Vol.: (0123456789) 1 3 Plant Soil (2024) 505:779–793 https://doi.org/10.1007/s11104-024-06709-4 RESEARCH ARTICLE The interaction between Fusarium oxysporum f. sp. cubense tropical race 4 and soil properties in banana plantations in Southwest China Wenlong Zhang · Tingting Bai · Arslan Jamil · Huacai Fan · Xundong Li · Si‑Jun Zheng · Shengtao Xu Received: 4 March 2024 / Accepted: 28 April 2024 / Published online: 18 May 2024 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024 Abstract  Aims  Banana Fusarium wilt (Fusarium oxysporum f. sp. cubense tropical race 4) is a typical destruc- tive soil-borne disease, which was the main limiting factor for the sustainable development of the banana industry worldwide. In banana production, soil physi- ochemical properties and soil microbiome were effec- tively affected the occurrence and spread of Fusar- ium wilt. However, there is still a lack of systematic research, particularly in exploring the correlation between the occurrence of banana Fusarium wilt and soil properties across various climates and soil types. Methods  In this study we investigated the soil phys- icochemical properties, bacterial and fungal commu- nity composition, and pathogenic fungal abundance in 140 banana plantations which were affected by banana Fusarium wilt in Yunnan Province, China. Results  The results showed that the abundance of soil-borne pathogenic fungi was positively correlated with total phosphorus, total nitrogen, organic matter, urease activity, annual precipitation, and the alpha diversity of bacterial and fungal communities. In contrast, it showed a significant negative correlation with the annual mean temperature. As the abundance of pathogen increased, numerous potential disease- suppressive bacterial genera (such as Rhodanobacter, Gemmatimonas, Novosphingobium) and soil-borne pathogenic fungal genera (such as Plectosphaerella, Nigrospora, Cyphellophora) also increased, and the co-occurrence network showed a higher modulariza- tion index. Conclusions  The results enhance the understand- ing of the patterns of soil-borne pathogenic fungal population dynamics in banana plantations, which would provide evidence and guidance for reducing pathogenic fungal abundance and selecting beneficial microorganisms in banana production. Furthermore, this would provide a theoretical basis for sustainable prevention and control of banana wilt disease. Responsible Editor: Luz E. Bashan. Wenlong Zhang and Tingting Bai contributed equally to this work. Supplementary Information  The online version contains supplementary material available at https://​doi.​ org/​10.​1007/​s11104-​024-​06709-4. W. Zhang · T. Bai · A. Jamil · H. Fan · X. Li · S.-J. Zheng (*) · S. Xu (*)  Yunnan Key Laboratory of Green Prevention and Control of Agricultural Transboundary Pests, Agricultural Environment and Resources Institute, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan 650205, China e-mail: sijunzheng63@163.com S. Xu  e-mail: xushengtao.14@hotmail.com S.-J. Zheng  Bioversity International, Kunming, Yunnan 650205, China http://crossmark.crossref.org/dialog/?doi=10.1007/s11104-024-06709-4&domain=pdf https://doi.org/10.1007/s11104-024-06709-4 https://doi.org/10.1007/s11104-024-06709-4 780 Plant Soil (2024) 505:779–793 1 3 Vol:. (1234567890) Keywords  Banana Fusarium wilt · Physiochemical properties · Soil microbial communities · Pathogenic fungal abundance Introduction The relentless onslaught of soil-borne fungal patho- gens in modern agriculture poses a staggering threat, causing an estimated 15% annual yield reduction across crops (Newbery et  al. 2016). Concurrently, modern and intensive farming practices are promot- ing the proliferation of plant pathogens, particularly aggressive soil borne fungi (Hartmann and Six 2023). Current chemical fungicides used to combat these pathogens have shown limited efficacy and pose sig- nificant environmental risks (Delgado-Baquerizo et  al. 2020). In addition to traditional epidemiologi- cal management measures, alternative soil manage- ment practices are increasingly becoming a focus of research, such as soil fertility regulation and biologi- cal control (Katan 2017). The growth and development of soil-borne patho- gens are inevitably influenced by the entire soil eco- system (Bakker et al. 2020). Soil fertility is one of the key driving factors in the occurrence and develop- ment of soil-borne diseases (Panth et al. 2020). Addi- tionally, soil characteristics have a significant impact in influencing the structure and activity of microbial communities in the soil, modulating plant defense mechanisms and thus regulating pathogen popula- tions (Xiong et al. 2017). The coordinated evolution of plants and their root microbiota assists plants in better adapting to their environment, such as nutri- ent cycling, growth stimulation, and resilience to both environmental stresses and biological threats (Trivedi et al. 2020). The microbiome can protect plants from pathogen invasion through various mechanisms, such as producing inhibitory substances or occupying eco- logical niches that directly suppress the growth of pathogen, and/or inducing plant immune responses to enhance the plant’s resistance to pathogenic micro- organisms (Li et al. 2021a, b). Conversely, pathogens can also exploit soil microorganisms to enhance their virulence (Snelders et al. 2020). Therefore, this recip- rocal interaction between soil-borne pathogens and the plant microbiome is of paramount importance in understanding the dynamics of soil-borne diseases. Banana (Musa spp.) as the world’s top traded fruit and the fourth-largest staple crop hold extensive economic and nutritional values (Evans et  al. 2020). However, Fusarium wilt of Banana (FWB) caused by Fusarium oxysporum f. sp. cubense (Foc) infection poses a signifi- cant threat to the sustainable development of the banana industry (Pegg et al. 2019). Commercial banana varieties are unable to withstand the infect of Fusarium oxyspo- rum f. sp. cubense tropical race 4 (Foc TR4), which has now reached -producing regions globally (Zuo et  al. 2018). Due to the limited banana genetic diversity within the banana crop, breeding disease-resistant cultivars are a formidable challenge. In addition to optimizing field management practices, the use of biological control to regulate soil microbial communities for reducing the incidence of wilt disease is emerging as a future develop- ment trend (Siamak and Zheng 2018). Improving soil properties had been proven to effec- tively enhance disease control (Ghorbani et  al. 2008). Despite previous research uncovering an association between the occurrence of FWB and the soil micro- biome (Fu et al. 2017; Hong et al. 2020; Kaushal et al. 2020), the specific interactions between soil properties, Foc TR4, and soil microbiome at different pathogen lev- els remain elusive. To address this gap in knowledge, we conducted a comprehensive study involving the collec- tion of soil samples from 140 banana plantations in Yun- nan province affected by FWB. Our investigation delves into assessing the physicochemical properties, enzyme activity, bacterial and fungal community composition, and the abundance of Foc TR4. The hypothesis of this study was that soil Foc TR4 abundance was highly cor- related with the soil properties, particularly with the soil community composition. The study aims to unravel the key drivers of Foc TR4 abundance in banana plantation soils, explore the impact of pathogen abundance on the soil microbial community, and identify microbial taxa associated with the pathogen. Through these efforts, we aim to contribute valuable insights into managing patho- genic fungal abundance and promoting beneficial micro- organisms in banana production. Materials and methods Sampling points and soil sampling Through preliminary on-site inspections in Yun- nan Province, the following sampling areas were 781Plant Soil (2024) 505:779–793 1 3 Vol.: (0123456789) identified. The 21 counties and cities were selected as representatives of different climate and soil types (Fig.  S1). For each county or city, 8–10 sampling points were established within typical banana orchards exhibiting symptoms of Fusarium wilt. Soil samples were collected from the rhizosphere. We obtained cli- mate data including annual average temperature of all sampling points from the Worldclim database (https://​ www.​world​clim.​org). The basic information such as geographical location and climate characteristics of each sampling point was shown in Table S1. Soil samples were collected from five parts around one same plant at a depth of 0–20 cm, and the pro- tocol was according to Lundberg et al. (2012). After mixing, the soil samples were placed in sterile, self- sealing bags and labeled. A portion of the samples was subjected to -80  °C freeze-drying to remove moisture, and then stored at -80  °C for subsequent determination of soil microbial and pathogenic con- tent. Another portion was air-dried naturally, and reserved for the analysis of soil physicochemical indi- cators and enzyme activity after sieving through a 75-mesh sieve. Determination of soil nutrients and enzyme activity Soil pH was determined using an acidity meter (PHSJ-4  F, Shanghai INESA Scientific Instrument Co., Ltd, Shanghai, China). The soil chemical meas- urement analyses followed the standard procedures according to Page et  al. (1982) and Klute and  Page (1986). The total soluble salt content was measured by the conductivity method. Organic matter content was determined by the potassium dichromate titra- tion method. Alkaline nitrogen content was deter- mined using the sodium hydroxide-alkali diffusion method. Available phosphorus content was meas- ured by the sodium bicarbonate-molybdenum anti- mony anti-absorption spectrophotometry method. Available potassium content was determined by the ammonium acetate-flame photometry method. Total nitrogen in the soil were determined using a carbon- nitrogen elemental analyzer (VARIO MAX C/N, Ger- many) through dry combustion. Microbial carbon and nitrogen content were determined using the chloro- form fumigation extraction method with K2SO4. Soil urease activity was determined by the indophenol blue colorimetric method. Soil catalase activity was determined by the potassium permanganate titration method. Soil sucrase activity was determined using the 3, 5-dinitrosalicylic acid colorimetric method. Alkaline phosphatase activity was determined by the disodium phenylphosphate colorimetric method. DNA extraction and PCR amplification Genomic DNA was extracted from soil samples using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.), following the manufacturer’s protocols. The DNA quality and concentration were assessed through 1.0% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Scientific, United States) and stored at -80  °C until further use. For bacterial 16  S rRNA gene amplifi- cation of the V3-V4 hypervariable region, primer pairs 338 F (5’-ACT​CCT​ACG​GGA​GGC​AGC​AG-3’) and 806R (5’-GGA​CTA​CHVGGG​TWT​CTAAT- 3’) were employed. Additionally, the ITS1 region of fungal ITS rRNA genes was targeted using the for- ward primer ITS5F (5’- GGA​AGT​AAA​AGT​CGT​ AAC​AAGG-3’) and reverse primer ITS1R (5’- GCT​ GCG​TTC​TTC​ATC​GAT​GC-3’) with a T100 Ther- mal Cycler PCR thermocycler (BIO-RAD, USA). The PCR reaction mixture included 4 µL 5 × Fast Pfu buffer, 2 µL 2.5 mM dNTPs, 0.8 µL each primer (5 µM), 0.4 µL Fast Pfu polymerase, 10 ng of template DNA, and ddH2O to a final volume of 20 µL. PCR cycling conditions consisted of initial denaturation at 95 °C for 3 min, followed by 27 cycles of denaturing at 95  °C for 30  s, annealing at 55  °C for 30  s, and extension at 72 °C for 45 s. A single extension step at 72 °C for 10 min concluded the process, followed by cooling to 4 °C. The PCR product was purified from a 2% agarose gel using the PCR Clean-Up Kit (YuHua, Shanghai, China) according to the manufacturer’s instructions. Quantification was performed using Qubit 4.0 (Thermo Fisher Scientific, USA). Quantitative real‑time PCR analyses Primers FocSc-1(5’-CAG​GGG​ATG​TAT​GAG​GAG​ GCT​AGG​CTA-3’) and FocSc-2(5’-GTG​ACA​GCG​ TCG​TCT​AGT​TCC​TTG​GAG-3’) were used to quan- tify the abundances of Foc TR4 by quantitative real- time PCR (qPCR). The qPCR amplification was per- formed using Takara SYBR Premix Ex TaqTM (Tli RNaseH Plus) kit (Code No. RR820). The establish- ment of a standard curve utilized the recombinant https://www.worldclim.org https://www.worldclim.org 782 Plant Soil (2024) 505:779–793 1 3 Vol:. (1234567890) plasmid pMD18-T-158, containing a 158 bp fragment. The concentration of the extracted plasmid DNA was determined and then converted into copy numbers using the formula: Copy number = (6.02 × 1023 cop- ies mol−1 × plasmid concentration g µL−1) / MW g mol−1. Here, MW (average molecular weight of dou- ble-stranded DNA) = base pairs × 660 / base pair. Six concentration gradients (108, 107, 106, 105, 104, 103 copy number µL−1) were prepared as standard sam- ples for constructing the standard curve. The standard curve required R² > 0.99 and 90 < Eff % < 110. The pathogen content in the tested samples was calculated based on instrument detection results, the mass of the sample used for DNA extraction, the total amount of extracted DNA, and the template amount used in the qPCR reaction. The pathogen content was expressed as copy number per gram of soil. Illumina sequencing and data processing The purified amplicons, pooled equimolarly, underwent paired-end sequencing on an Illumina PE250 platform (Illumina, San Diego, USA) by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China), following standard protocols. Raw sequencing reads were submit- ted to the NCBI Sequence Read Archive (SRA) data- base (16 S: PRJNA946914, ITS: SRPPRJNA947073). Raw FASTQ files were demultiplexed using an in- house Perl script, then quality-filtered with fastp version 0.19.6 (Chen et  al. 2018), and merged using FLASH version 1.2.7 (Magoč and Salzberg 2011) based on the following criteria: (1) reads with an average qual- ity score < 20 over a 50 bp sliding window were trun- cated, and reads shorter than 50  bp were discarded; reads with ambiguous characters were also removed; (2) only overlapping sequences longer than 10 bp were assembled with a maximum mismatch ratio of 0.2 in the overlap region; unassembled reads were discarded; (3) samples were differentiated by barcode and prim- ers, and sequence direction was adjusted with exact barcode matching and a 2-nucleotide mismatch allow- ance in primer matching. The optimized sequences were clustered into operational taxonomic units (OTUs) at a 97% sequence similarity level using UPARSE 7.1 (Stackebrandt and Goebel 1994; Edgar 2013). The most abundant sequence in each OTU was selected as a rep- resentative sequence. The OTU table was manually filtered, removing chloroplast sequences from all sam- ples. To minimize sequencing depth effects on alpha and beta diversity measures, the number of 16 S rRNA gene sequences per sample was rarefied to 20,000. The taxonomy of each OTU representative sequence was determined using the RDP Classifier version 2.2 (Wang et al. 2007) against the 16 S rRNA gene database (e.g., Silva v138) with a confidence threshold of 0.7. Statistical analysis Correlation between the abundance of Foc TR4 and soil properties, enzyme activity, and climate calcu- lated by Spearman. Bioinformatic analysis of the soil microbiome was carried out using the Majorbio Cloud platform (https://​cloud.​major​bio.​com) and R 4.2.3. Alpha diversity includes observed OTUs and Shannon index, and correlation analysis with Foc TR4 was based on Spearman. The similarity among the microbial communities in different samples was determined by principal coordinate analysis (PCoA) based on Bray-curtis dissimilarity using Vegan v2.5-3 package, and correlation analysis with environmen- tal factors calculated through Mantel’s test (Dixon 2003). Constructing a core Microbial Evolution Tree Using NJ Method by MEGA 7 and optimized with iTOL (https://​itol.​embl.​de/). The co-occurrence net- work was constructed using vegan, psych (Revelle and Revelle, 2015), and igraph (Han et  al. 2010) packages and Gephi 0.9.2, and calculated the correla- tion between each module and the abundance of Foc TR4. Each node represents an OTU, and each edge represents a significant correlation between node (Spearman, R > 0.6, P < 0.05) (De Vries et al. 2018). Calculating the module index through Gephi 0.9.2 was performed. Results Correlation between soil characteristics and Foc TR4 abundance In 140 soil samples collected from diseased banana plantations, the average Foc TR4 gene copy number was 3.35 × 106 copies g−1, ranging from 1.53 × 107 to 1.32 × 105 copies g−1. The average pH was 5.38, with 111 samples having a pH lower than 6, indicat- ing an overall acidic nature. Organic matter content was 26.50  g kg−1. Total nitrogen was 1.48  g kg−1, while available nitrogen was 115.91  mg kg−1. Total https://cloud.majorbio.com https://itol.embl.de/ 783Plant Soil (2024) 505:779–793 1 3 Vol.: (0123456789) phosphorus measured 4.04  g kg−1, and available phosphorus was 91.14 mg kg−1. Total potassium was 34.39 g kg−1, and available potassium was 493.87 mg kg−1. Enzyme activity measurements revealed cata- lase at 44.63 g−1 soil 24 h−1, phosphatase at 14.83 g−1 soil 24  h−1, invertase at 29.86  g−1 soil 24  h−1, and urease at 273.56  g−1 soil 24  h−1. The abundance of Foc TR4 showed a significant positive correlation with total phosphorus (R = 0.32, P < 0.01), total nitro- gen (R = 0.30, P < 0.05), organic matter (R = 0.18, P < 0.05), urease (R = 0.18, P < 0.05), and annual pre- cipitation (R = 0.34, P < 0.01). In contrast, it exhibited a significant negative correlation with mean annual temperature (R = − 0.25, P < 0.01) (Fig. 1). Community diversity and composition and Foc TR4 correlations The abundance of Foc TR4 was significantly posi- tively correlated with the diversity (R = 0.18, p < 0.05) and richness (R = 0.22, p < 0.01) of bacterial com- munity, but for fungal communities, only richness shows a significant positive correlation (R = 0.21, p < 0.05) (Fig.  2). Furthermore, both bacterial com- munity diversity and richness are significantly associ- ated with the composition of bacterial (Shannon: R = -0.72, Obs: R = 0.48, p < 0.001) and fungal (Shannon: R = 0.42, Obs: R = 0.22, p < 0.01) communities, while fungal community species richness was significantly associated with the composition of both bacterial (R = 0.28, p < 0.001) and fungal (R = 0.24, p < 0 0.01) communities (Fig S2). The OTU level, The mental’s test indicated that the composition of bacterial and fungal communi- ties was significantly correlated with pH (R = 0.32, p < 0.001 for bacteria, R = 0.34, p < 0.001 for fungi), CAT (R = 0.25, p < 0.001, R = 0.26, p < 0.001), INV (R = 0.13, p < 0.01, R = 0.14, p < 0.01), AN (R = 0.13, p < 0.01, R = 0.10, p < 0.01), and AP (R = 0.13, p < 0.01, R = 0.17, p < 0.01). The composition of bacterial communities at the order level was signifi- cantly correlated with the abundance of pathogenic bacteria (R = 0.06, p < 0.05). At the phylum level, the composition of fungal communities was significantly correlated with the abundance of pathogenic fungal (R = 0.17, p < 0.001), while at other taxonomic levels, there was no correlation between the composition of bacterial and fungal communities and the abundance of pathogenic bacteria (Table 1). Fig. 1   The correlation between environmental factors and Foc TR4 abundance based on Spearman (**P < 0.01, *P < 0.05). TP: total phosphorus; APR: annual precipitation; TN: total nitrogen; AMT: annual mean temperature; SOM: soil organic matter; URE: urease. Green represents positive correlation, red represents negative correlation Fig. 2   Correlation between Shannon index (a) and observed OTUs index (b) of bacterial and fungal communities and abundance of Foc TR4 based on Spearman 784 Plant Soil (2024) 505:779–793 1 3 Vol:. (1234567890) Microbial taxa associated with pathogen At the phylum level, Proteobacteria, Bacteroidota, Cyanobacteria, Rozellomycota, and Glomeromycota are significantly positively correlated with the abun- dance of Foc TR4, while Firmicutes and WPS-2 are significantly negatively correlated with pathogenic bacteria abundance (Table  2). At the genus level, Rhodanobacter, Gemmatimonas, Novosphingobium, Allorhizobium-Neorhizobium-Pararhizobium-Rhizo- bium, Solirubrobacter, Ilumatobacter, Reyranella, Mucilaginibacter, Hyphomicrobium, Actinoplanes, Rhodoplanes, SWB02, Flavobacterium, Devosia, Dokdonella, Trichoderma, Purpureocillium and Metarhiziu was positively correlated with the abun- dance of FocTR4, and Bacillus is negatively cor- related with the abundance of pathogenic bacteria. These genera are known to possess high biocontrol potential (Table 3). We constructed a phylogenetic tree of the core bacterial OTUs found in all samples and correlated them with environmental factors. Among the core microbiota, there were 14 Actinobacteria, 13 Proteo- bacteria, 3 Firmicutes, and 1 each of Nitrospirota, Acidobacteriota, and Myxococcota. Specifically, OTU23598 (Pedomicrobium, R = 0.257), OTU23485 (Sphingomonas, R = 0.175), OTU24018 (Devosia, R = 0.255), and OTU507 (Mycobacterium, R = 0.257) Table 1   The correlation between environmental factors and microbial community composition based on Mental’s test CAT​ Catalase, PHO Phosphatase, INV Invertase, URE Urease, SOM soil organic matter, TN total nitrogen, AN available nitrogen, TP total phosphorus, AP available phosphorus, TK total potassium, AK available potassium, Foc TR4 the abundance of Foc TR4 *Significant at the 0.05 probability level **Significant at the 0.01 probability level ***Significant at the 0.001 probability level Community Factor Phylum Class Order Family Genus OTU Bacterial CAT​ 0.073 0.299*** 0.295*** 0.32*** 0.311*** 0.251*** PHO 0.078* 0.06 0.063 0.077* 0.08* 0.065 INV 0.007 0.175*** 0.182*** 0.196*** 0.189*** 0.132** URE 0.006 0.076 0.085* 0.089* 0.085* 0.048 pH 0.076 0.374*** 0.394*** 0.424*** 0.411*** 0.317*** SOM 0.051 0.172** 0.176** 0.187** 0.182** 0.16** TN 0.027 0.133* 0.127** 0.134** 0.126* 0.087 AN 0.055 0.097* 0.109** 0.119*** 0.128** 0.132** TP -0.007 0.037 0.04 0.046 0.07 0.089 AP -0.026 0.13* 0.146** 0.162** 0.159** 0.133** TK -0.029 -0.029 -0.025 -0.012 0.004 0.028 AK 0.078 -0.027 -0.009 -0.002 0.001 -0.025 FocTR4 0.034 0.059* 0.032 0.021 0.012 0.016 AMT -0.043 0.007 0.004 0.01 0.006 0.03 APR 0.043 -0.056 -0.076 -0.081 -0.089 -0.087 Fungal CAT​ 0.061 0.066 0.101* 0.099* 0.112** 0.262*** PHO 0.045 -0.06 -0.078 -0.069 -0.069 0.089* INV 0.03 -0.021 -0.008 -0.002 0.008 0.137** URE -0.006 0.093* 0.085* 0.069 0.063 0.055 pH 0.078* 0.04 0.044 0.055 0.066 0.339*** SOM 0.034 -0.012 0.001 0.019 -0.004 0.142* TN 0.042 -0.05 -0.038 -0.032 -0.05 0.076 AN 0.064* 0.101** 0.109** 0.132*** 0.125** 0.103** TP 0.047 0.154** 0.15** 0.177** 0.192** 0.088 AP 0.006 0.056 0.092 0.108* 0.109* 0.165** TK 0.019 0.175** 0.201*** 0.238*** 0.257*** 0.053 AK 0.004 -0.056 -0.055 -0.044 -0.051 0.011 FocTR4 0.171*** -0.019 0.009 0.016 0.016 0.008 AMT 0.095* -0.057 -0.062 -0.053 -0.06 0.018 APR 0.061* -0.088 -0.054 -0.035 -0.037 -0.107 785Plant Soil (2024) 505:779–793 1 3 Vol.: (0123456789) were significantly (P < 0.05) positively correlated with the abundance of pathogenic bacteria. OTU23514 (Unclass_Xanthobacteraceae, R = − 0.175), OTU23930 (Bacillus, R = − 0.216), OTU14931 (Candidatus_Soli- bacter, R = − 0.197), OTU23396 (unclass_Streptomyc- etaceae, R = − 0.177), and OTU14049 (Mycobacterium, R = − 0.225) were significantly (P < 0.05) negatively correlated with the abundance of FocTR4. Among the core fungal OTUs, only OTU11728 (Fusarium) and OTU12062 (Trichoderma) were identified, and OTU12062 showed a significant negative correlation with the abundance of pathogenic bacteria (R = − 0.270, P < 0.01) (Fig. 3). Network analysis We constructed cross - kingdoms networks for bac- teria and fungi (Fig. 4a), in which the module 5 was significantly positively correlated with the abun- dance of Foc TR4 (Fig.  4b, Table  S2). Exception OUT23378 (unclass_Chloroplast), all other members in this module were fungi. We constructed correlation networks based on different Foc TR4 abundances. The results indicate that as the Foc TR4 abundance in the soil increases, the modularity index of the cross - kingdoms network significantly increases (R = 0.758, P < 0.05), suggesting an improvement in network sta- bility (Fig.  4c). There was no significant correlation observed between the average degree and the abun- dance of Foc TR4, and there was no correlation with the complexity of the cross-domain network. Table 2   The correlation between microbial taxa and Foc TR4 in the level of phylum by Spearman *Significant at the 0.05 probability level **Significant at the 0.01 probability level ***Significant at the 0.001 probability level R correlation coefficient with Foc TR4, RA Relative abundance of Foc TR4 Phylum R RA Proteobacteria 0.27** 31.90% Bacteroidota 0.39*** 4.19% Firmicutes -0.27** 2.52% WPS-2 -0.18* 0.92% Cyanobacteria 0.21* 0.52% Rozellomycota 0.22** 0.41% Glomeromycota 0.17* 0.19% Table 3   The correlation between microbial taxa and Foc TR4 in the level of genus by Spearman *Significant at the 0.05 probability level **Significant at the 0.01 probability level ***Significant at the 0.001 probability level R correlation coefficient with Foc TR4, RA Relative abundance of Foc TR4 Phylum Genus R RA Proteobacteria Rhodanobacter 0.17* 4.88% Proteobacteria Dongia 0.17** 4.23% Gemmatimonadota Gemmatimonas 0.17** 3.99% Actinobacteriota Nakamurella 0.18** 3.85% Actinobacteriota Galbitalea 0.18** 3.80% Bacteroidota Puia 0.18** 3.51% Proteobacteria Novosphingobium 0.19** 2.62% Proteobacteria Allorhizobium- Neorhizobium-Para- rhizobium-Rhizobium 0.20** 2.05% Actinobacteriota Solirubrobacter 0.20** 2.01% Actinobacteriota Ilumatobacter 0.20** 1.81% Proteobacteria Reyranella 0.20** 1.71% Bacteroidota Mucilaginibacter 0.21** 1.12% Proteobacteria Hyphomicrobium 0.22** 0.82% Actinobacteriota Actinoplanes 0.23** 0.64% Proteobacteria Ellin6067 0.23** 0.54% Proteobacteria Rhodoplanes 0.24** 0.49% Proteobacteria SWB02 0.24** 0.43% Bacteroidota Flavobacterium 0.24** 0.41% Bacteroidota Edaphobaculum 0.24** 0.38% Acidobacteriota Candidatus_Koribacter -0.25** 0.29% Proteobacteria Devosia 0.26** 0.17% Firmicutes Bacillus -0.26* 0.17% Proteobacteria Dokdonella 0.26** 0.16% Proteobacteria Castellaniella 0.26** 0.16% Ascomycota Trichoderma 0.21* 5.71% Ascomycota Plectosphaerella 0.23** 1.43% Ascomycota Nigrospora 0.29*** 0.74% Ascomycota Cyphellophora 0.19* 0.51% Ascomycota Codinaea 0.18* 0.29% Ascomycota Microascus 0.22** 0.27% Ascomycota Purpureocillium 0.28*** 0.20% Ascomycota Dactylonectria 0.19* 0.18% Ascomycota Scytalidium 0.22** 0.16% Ascomycota Metarhizium 0.20* 0.14% Ascomycota Pyrenochaetopsis 0.25** 0.14% Ascomycota Sodiomyces 0.22* 0.13% Ascomycota Acrocalymma 0.2* 0.13% Ascomycota Immersiella 0.30*** 0.13% Ascomycota Apiosordaria 0.18* 0.12% 786 Plant Soil (2024) 505:779–793 1 3 Vol:. (1234567890) Discussion In this study, we attempted to investigate the abun- dance of Foc TR4 in banana plantations and their relationship with soil and environmental factors, as well as the microbial communities. By characteriz- ing the bacterial and fungal communities at 140 soil samples were collected from 140 sampling points in banana plantations through amplicon sequenc- ing and analyzing soil physicochemical properties, we found that the abundance of pathogens was sig- nificantly positively correlated with total phosphorus, total nitrogen, organic matterin the soil and annual precipitation, while it was negatively correlated with temperature. Pathogen abundance was positively cor- related with bacterial diversity and fungal richness, but it was not related to the composition of bacterial and fungal communities. However, within different taxonomic groups, many microorganisms were still found to be correlated with Foc TR4, with the asso- ciated bacteria often having high biocontrol poten- tial and the fungi mainly being soil-borne pathogens. Additionally, our network analysis revealed that as the pathogen abundance increased, the modularity index of the network also increased. These results deepen our understanding of the patterns of soil pathogen Fig. 3   The phylogenetic distribution of the bacterial core OTUs (present in all soil samples) and their primary driving factors were investigated. A phylogenetic tree was constructed using the NJ method. A heatmap was generated to illustrate the correlation between environmental factors and core OTUs using the Spearman correlation coefficient. CAT: Catalase; PHO: Phosphatase; INV: Invertase; URE: UreaseSOM: soil organic matter; TN: total nitrogen; AN: available nitrogen; TP: total phosphorus; AP: available phosphorus; TK: total potas- sium; AK: available potassium. FocTR4: the abundance of Foc TR4 787Plant Soil (2024) 505:779–793 1 3 Vol.: (0123456789) population changes in banana plantations and provide evidence and guidance on how to reduce pathogen abundance and select beneficial microorganisms in banana production. The impact of soil properties and environmental factors on the abundance of pathogen Revealing the influence of soil properties on the abundance of soil-borne pathogenic fungi is cru- cial for helping banana plantations reduce patho- gen abundance in the soil (Löbmann et  al. 2016; Van Agtmaal et al. 2018). Our study indicates that total phosphorus, total nitrogen, and organic mat- ter in the soil are significantly positively correlated with the abundance of pathogens. In the case of interactions between under conditions of high phos- phorus, more fungi tend to exhibit plant-damaging effects (Mesny et  al. 2021). A research shows that higher phosphorus application rates increase the severity of peanut wilt disease (Li et al. 2021a, b). High nitrogen content and low pH could accelerate the infect of FocTR4 and hinder plant development (Segura-Mena et al. 2021) in the soil. A study also suggests that increasing soil nitrogen levels could increase the incidence of banana wilt disease by Foc 1 and the losses of biomass (Segura et al. 2022). It is generally believed that the application of organic fertilizers reduces the number of pathogens (Zhang et al. 2014; Tao et al. 2020), which may be related to adding organic fertilizers with a high diversity of carbon sources can enhance microbial diver- sity, thereby reducing pathogen numbers (Shen et  al. 2013). Excessive organic matter can also increase the number of pathogenic fungi in the soil, a research shows that the application of crop straw and fresh livestock manure significantly increases the proportion of plant pathogenic fungi, which is associated with increased soil organic carbon (Du et al. 2022). In this study, the positive correlations between Foc TR4 and these nutrients may be related to excessive fertilizer application in banana planta- tions. Exceeding the threshold of nutrients required for plant growth can increase the nutrients available to pathogens in the soil and increase the plant pre- disposition, making it easier for pathogens to infect (Walters and Bingham 2007; Zhang et al. 2022). Urease activity is an important indicator of soil nitrogen availability, and its enzyme-catalyzed products are a nitrogen source available to plants. Fig. 4   Inter-kingdoms co-occurrence networks for bacteria and fungi. Different colors represent different modules, and each node represents an OTU (a). Correlation between Mod- ule 5 and abundance of Foc TR4 by Spearman (b). Correlation between the module index of different Foc TR4 and abundance of pathogen by Spearman (c) 788 Plant Soil (2024) 505:779–793 1 3 Vol:. (1234567890) Its activity can indicate soil nitrogen-supplying capacity and is often positively correlated with organic matter content, total nitrogen, and read- ily available nitrogen content (Zantua et  al. 1977; Dharmakeerthi and Thenabadu 1996). The positive correlation with FocTR4 in this study reflects the increased pathogen abundance in soils with high nitrogen content. Despite large-scale studies suggesting an increase in soil-borne pathogens with climate warming (Del- gado-Baquerizo et al. 2020), this study reveals that the soil content of FocTR4 is significantly nega- tively correlated with the annual average tempera- ture and positively correlated with precipitation. The possible reason for this is that more overcast and cooler days can increase plant predisposition and accelerate the spread of pathogens (Pegg et al. 2019). Additionally, studies by Buddenhagen (2009) proposed Foc TR4 as a genetically diverse fungal species with various geographic lineages and patho- types. Some of these, such as the South American lineage, are likely more adapted to cooler climates, exhibiting optimal growth and reproduction at tem- peratures below 20 °C (Dita et  al. 2017; Ploetz 2006). In contrast, the globally prevalent Tropical Race 4 (TR4) lineage thrives in warmer conditions (25–35 °C) (García-Bastidas et  al. 2014; Molina et al. 2009). Other lineages like Foc TR4, found in Southeast Asia, may demonstrate wider tempera- ture tolerance or even a preference for cooler condi- tions (Maryani et al. 2019). Therefore, in the future, banana plantations in Yunnan Province should aim to select locations with higher annual average tem- peratures and lower precipitation. Additionally, they should reduce fertilizer application, especially nitrogen and phosphorus, and find the appropriate fertilizer amounts. This may help slow down the accumulation of pathogens, thus reducing the inci- dence of diseases. The interaction between pathogen abundance and soil bacterial and fungal communities Higher soil microbial diversity is considered to be associated with soil disease suppression (Garbeva et  al. 2004). In this study, the soil bacterial com- munity species diversity and fungal community species richness in banana plantations were sig- nificantly positively correlated with the abundance of pathogens. While this contradicts the traditional paradigm, it aligns with other studies suggesting that the relationship between diversity and dis- ease can be more complex, context-dependent, and driven by specific functional groups rather than overall diversity indices (Liu et  al. 2023). Previ- ous studies have shown that disease-suppressive soils in banana plantations have more stable bac- terial and fungal community diversity and exhibit higher diversity in response to Foc TR4 invasion compared to disease-conducive soils (Ou et  al. 2019). One potential explanation for our findings might lie in the specific interactions within the banana soil microbiome. Network analysis revealed that stronger competition among microbial species indeed reduced pathogen dominance, implying the presence of antagonistic or resource-limiting mech- anisms acting against Foc TR4 (Coyte et al. 2015). Higher microbial diversity hinders the establish- ment of pathogens in the soil (Gao et al. 2021). The Mantel’s test results in this study show that the composition of both bacterial and fungal com- munities is significantly correlated with pH, avail- able nitrogen, and available phosphorus. Notably, the abundance of pathogens did not exhibit a significant correlation with the composition of the microbial community. The possible reason was that the soil samples originated from diverse regions and climates, which might reduce the correlation between Foc TR4 and microbiome composition. The primary drivers of soil microbial communities are abiotic factors such as climate and soil properties, with pH being particu- larly significant (Islam et al. 2020). The relationship between soil properties and microorganisms is recip- rocal. Microorganisms regulated by the environment simultaneously drive changes in the environment, and the subsequent changes in soil properties driven by microorganisms subsequently alter the composition and function of microbial communities (Hartmann and Six 2023). These factors can serve as broad targets for the regulation of soil microorganisms. However, vari- ous microbial taxonomic groups remained signifi- cantly associated with pathogen abundance. With increasing pathogen abundance, there was a signifi- cant enrichment of bacteria that are known to sup- press pathogen abundance. For example, Rhodano- bacter, the application of salicylic acid led to the enrichment of Rhodanobacter in the root system of 789Plant Soil (2024) 505:779–793 1 3 Vol.: (0123456789) watermelons, assisting in resisting the infection of a specialized watermelon sickle fungus (Zhu et  al. 2022). High-abundance Gemmatimonas has been identified as a potential biomarker for soil health in Central American banana plantations (Shen et al. 2014), and numerous studies have revealed its (potential) functions in suppressing disease occur- rence (Franke-Whittle et  al. 2015; Rangjaroen et  al. 2017; Pang et  al. 2022), including Candida- tus_Koribacter (Xu et  al. 2022) and Bacillus (He et  al. 2021), which are negatively correlated with pathogen abundance. Conversely, as the abundance of soil-borne pathogenic fungi gradually increased, beneficial microorganisms such as Trichoderma (Zin and Badaluddin 2020), Purpureocillium, and Metarhizium (Baron et  al. 2020), which have the potential to suppress diseases, were identified. These fungi have been reported as potential biocon- trol fungi for various crop disease. In conclusion, while beta diversity did not exhibit a correlation with pathogens, there was still a signifi- cant enrichment of beneficial bacteria and soil-borne pathogenic fungi associated with increasing pathogen abundance. This reflects the complex species interac- tions that affect soil microbial community composi- tion, and this process is likely primarily influenced by host involvement since most of these microorgan- isms have been shown to be preferentially recruited by hosts through the release of corresponding car- bon sources into the soil. A study showed that com- pared to healthy chili peppers, plants infected with Fusarium wilt disease are more susceptible to infec- tion by other pathogenic fungi, but their roots, stems, and fruit niches all accumulate potential beneficial bacteria (Gao et al. 2021). Additionally, core micro- organisms related to pathogens were identified to be present in all regions, making them excellent targets for soil biological regulation (Toju et al. 2018). Such as, Sphingobium, A type of plant growth promoting rhizobacteria (Asaf et  al. 2020) and a large number of reported healthy soil biomarkers (Innerebner et al. 2011; Fu et al. 2017; Ding et al. 2021). The infection of pathogen increases competition within the soil microbiome The infection of pathogens intensifies competition within the soil microbiome, as revealed by our co- occurrence network analysis. We identified a module of closely associated microbial taxa, predominantly fungi, linked to pathogen presence. Interestingly, this module included both beneficial and pathogenic players. One prominent member is Mortierella, an agricultural inoculant known for its cellulose, hemi- cellulose, and chitinase-degrading enzymes (Li et al. 2020; Ozimek and Hanaka 2020). Studies have shown that M. alpina inoculation significantly suppresses ginseng root rot caused by Fusarium while promot- ing plant growth-promoting bacteria and fostering a more stable network (Wang et  al. 2022). This high- lights the multifaceted roles some microbes play within the community, potentially acting as com- petitors against pathogens while benefiting the host. However, the module also harbors Plectosphaerella, a soil-borne fungal pathogen known to induce wilt disease (Carlucci et al. 2012; Raimondo and Carlucci 2018). This underscores the complexity of soil micro- bial interactions, where beneficial and detrimental actors can coexist and potentially influence each oth- er’s niches and activities (Ray et al. 2020). Building synthetic communities with carefully chosen players, like cross-feeding communities, may offer promising strategies for manipulating these interactions and pro- moting disease suppression (Vorholt et  al. 2017; Ke et al. 2021). Our findings further suggest that patho- gen abundance intensifies community modularity, hinting at heightened competition within the micro- biome. This aligns with existing research demonstrat- ing that increased competition can hinder pathogen invasion and establishment (Wei et  al. 2015). Inter- estingly, while healthy banana plants exhibit a more stable community as expected (Fu et al. 2019), higher pathogen abundance seems to trigger the recruit- ment of specific beneficial bacteria. This suggests a dynamic response by the host and microbiome, poten- tially favoring competitive microbes against the path- ogen. However, the recruitment of beneficial fungi appears more selective, with an increased presence of soil-borne fungi alongside pathogens. This raises intriguing questions about the specific mechanisms underlying host-microbe interactions and niche differ- entiation within the soil community under pathogen pressure. Unraveling these complexities through fur- ther research is crucial for developing effective strat- egies to manipulate the microbiome towards sustain- able disease management. Through this research, we gain valuable insights into the intricate relationship between soil properties, 790 Plant Soil (2024) 505:779–793 1 3 Vol:. (1234567890) microorganisms, and soil-borne pathogens. By appre- ciating the complexities and dynamics within the soil microbiome, we can unlock promising avenues for promoting soil health and mitigating soil-borne dis- eases in banana plantations and beyond. Conclusions Our findings highlight the importance of understand- ing Fusarium banana wilt pathogens and the environ- mental factors that influence them. We suggested the pathogen infection significantly impact the planta- tions and its native microbial communities. In addi- tion, we observed a positive correlation among the pathogen abundance, soil factors, and diversity of microbial communities. Conversely, a negative cor- relation was observed with the annual mean tem- perature. As Foc TR4 accumulate, beneficial bacteria (such as Rhodanobacter, Gemmatimonas, Novosphin- gobium) and soil borne fungi (such as Plectosphaere- lla, Nigrospora, Cyphellophora) were recruited, and network was more stable. This study contributes valuable information for mitigating pathogenic fungal abundance and promoting beneficial microorganisms in banana production. Acknowledgements  We thank all staff members in the Yunnan Key Laboratory of Green Prevention and Control of Agricultural Transboundary Pests for their considerable help with samples collection. The research leading to these results received funding from Yunnan Provincial Agricultural Joint Special Project (202101BD070001-001), Yunnan Provincial Xingdian Talent Program “Young talent” Project (YNWR- QNBJ-2019-246; YNWR-QNBJ-2020-298), Natural Science Foundation of China (31600349), Yunnan Science and Tech- nology Mission (202204BI090019) and the earmarked fund for CARS (CARS-31-22). Special thank you is also given to the reviewers. Authors’ contributions  Wenlong Zhang: Methodology, Software, Visualization, Writing - review & editing. Tingting Bai: Conceptualization, Supervision, Funding acquisi- tion. Arslan Jamil: Writing - review & editing. Huacai Fan: Resources, Investigation. Xundong Li: Supervision, Funding acquisition. Si-Jun Zheng: Conceptualization, Supervision, Funding acquisition. Shengtao Xu: Conceptualization, Super- vision, Funding acquisition, Writing - review & editing. Declarations  Conflict of interest  The authors declare no conflicts of inter- est. 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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. https://doi.org/10.1016/j.soilbio.2017.11.015 https://doi.org/10.1016/j.soilbio.2017.11.015 https://doi.org/10.1016/j.chom.2017.07.004 https://doi.org/10.1111/j.1744-7348.2007.00176.x https://doi.org/10.1128/AEM.00062-07 https://doi.org/10.3389/fmicb.2022.850917 https://doi.org/10.3389/fmicb.2022.850917 https://doi.org/10.1038/ncomms9413 https://doi.org/10.1038/ncomms9413 https://doi.org/10.1016/j.soilbio.2017.07.016 https://doi.org/10.1016/j.micres.2021.126917 https://doi.org/10.1016/j.micres.2021.126917 https://doi.org/10.2136/sssaj1977.03615995004100020036x https://doi.org/10.2136/sssaj1977.03615995004100020036x https://doi.org/10.1016/S1002-0160(14)60047-3 https://doi.org/10.1016/S1002-0160(14)60047-3 https://doi.org/10.1016/j.agee.2022.108176 https://doi.org/10.1016/j.agee.2022.108176 https://doi.org/10.3389/fmicb.2022.1015038 https://doi.org/10.1016/j.aoas.2020.09.003 https://doi.org/10.1007/s10658-017-1406-3 The interaction between Fusarium oxysporum f. sp. cubense tropical race 4 and soil properties in banana plantations in Southwest China Abstract Aims Methods Results Conclusions Introduction Materials and methods Sampling points and soil sampling Determination of soil nutrients and enzyme activity DNA extraction and PCR amplification Quantitative real-time PCR analyses Illumina sequencing and data processing Statistical analysis Results Correlation between soil characteristics and Foc TR4 abundance Community diversity and composition and Foc TR4 correlations Microbial taxa associated with pathogen Network analysis Discussion The impact of soil properties and environmental factors on the abundance of pathogen The interaction between pathogen abundance and soil bacterial and fungal communities The infection of pathogen increases competition within the soil microbiome Conclusions Acknowledgements References