5A and Supplementary Table 1) (P < 0.01). Similarly, 29 fungal genera were significantly depleted and 23 fungal genera were enriched in the SP group compared to the NP group (Fig. 5B and Supplementary Table 1) (P < 0.01). In addition, there were 19 depleted and 25 enriched fungal genera between the DP and SP groups (Fig. 5C and Supplementary Table 1) (P < 0.01).

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
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Variation in fungal abundance in root tissue, rhizosphere soil, and bulk soil samples. Changes in fungal communities in root tissue, rhizosphere soil, and bulk soil samples from (A) dead trees and (B) diseased trees compared to healthy trees, respectively. (C) Changes in fungal communities in root tissue, rhizosphere soil, and bulk soil samples from dead trees compared to diseased trees. fold-change > 2, P < 0.01. The numbers in the figure correspond to the quantities of green and red dots, which represent fungal ASVs that are significantly depleted and enriched, respectively. Dead, healthy, and diseased trees are abbreviated as DP, NP, and SP, respectively.

In the rhizosphere soil group, the abundance of 26 fungal genera was depleted, and 23 fungal genera were enriched in the DP group compared to the NP group (Fig. 5A and Supplementary Table 2). In the SP group, the abundance of 22 fungal genera was significantly depleted, and 24 fungal genera were enriched compared to the NP group (Fig. 5B and Supplementary Table 2). Additionally, 12 fungal genera were depleted, and 21 fungal genera were enriched between the DP and SP groups (Fig. 5C and Supplementary Table 2) (P < 0.01).

In the bulk soil group, 25 fungal genera were depleted, and 20 fungal genera were significantly enriched in the DP group compared to the NP group (Fig. 5A and Supplementary Table 3). In the SP group, 26 fungal genera were depleted, and 24 fungal genera were enriched compared to the NP group (Fig. 5B and Supplementary Table 3). Similarly, there were 20 depleted and 15 enriched fungal genera between the DP and SP groups (Fig. 5C and Supplementary Table 3) (P < 0.01).

Finally, We compared the common fungal communities of healthy, diseased, and dead C. cathayensis trees at the RT, RS, and BS levels (Fig. 6 and Supplementary Tables 4, 5, 6). In the root tissue group, a total of 13 fungal genera were shared between the NP, SP, and DP trees. Notably, 14 fungal genera were shared between the DP and SP trees (Fig. 6A and Supplementary Table 4). Likewise, a total of 17 fungal genera were shared between the NP, SP, and DP trees in the rhizosphere soil group, while 18 fungal genera were shared between the DP and SP trees (Fig. 6B and Supplementary Table 5). In the bulk soil group, 40 fungal genera were shared between the DP and SP trees. Additionally, 14 fungal genera were shared between the NP, SP, and DP trees (Fig. 6C and Supplementary Table 6).

Fig. 6
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The common fungi in (A) root tissue, (B) rhizosphere soil, and (C) bulk soil of different tree groups shown in Venn diagrams. The numbers in the figure represent the number of fungal ASVs. DP, NP, and SP represent dead, healthy, and diseased trees, respectively.

Microbiome Co-occurrence networks in NP, SP, and DP groups

To explore the differences in microbial network structures in the soil at the roots of healthy, diseased, and dead C. cathayensis trees, we analyzed previously published bacterial amplicon data5 and fungal amplicon data to calculate the topological characteristics of their co-occurrence networks. In the bacterial network, the soil from dead C. cathayensis trees contained 4131 nodes and 7254 edges, which was higher than the numbers found in the soil of healthy and diseased trees (Fig. 7A). For the fungal network, the soil from diseased C. cathayensis trees had 791 nodes and 768 edges, surpassing the soil of both healthy and dead trees in terms of network size (Fig. 7B). A network vulnerability analysis further revealed that the bacterial network in the soil of diseased C. cathayensis trees was more vulnerable, while the fungal network in the soil of healthy trees was more vulnerable (Fig. 7C,D). Additionally, we examined the cross-networks of bacteria and fungi in the root soils of healthy, diseased, and dead C. cathayensis trees. The soil from healthy trees possessed 1000 nodes and 992 edges, significantly more than those in the soil from diseased and dead trees (Table 1). Notably, the interaction between bacteria and fungi in the soil of healthy trees were predominantly positive, with very few negative correlation (Fig. 7E and Supplementary Data 1). In contrast, the soil of diseased and dead trees exhibited relatively independent bacterial and fungal networks, with fewer interactions and an approximately equal proportion of positive and negative correlations (Fig. 7E and Supplementary Data 2 and 3). The cross-network analysis indicated that a harmonious coexistence of bacteria and fungi in the root soils of C. cathayensis trees is crucial for preventing root rot disease.

Fig. 7
Fig. 7The alternative text for this image may have been generated using AI.
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Ecological networks of microbial associations across different tree groups. (A) Bacterial network and (B) fungal network in the root soils of healthy, diseased, and dead C. cathayensis trees. Vulnerability of (C) bacterial networks and (D) fungal networks in the root soils of healthy, diseased, and dead C. cathayensis trees. (E) Bacterial-fungal cross-networks in the root soils of the healthy, diseased, and dead C. cathayensis trees. Each node represents an ASV and is colored according to its biological category, with pink denoting bacteria and blue denoting fungi. The size of each node corresponds to the degree of its respective ASV. Strong (R > 0.8) and statistically significant (P < 0.05) associations are indicated by connections between nodes.

Table 1 General characteristics of bacterial-fungal co-occurrence networks in healthy, diseased, and dead C. cathayensis trees.
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Distinction of the dominant fungal genera

To provide a comprehensive overview of the dominant fungal genera in dead, healthy, and diseased C. cathayensis trees, we present the top 70 dominant fungal genera in phylogenetic trees at the RT and RS group levels (Fig. 8). In the RT group, the dominant fungal genera of dead C. cathayensis trees included Ganoderma, Hymenopellis, Xylaria, Codinaea, Paracremonium, Penicillifer, Ilyonectria, and Mariannaea. In contrast, the dominant fungal genera in diseased C. cathayensis trees in the RT group were Nadsonia, Pezicula, Leotia, Humicola, and Chaetomium. Healthy C. cathayensis trees in the RT group were rich in Tomentella, Scleroderma, Laccaria, Amanita, Phlyctis, and Arxiella (Fig. 8A).

Fig. 8
Fig. 8The alternative text for this image may have been generated using AI.
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Taxonomic tree showing the relative abundance of the top 70 dominant fungi in (A) root tissue and (B) rhizosphere soil across different tree groups. Phyla within the tree are indicated by color ranges. The relative abundance of each ASV in dead, healthy, and diseased trees is represented by black, green, and red colored bars, respectively. iTOL was used to draw the taxonomic dendrogram. Dead, healthy, and diseased trees are abbreviated as DP, NP, and SP, respectively.

In the RS group, the dominant fungal genera in the soil of dead C. cathayensis trees included Peziza, Cladophialophora, Gliocladiopsis, Paracremonium, Dictyochaeta, Tolypociadium, and Trichoderma. Similarly, diseased C. cathayensis trees of the RS group were rich in Solicoccozyma, Nadsonia, Coniosporium, and Cistella. In the case of healthy C. cathayensis trees in the RS group, Hymenogaster, Laccaria, Inocybe, Scleroderma, Lactarius, and Russula were the dominant fungal genera (Fig. 8B).

Discussion

Root rot is a common soil-borne plant disease that seriously affects crop production worldwide7. In recent years, root rot in C. cathayensis has devastated the C. cathayensis industry, severely affecting production and local farmers’ income. However, the pathogen responsible for this disease remains unknown. Previous studies utilizing 16 S rRNA amplicon sequencing identified imbalances in the root microbiota, particularly the reduction of certain probiotics, but did not uncover any potential pathogens5. To gain further insights into the pathogenesis of C. cathayensis root rot disease, we employed ITS1 amplicon sequencing to compare the fungal community composition in diseased, dead, and healthy C. cathayensis trees. The results revealed no significant differences in fungal richness and Shannon indices between diseased, dead, and healthy C. cathayensis in the RT and RS groups, indicating that there were no major changes in fungal richness or evenness across these conditions (Fig. 1A,B). However, in the BS group, we observed significant differences in fungal richness and evenness between diseased, dead, and healthy C. cathayensis trees (Fig. 1C). In addition, our results also showed that Ascomycota and Basidiomycota were the two dominant fungal phyla in the root tissue, rhizosphere soil, and bulk soil of C. cathayensis trees, accounting for approximately 93.63% of the total fungal community (Fig. 4A). This is consistent with previous studies, as these two phyla are the largest terrestrial fungal groups and their closest sister taxa17,27. Furthermore, in the RT and RS groups, the proportion of Ascomycota was higher in dead and diseased trees compared to healthy trees (Fig. 4A). In particular, Basidiomycota is dominant in RS and BS groups of healthy trees. However, in diseased and dead trees, the relative abundance of Ascomycota in the RS and BS groups was significantly higher than that of Basidiomycota (Fig. 4A). Interestingly, some Ascomycota fungi are known plant pathogens24, suggesting that the shift in fungal community composition could be linked to the onset of C. cathayensis root rot. This aligns with earlier reports5,12,14,15,28,29 that disturbances in fungal species balance can lead to plant diseases.

When analyzing the dominant microbiota in the RT and RS of diseased, dead, and healthy trees, we unexpectedly identified the dominant fungi Xylaria and Ilyonectria in the RT of dead trees, which were absent in healthy trees (Fig. 8A). Condinaea and Gliocladiopsis were predominantly present in dead trees, with less abundance in diseased and healthy trees (Fig. 8A and B). These findings are consistent with studies suggesting that Xylaria can cause root-related diseases, such as black root rot in apples and taproot decline in soybeans30. Additionally, Codinaea fertilis has been implicated in root rot in white clover31, while Ilyonectria is known to cause root rot and rust in ginseng32. Gliocladiopsis has also been linked to root rot in avocado33. Taken together, these results suggest that Xylaria, Condinaea, Ilyonectria, and Gliocladiopsis may be involved in the development of C. cathayensis root rot, marking the first report of potential pathogens in this context.

Furthermore, Chaetomium was abundant in the RT of diseased trees, but less so in healthy and dead trees (Fig. 8A). Chaetomium globosum is known to control a wide range of plant pathogens34, suggesting that Chaetomium may play a biocontrol role in C. cathayensis trees. Similarly, Trichoderma, which was abundant in the rhizosphere soil of dead and diseased trees, but less so in healthy trees (Fig. 8B), is an important biocontrol fungus that helps protect plants from disease35. These findings suggest that both Chaetomium and Trichoderma have potential as biocontrol agents. Their inhibitory activity against the candidate pathogen should be further validated through in vitro dual-culture tests in future studies.

Finally, the dominant fungi identified in the root tissues or rhizosphere soil of healthy trees included Laccaria, Amanita, Inocybe, Lactarius, and Russula (Fig. 8A,B), all of which belong to Basidiomycota. The presence of L. ochropurpurea has been shown to enhance pathogen resistance in chestnut plantations (Castanea dentata)36. Inocybe, Lactarius, Russula, and Amanita are the dominant fungal species in C. illinoinensis trees37. These fungi are likely to serve as beneficial probiotics for C. cathayensis trees.