The sugarcane cultivars were planted in the same sugarcane plot in Nanshunyi Village, Dagang Town, Nansha District, Guangzhou City, Guangdong Province, China (22°47′04.4″N and 113°26′14.8″E), following regular irrigation, fertilization and maintenance practice. Samples used in this study were collected on 31 October 2019. We arbitrarily determined sugarcane root rot severity according to the height and stoutness of above-ground part of plants, as healthy (DHZA), mildly diseased (DHZB), moderately diseased (DHZC) and severely diseased (DHZD) groups (Supplementary Figure S1B). “DHZ” is short for “Dahuizhong”, the cultivar name called by the local farmer. Six sugarcane plantlets (adult-plants) from each group were randomly selected for collection of their roots and rhizosphere soil samples. The sampling follows the established method (Clemensson-Lindell and Persson., 1992): the roots of each sugarcane were dug out and loosely attached soil was removed by manual shaking, whereas the rhizosphere soil was collected from the surface of the roots. After collecting the rhizosphere soil, the sugarcane roots were washed with clean water, and air-dry. The cleaned root system and rhizosphere soil were sealed in sterile ziplock bags for later use. Photographs of the dried roots were taken, and total root length, root surface area, and root volume were measured using the software RhizoVision Explorer v1.0 (Seethepalli et al., 2021).

Microbial DNA was extracted from fresh soil samples using the E. Z.N.A^® Soil DNA Kit (Omega Bio-Tek Inc., Norcross, GA, United States ). The concentration and purity of extracted DNA were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States ). The microbial community structure was determined based on paired-end amplicon sequencing of the 16S rDNA gene (for bacteria) and the Internal Transcribed Spacer (ITS) region of fungal ribosomal DNA (for fungi) on the Illumina MiSeq PE 250 platform, following the established protocol (Schmidt et al., 2019). The V5-V7 hypervariable regions of the bacterial 16S rDNA gene were amplified using primers 799F and 1193R (Zhang et al., 2019) to assess bacterial communities, using the established PCR reaction conditions and procedures were described in Han et al. (2020). The fungal ITS1 region was amplified from each sample using primers ITS1F and ITS2R (Schmidt et al., 2019), which provided a comprehensive coverage with the highest taxonomical accuracy for fungal sequences, to assess fungal communities, using the reported PCR reaction conditions and procedures (Kang et al., 2021). PCR reaction was performed on a Bio-Rad S1000 Thermal Cycler (Bio-Rad, CA). Subsequently, high-throughput sequencing of the DNA libraries was performed using Illumina Miseq PE 250 platform and the sequences were deposited in the NCBI database with accession number PRJNA858005 and PRJNA858105 (http://www.ncbi.nlm.nih.gov), and GSA database (Chen et al., 2021) with accession number PRJCA011320 (https://ngdc.cncb.ac.cn/gsa/).

For multiple samples sequenced in parallel, the sliding window method was used to screen the quality of the paired-end sequences in the FASTQ format one by one, and then FLASH v1.2.11 (http://ccb.jhu.edu/software/FLASH/) was used to pair the paired-end sequences that passed the primary quality screening according to overlapping bases (Magoč and Salzberg., 2011). Subsequently, the ligated sequence identifications were assigned to corresponding samples, and QIIME2 v2020.2 (https://qiime2.org//) were used to demultiplexed raw sequences (Bolyen et al., 2019). Quality control functions in the DADA2 v1.5.2 (https://benjjneb.github.io/dada2/bigdata.html) plugin for precut, reverse reads and noise removal, chimera detection and removal to filter low quality reads, reconstruct amplicon sequence variants (ASVs), and generate feature tables for ASV counts (Callahan et al., 2016). Sequences belonged to archaea, mitochondria, and chloroplasts were removed. To avoid potential bias caused by differences in sequencing depth, the number of sequences in each sample was rarefied (Zhou et al., 2022). Bacterial and fungal community alpha diversity was processed in the Majorbio cloud (https://cloud.majorbio.com). Subsequently, at the ASV level, the microbial richness (Chao1) and diversity (Shannon) of rhizosphere soil microbiomes correlating with different disease severity (DHZA, DHZB, DHZC, and DHZD) were calculated to characterize the differences in alpha diversity between samples. Beta diversity is based on the Abundance-based Jaccard distance algorithm and the ANOSIM method, to evaluate and compare the differences between sample communities. Heatmaps, PCoA, and RDA were performed using the Majorbio cloud (http://cloud.majorbio.com).

BugBase (https://bugbase.cs.umn.edu/index.html) was used for prediction of bacterial phenotypes (Ward et al., 2017), including biofilm formation, pathogenicity, presence of mobile element(s), oxygen utilization (including aerobic, anaerobic, facultatively anaerobic, and oxidative stress tolerant), etc. The fungal community was analyzed by microecological guild using FUNGuild (Nguyen et al., 2016), which could be subdivided into three nutritional modes: pathotroph, symbiotroph and saprotroph. By respectively linking bacterial species classification and fungal species classification with phenotypic BugBase and functional FUNGuild classification, the classification of bacteria and fungi was predicted.

The interaction between rhizosphere soil bacteria and the pathogen Fusarium genus was evaluated by determining the Spearman rank correlation coefficient and topological network properties of Fusarium and bacterial pairs. Co-occurrence networks were constructed through the MENA (molecular ecological network analysis pipeline) based analysis protocol (Feng et al., 2022). The network topology characteristics include the total number of network nodes (Operational Taxonomic Unit, OTU), total number of edges (connections between nodes), and degrees of co-occurrence (the number directly related to nodes) (Hartman et al., 2018). The co-occurrence network diagram was drawn using Cytoscape v.3.3.0 software (http://www.cytoscape.org/) to visualize the network.

The diseased plants with typical symptoms of sugarcane root rot were collected and separated according to the tissue separation method (Xiao et al., 2020) as schematically presented in Supplementary Figure S1C. The fungal hyphae and spore were observed under a microscope, and classified according to The Fusarium laboratory manual (Leslie and Summerell., 2006), and verified whether they were pathogen according to Koch’s Law (Supplementary Figure S1D). For molecular identification, fungal genomic DNA was extracted using the fungal genomic DNA extraction kit (Omega Bio-Tek Inc., Norcross, GA, United States ) and subjected to PCR amplification using primers EF-1 and EF-2 (Geiser et al., 2004), following standard PCR reaction conditions and procedures (Herron 2015). The amplified products were sequenced by Sangon Biotechnology Co., Ltd. (Shanghai, China), followed by BLAST search (http://blast.ncbi.nlm.nih,gov/Blast.cgi) to identify the fungal genus. The phylogenetic analysis was performed with MEGA7 (https://www.megasoftware.net/).

After weighing 10 g of each rhizosphere soil sample, sterile water was used to prepare a soil dilution (Supplementary Figure S1E), which was then streaked to Nutrient Agar (NA) medium and cultured for 3 days. Single colonies with different colonial shapes and/or colors were isolated (Li et al., 2020), and evaluated by the confrontation culture method, the mycelial growth rate method and the spore germination method, following the established procedure (Li et al., 2020). The diameter of the fungal colony and the rate of fungal spore germination, with or without confrontation of screened bacterial strains, were measured, and used to calculate the inhibition rate using the following formula: mycelial growth inhibition (%) = (control colony diameter–treatment colony diameter)/(control colony diameter–inoculation plug diameter) ×100; spore germination inhibition (%) = (control spore germination rate–treatment spore germination rate)/control spore germination rate ×100.

All the selected biocontrol bacteria were identified to the species level based on their morphological characteristics, physiological and biochemical characteristics (Breed., 1984; Dong and Cai., 2001), and 16S rDNA sequence alignment. For PCR amplification of the bacterial 16S rDNA gene, universal primers 27F/1492R (Lane., 1991) were used. The amplified products were sequenced by Sangon Biotechnology Co., Ltd. (Shanghai, China) and subjected to BLAST search (http://blast.ncbi.nlm.nih,gov/Blast.cgi). Phylogenetic analysis was performed with MEGA7 (https://www.megasoftware.net/).

The screened biocontrol bacteria were cultured in 100 ml of liquid Nutrient Broth (NB) medium, at 37°C, and shaking at 180 r/min, for 24 h. The bacterial cells were centrifuged and adjusted OD₆₀₀ to 0.5 in sterile water. Such biocontrol bacteria suspension was evenly mixed with sterile soil and vermiculite in a ratio of 1:30, and placed in different U-PVC pipes, in which the healthy sugarcane tubers of equal size were transplanted, after disinfection (to avoid unequal residual microbes brought in by the tubers). The isolated pathogenic fungal strain (#3) that causes sugarcane root rot was cultured 100 ml of Potato Dextrose Broth (PDB) medium at 28°C and shanking at 180 r/min for 5 days, and diluted with sterile water to 1 × 10⁶ spores/ml. The fungal suspension was irrigated into the soil mixed with different biocontrol bacterial suspension, with the same proportion (Supplementary Figure S1F). Sugarcane grown in the soil containing no biocontrol bacteria served as the blank control. Three biological repeats were performed for each biocontrol bacteria treatment. After 45 days of sugarcane growth under different combination of fungal/bacterial inoculation, we dug out all the sugarcane roots, washed them with clean water, and air-dry, before imaging and measuring total root length, root surface area, and root volume using the software RhizoVision Explorer v1.0 (Seethepalli et al., 2021).

To assess the growth promoting effect, individual bacteria were inoculated to the sterile soils and vermiculite, following the same procedure as aforementioned. The sugarcane plants were allowed to grow in such bacterial inoculated soils for 45 days, before characterization of above-ground parts and roots. Qualitative determination of phosphorus-dissolving, nitrogen-fixing, siderophore and 3-indoleacetic acid capacities followed the established protocols (Syed-Ab-Rahman et al., 2018).

All statistics analyses were performed using SPSS v21 software (IBM, United States ), and analyzed by Duncan test of analysis of variance (ANOVA). p values <0.05 were considered statistically significant.

A longer root system, with a larger total surface area and volume, allows plants to absorb a wide range of soil water and nutrients, resulting in stronger plants (Deng et al., 2020). We arbitrarily determined sugarcane root rot severity, and categorized it as 4°: healthy (DHZA), mildly diseased (DHZB), moderately diseased (DHZC) and severely diseased (DHZD), based on the height and stoutness of above-ground part of sugarcane plantlets (Supplementary Figure S1B). “DHZ” is short for “Dahuizhong”, the cultivar name called by the local farmer. The total root length (Figure 1A), volume (Figure 1B) and surface area (Figure 1C) of sugarcane roots were significantly reduced, with increasing severity degrees. Compared with DHZA, the total root length of DHZB, DHZC or DHZD plantlets (adult-plants) decreased by 162.12, 292.46 and 593.95 cm, respectively; the total volume was decreased by 39.68, 45.15 and 55.88 cm³; and the total surface area was decreased by 258.02, 293.31 and 363.91 cm², respectively. Overall, we confirmed that the sugarcane root rot severity degree was highly correlated to the growth displayed by the above-ground part of sugarcane.

Alpha diversity showed no significant difference of rhizosphere soil bacterial community between different disease severity groups (p > 0.05, Figures 2A,B). Pearson correlation analysis showed that the total root surface area of DHZA was negatively correlated with the Chao1 index, but positively correlated with the Shannon index (Figure 2C). However, no significant correlation was found in other groups, between disease severity degrees and bacterial community diversity (Figure 2C). On the other hand, rhizosphere soil fungal community from DHZD group had the highest Chao1 and Shannon indices (Figures 2D,E). Particularly, the Chao1 index of rhizosphere soil fungal communities was significantly different among healthy and different diseased groups (p < 0.01, Figure 2D), while the Shannon index was not significantly different (p > 0.05, Figure 2E), indicating that the fungal community richness, but not the diversity, was correlating with disease severity. Pearson correlation analysis showed that the Chao1 index of the rhizosphere soil fungal community in DHZB group was negatively correlated with the total root length, and the Shannon index of the rhizosphere soil fungal community in DHZC group was positively correlated with the total root surface area (Figure 2F).

PCoA analysis of the bacterial communities based on the Abundance-based Jaccard distance revealed that soil microbiome formed three distinct clusters (Figure 3A). The DHZA samples were separated from DHZB samples along the first axis (PERMANOVA, p < 0.05), while separated from DHZC and DHZD samples, which were clustered together (PERMANOVA, p < 0.05), along the second axis (PERMANOVA, p < 0.05) (Figure 3A). The microbiome in DHZA samples was mainly distributed in the positive value area, while those from DHZB, DHZC and DHZD samples in the negative value area, of PCoA1 (Figure 3A). These results confirm that sugarcane root rot disease significantly affected the rhizosphere bacterial community composition.

The bacterial community compositions varied significantly in different severity groups (Figure 3B). Proteobacteria and Actinobacteriota were most abundant in all sampled groups, accounting for more than 65% of the identified phyla. The rest of phylum level in the rhizosphere soil samples showed significant difference (p < 0.05) among groups DHZA-DHZD, except for Proteobateria, based on Kruskal–Wallis H test analysis (Figure 3C). The relative abundance of Actinobacteriota was decreased by 16.10% and 18.48% respectively in DHZC and DHZD, while increased by 3.91% in DHZB, as compared to that in DHZA. The trend of Proteobacteria abundance was opposite to that of Actinobacteriota, as it increased by 51.93% and 51.59% in DHZC and DHZD respectively, and decreased by 3.32% in DHZB, compared to that of DHZA. The relative abundances of Acidobacteriota and Chloroflexi were significantly increased in DHZB, DHZC and DHZD groups in comparison to healthy (DHZA) samples. In contrast, the relative abundance of Firmicutes decreased with increasing disease severity (Figures 3B,C). We further illustrated the correlation between disease severity degree and rhizosphere bacterial community composition by Redundancy Analysis (RDA) analysis. As shown in Figure 3D, RDA1 and RDA2 explained 22.20% and 6.18% of the sample differences, respectively. The composition of bacterial community was indeed affected by disease severity degree, which was reflected by the total root length (R ² = 0.8828, p = 0.002), total root volume (R ² = 0.8687, p = 0.002) and total root surface area (R ² = 0.8378, p = 0.003). We speculate that the rhizosphere bacterial community composition was changed as a consequence of increased severity of disease.

Similar as the bacterial communities, the fungal community diversity also formed three distinct clusters, based on PCoA analysis, with the rhizosphere soil fungal species composition of DHZC and DHZD groups in a cluster, and DHZA and DHZB respectively in a cluster (Figure 4A). The rhizosphere soil fungal microbiome of DHZA group was mainly distributed in the positive value area, while that of DHZB, DHZC and DHZD groups in the negative value area, of PCoA1 (Figure 4A). These results reveal significant change in the fungal species composition potentially resulted from disease progression.

We further assessed fungal relative abundances at the genus level, and found that Fusarium, Talaromyces, Neocosmospora, Trichoderma and Gibberella were commonly present in all the tested groups (Figure 4B). Furthermore, significant differences in the relative abundance of each genus were found among different groups (Figure 4C), supporting a correlation between fungal community composition and disease severity degree. The relative abundances of Fusarium, Talaromyces, and Neocosmospora in DHZA rhizosphere soil samples were 13.77%, 10.33%, and 9.56%, respectively, and totaled as 33.66% out of all identified fungal genera. Surprisingly, the totaled relative abundances of these genera decreased in DHZB, DHZC and DHZD rhizosphere soil samples, as 19.21%, 14.95% and 10.71% respectively. Notably, the relative abundances of Fusarium, the genus potentially containing the pathogen of sugarcane root rot as reported (Wang et al., 2018; Ren et al., 2021), was 13.77%, 9.85%, 4.41%, and 6.47% in group DHZA to DHZD, showing a negative correlation with increased disease severity (Figures 4B,C). RDA analysis confirmed that the composition of fungal community was also affected by disease severity degree, which was reflected by the total root length (R ² = 0.6726, p = 0.001), total root volume (R ² = 0.7385, p = 0.010) and total root surface area (R ² = 0.8319, p = 0.001) (Figure 4D).

Phenotypic prediction of the rhizosphere soil bacterial microbiome was performed using BugBase database, and significant differences among DHZA to DHZD groups was found in terms of anaerobic, facultatively anaerobic, forming biofilms, potentially pathogenic, aerobic, and containing mobile elements, but not in stress tolerant, Gram positive and Gram negative (Supplementary Figure SA). It was worth noting that the predicted proportion of facultatively anaerobic and forming biofilms in rhizosphere soil bacteria was highest in the healthy (DHZA) group, as compared to diseased groups (Supplementary Figures S2B,C).

On the other hand, functional prediction of the rhizosphere soil fungal microbiome was performed using Fungi Functional Guild (FUNGuild) database. Saprotroph was the most common tropic type in all tested rhizosphere soil samples, with an average proportion of 46%, and the pathotroph in the second place, with an average proportion of 18.5%. It was worth noting that the proportion of saprotroph was higher, while pathotroph was lower, in DHZA group in comparison to DHZD group (Supplementary Figure S2D). Overall, from a biological point-of-view, we infer that decreased proportion of bacteria with facultatively anaerobic and forming biofilms functions, and of saprophytic fungi, accompanied with increased proportion of pathotropic fungi, could be viewed as an indicator of sugarcane root rot.

A co-occurrence network was constructed for a better understanding of the relationship between the fungal pathogen Fusarium species and the bacterial community in the rhizosphere soil. A connection with a magnitude >0.5 (positive correlation) or < -0.5 (negative correlation) and statistically significant (p < 0.05) was defined as correlation. The drawn network diagram was represented by different OTUs with positive and negative interactions, where the solid line for positive interaction indicates the change along the same trend among species, and the dashed line for negative interaction indicates the change along the opposite trend among species. We found correlations between different Fusarium groups (Fusarium 1, Fusarium 2 and Fusarium 3) and rhizosphere soil bacterial communities, among which Burkholderia had the largest number of species, with a total of 10, and Bacillus in the second place, with a total of 9 (Figure 5A). Correlation between three Fusarium groups also existed (Figure 5A). Notably, as the disease severity gradually increased, the cumulative relative abundances of Fusarium, Burkholderia and Bacillus species also accordingly changed, displaying an increase or decrease trend (Figure 5B). The DHZB group contained the most Bacillus and Burkholderia species, while the DHZA group contained the most Fusarium species in the rhizosphere soil (Figure 5B). Overall, our data demonstrate that a correlation between Fusarium species and rhizosphere soil bacterial communities, with Burkholderia and Bacillus species being more strongly correlated. Also, combined with the trend of cumulative relative abundance, it was speculated that some Burkholderia and Bacillus species may act as new antagonistic bacteria to inhibit the growth and reproduction of (pathogenic) Fusarium species. We infer that the plants infected by Fusarium species may tend to recruit beneficial microorganisms, including Burkholderia and Bacillus species, in order to suppress pathogenic microorganisms and/or boost plant immunity.

Three fungal strain was isolated from the diseased samples, and named as GXUF-3. When grown on Potato Dextrose Agar (PDA) medium for 5 days, GXUF-3 formed luxuriant and white velvet-like mycelial colony, which was white when viewed from backside (Supplementary Figure S3A). CXUF-3 produced abundant large conidia of sickle- or spindle-shape, with 0–5 septum per conidium, when cultured in liquid carboxymethyl cellulose (CMC) medium (Supplementary Figure S3B). BLAST search using the PCR amplified Elongation factor 1-alpha (EF-1α) region of GXUF-3 showed a 100% homology with Fusarium commune (MF150040.1). The phylogenetic analysis based on the amplified EF-1α region showed that the GXUF-3 strain was clustered with several F. commune strains, and relatively far away from other Fusarium species (Supplementary Figure S3C). Overall, the GXUF-3 strain was identified as F. commune. To verify whether GXUF-3 strain was the causal pathogen of sugarcane root rot, we inoculated it in the sterilized soils that was used for planting sugarcane. After 45 days growth we observed that the infected sugarcane roots were seriously hindered, gradually turning brown, soft and rotten, and meanwhile the above-ground part was dwarfed, and the leaves turned yellow (Supplementary Figure S3D). In contrast, the plants inoculated with sterile water (untreated control) grew normally and showed no disease symptoms (Supplementary Figure S3D). The re-isolated fungal strain from the infected sugarcane displayed similar colonial morphology as GXUF-3 (Supplementary Figure S3D), confirming that GXUF-3 was the causal pathogen of sugarcane root rot.

A total of 273 bacterial strains were isolated from the tested rhizosphere soil samples, among which 7 strains with potential biocontrol effects were obtained by confrontation culture with F. commune strain GXUF-3. We named these seven biocontrol strains as GXUB-1, GXUB-2, GXUB-3, GXUB-4, GXUL-1, GXUL-2, GXUJ-1 (Supplementary Figure S4A). GXUL-1 strain displayed the weakest, while GXUB-1 strain with the strongest, inhibitory effect on fungal mycelial growth (Supplementary Figures S4A,B). Furthermore, we found that mycelial growth and spore germination of GXUF-3 was suppressed by different concentration of biocontrol bacterial fermentation broth (Supplementary Figures S4C,D), suggesting that the tested bacterial strains produced anti-fungus compound(s). We also noticed a significant difference in mycelial growth and spore germination inhibition rates among different bacteria strains (Supplementary Figures S4C, D; Supplementary Table S1). Overall, we obtained seven bacterial strains with varied inhibitory effect on mycelial growth and spore germination of the fungal pathogen causing sugarcane root rot.

Based on morphological (Figure 6A), physiological and biochemical characterization (Supplementary Table S2), and molecular identification (Figure 6B), we identified GXUB-1 and GXUB-3 as B. velezensis, GXUB-2 as B. siamensis, and GXUB-4 as B. amyloliquefaciens; GXUL-1 and GXUL-2 as B. cepacia, and GXUJ-1 as A. pokkalii.

To test control effect of these seven strains on sugarcane root rot, we inoculated them individually, and respectively with fungal pathogen, to the sterile soils, on which sugarcane was planted. After 45 days growth, the total root length, total root surface area and total root volume of sugarcane were measured. Obvious changes of root phenotypes were seen in the biocontrol bacteria treated seedling, as compared to the control group which was inoculated with fungal pathogen alone (Figures 7A–C), confirming that all the tested biocontrol bacteria were effective in suppressing sugarcane root rot, among which B. siamensis GXUB-2 strain had the best control effect (Figures 7A–C).

To test the growth-promoting property of these seven strains, sugarcane was planted in bacteria inoculated soils, without fungal pathogen, for 45 days, and the root phenotypes were characterized. Obvious differences were visualized in the root phenotypes of sugarcane under different bacterial inoculation (Figures 7D–F). Surprisingly, inoculation with GXUB-3, GXUB-4 or GXDJ-1 alone suppressed growth of roots part (Figures 7D–F), while in presence of fungal pathogen, all these three strains displayed disease suppressing property (Figures 7A–C). In contrast, inoculation with the other four strains caused increase in the total root length and total root surface ar.

The ability of nitrogen fixation, phosphorus solubilization, phytohormea (Figures 7D–F). GXUB-1 and GXUL-1 caused a slight decrease, while GXUB-2 and GXUL-2 caused increase, in the total root volume (Figures 7D–F). In summary, we found that B. velezensis GXUB-3 strain, B. amyloliquefaciens GXUB-4 and A. pokkalii GXUJ-1 strain had a growth-inhibitory effect on the sugarcane roots, and the other four strains with growth-promoting property, among which B. siamensis GXUB-2 and B. cepacia GXUL-2 strains behaved better (Figures 7D–F)one(s) production, and siderophore production were assessed in these seven strains. Both GXUL-1 and GXUL-2 strains were able to produce the phytohormone indole-3-acetic acid (IAA), siderophores, and able to fix nitrogen and solubilize phosphorus (Supplementary Table S3). GXUB-1 displayed only siderophores producing ability (Supplementary Table S3). The three strains with inhibitory effect on sugarcane growth, GXUB-3, GXUB-4 and GXUJ-1, possessed none of the tested activities. However, GXUB-2 strain with the best disease suppression effect and an ideal growth-promoting effect, also did not possess four tested properties (Supplementary Table S3), suggesting that its biocontrol and growth-promoting mechanism may lie in other untested aspects.