Vol.: (0123456789) 1 3 Plant Soil (2023) 485:333–347 https://doi.org/10.1007/s11104-022-05829-z RESEARCH ARTICLE Phagotrophic protist‑mediated control of Polymyxa graminis in the wheat rhizosphere Chuanfa Wu · Chaonan Ge · Fangyan Wang · Haoqing Zhang · Zhenke Zhu · Didier Lesueur · Jian Yang · Jianping Chen · Tida Ge Received: 3 August 2022 / Accepted: 30 November 2022 / Published online: 5 December 2022 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract Purpose Uncovering potential biocontrol agents that suppress soil-borne pathogens is an important step toward developing sustainable management strate- gies for disease control and to maintain plant health. Plant cultivars influence rhizosphere microorganism- mediated soil-borne disease control. However, the disease-resistance mechanisms and microbial taxa involved in the control of soil-borne mosaic virus are largely unknown. Methods We designed a field experiment on wheat cultivars for virus-resistance identification and conducted metagenomic analysis to determine the potential mechanisms used by rhizosphere microbial communities that affect the density of the mosaic virus vector- Polymyxa graminis, and to identify potential microbes that inhibit virus transmission. Results We found high P. graminis abundance and microbial diversity in the susceptible wheat cultivar rhizosphere. The relative abundance of indicative Responsible Editor: Sven Marhan. Supplementary information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11104- 022- 05829-z. C. Wu · F. Wang · H. Zhang · Z. Zhu · J. Yang · J. Chen (*) · T. Ge (*)  State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology in Plant Protection of Ministry of Agriculture and Zhejiang Province, Institute of Plant Virology, Ningbo University, 818 Fenghua Road, Jiangbei District, 315211 Ningbo, China e-mail: jianpingchen@nbu.edu.cn T. Ge  e-mail: getida@nbu.edu.cn C. Ge  Ninghai General Station of Agricultural Technical Extension, Ninghai, Zhejiang 315600, China D. Lesueur  Centre de Coopération Internationale en Recherche Agronomique pour le Développent (CIRAD), UMR Eco&Sols, Hanoi, Vietnam D. Lesueur  Eco&Sols, University of Montpellier (UMR), CIRAD, Institut National de la Recherche Agronomique (INRAE), Institut de Recherche pour le Développent (IRD), Montpellier SupAgro, Montpellier 34060, France D. Lesueur  Common Microbial Biotechnology Platform (CMBP), Alliance of Bioversity International and International Center for Tropical Agriculture (CIAT), Asia hub, Hanoi, Vietnam D. Lesueur  School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Deakin University, 3125 Melbourne, VIC, Australia D. Lesueur  Chinese Academy of Tropical Agricultural Sciences, Rubber Research Institute, Haikou, China http://crossmark.crossref.org/dialog/?doi=10.1007/s11104-022-05829-z&domain=pdf http://orcid.org/0000-0003-0422-6122 https://doi.org/10.1007/s11104-022-05829-z https://doi.org/10.1007/s11104-022-05829-z 334 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) phagotrophs showed a strong negative correlation to P. graminis abundance during disease onset, indi- cating that predator–prey interactions influenced P. graminis activity. Moreover, we found strong and negative associations between the relative abundance of key ecological cluster, hub phagotrophic species and P. graminis abundance in multitrophic ecological networks. A structural equation model analyses indi- cated that phagotrophic protists were the main predic- tors of P. graminis abundance upon disease onset. Conclusion Our results demonstrate the important role of phagotrophic protists as top-down controllers for plant defense against pathogens. Our findings highlight the complexity of rhizosphere networks, reflecting the co-occurrence patterns of multi-trophic level microbes in virtual networks and strengthening the association between soil microbial diversity and plant health. Keywords Phagotrophic protists · Rhizosphere soil · Polymyxa graminis · Co-occurrence networks · Plant health Introduction Polymyxa graminis is a soil-borne protist of the order Plasmodiophorida that transmits over ten distinct plant viruses (Adams and Jacquier 1994). Wheat crops in China are susceptible to several soil-borne patho- genic viruses, such as Chinese wheat yellow mosaic virus (CWMV) (Diao et  al. 1999), and wheat yel- low mosaic virus (WYMV) (Ye et  al. 1999), which severely affect grain yield and quality, resulting in 70–80% yield loss during severe epidemics (Guo et al. 2019; Xu et  al. 2018). The viral vector, P. graminis exhibits strong resistance to environmental stress (including low temperature and drought), thereby maintaining infectivity for over ten years ( Adams 1991; Adams and Jacquier 1994). The protist vec- tor relies on water for transportation and infection of hosts (Adams 1991). Conventional tillage and water- ing can significantly accelerate virus transmission and host infection by P. graminis, thereby posing a long- term threat for wheat production (Guo et al. 2019). Chemical agents such as pesticides and fungicides have been mostly ineffective in suppressing the spread of P. graminis which eventually leads to the spread of wheat mosaic disease (Guo et  al. 2019). Presently, cultivation of disease-resistant wheat varieties is the most effective strategy to control wheat mosaic dis- ease (Cui et al. 2017; Wu et al. 2017). The resistance toward WYMV-infection in the resistant wheat culti- vars is exhibited by the roots throughout the infected culture system (Liu et  al. 2016). The secretion of abscisic acid by the root caps into the plant rhizos- phere could be attributed to the resistance of plants to wheat mosaic virus (He et al. 2021). The rhizosphere is the first line of defense against pathogenic infection, and it is important for the plants to be successfully colonized by beneficial microorgan- isms to keep the pathogen populations under check (Mendes et  al. 2013). The rhizosphere is colonized by a complex and diverse array of microorganisms, including protists, bacteria, nematodes, and fungi. Plants rely on some of these microbial taxa for spe- cific functions, including maintenance of homeostasis during growth. Upon pathogen infection, plants alter the production of root exudates to recruit beneficial rhizosphere microbial communities that in turn sup- press pathogenic infections (Bakker et  al. 2013; Fu et al. 2020; Liu et al. 2021; Wang et al. 2022). There- fore, a comparative characterization of the rhizos- phere microbes between healthy and infected plants would help identify beneficial microbes that maintain and enhance plant health (Bongiorno 2020; Gao et al. 2019). Research on rhizosphere microbiome assem- bly has adopted a bottom-up perspective to determine how resources such as root metabolites influence bacteria or fungi to support plant growth and pro- tect against stresses (Duran et  al. 2018; Manici and Caputo 2009; Sanguin et al. 2009; Wang et al. 2020; Wen et al. 2021). Till date, the role of protists as driv- ers of microbial community structure has been largely overlooked (Gao et al. 2019); few studies have eluci- dated the key microbial taxa and their potential inter- actions in the plant rhizosphere (Hassani et al. 2018). Therefore, a complete microbial analysis is required to determine the role of the rhizosphere microbiota and their multitrophic interactions to benefit plant health. Protists are highly diverse and abundant soil eukar- yotes and have long been recognized as sensitive bio- markers of soil health, which influence rhizosphere bacterial and fungal communities through nutrient- predator-prey interactions (Fournier et  al. 2022; Kramer et  al. 2016). However, protist communities have rarely been linked to plant health (Gao et  al. 2019). Protists, especially phagotrophic protists such as Glissomonadida and Cercomonadida influence 335Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) agroecosystems through diverse functions (Sapp et al. 2018); euglyphids and Rhogostomidae regulate paddy soil fungal community structure through strong top-down control (Huang et  al. 2021). Furthermore, phagotrophic protists that ingest microorganisms influence plant health by inhibiting pathogen repro- duction; they also influence bacterial function by triggering the activation of pathogen-suppressing sec- ondary metabolic genes during plant growth (Xiong et al. 2020). Additionally, protists improve crop yield by interacting with plant-beneficial microbes (Guo et  al. 2021). On the other hand, interactions with other protists, including pathogenic species such as P. graminis, have rarely been investigated. Given the significant influence of protists, understanding the relationship between rhizosphere protists and the viral vector P. graminis, as well as those among rhizos- phere microbes, is critical for revealing the mecha- nisms underlying soil-borne disease outbreaks and harnessing native microbes for soil-borne disease control. To investigate key microbial taxa that may inhibit the spread of mosaic virus disease, two winter wheat cultivars (one resistant and one susceptible to wheat mosaic disease) were cultivated in a field ecosystem. The experimental field has been used for studies on wheat mosaic disease over a long period of time, and the soil was naturally infected with P. graminis. We investigated the composition of the rhizosphere microbiome (protists, bacteria, and fungi) and the abundance of P. graminis, to identify potential micro- bial interactions in the rhizosphere of both resistant and susceptible wheat cultivars. The objectives of our study were to (1) identify changes in microbial diversity and potential interactions following disease development and (2) explore whether protists, espe- cially phagocytic protists, can predict P. graminis density and plant health in a highly managed farm- land ecosystem. Materials and methods Field site, plant material, and sampling Since 2012, the field experiment comprised a conven- tional cropping system (nine-year rotation of summer maize and winter wheat) in Linyi, China (35°11′N, 118°38′E). The experimental soil was classified as arenic fluvisol according to the WRB soil classifica- tion system (ISSS, ISRIC 1998). The soil has a pH of 6.2, total nitrogen of 1.19 g/kg, and organic carbon content of 12.65 g/kg. Winter wheat cultivars, For-susceptible wheat (FSW, Linmai 4) and For-resistant wheat (FRW, Jimai 22), were chosen based on their genetic resist- ance to soil-borne wheat virus pathogens. Seeds were provided by the Crop Research Institute, Shandong Academy of Agricultural Sciences, China. The field experimental design included ten blocks and two wheat cultivars. Each block consisted of two plots, each plot was 4  m long with 12 rows with a 0.2  m distance between two rows; neighboring plots were separated by 1.0 m. In October, the two cultivars were planted according to local farming practices. Before sowing, the soil was mechanically tilled to reduce soil physicochemical differences between plots. Compound fertilizer was used as a basal fertilizer at 750 kg ha–1 (nitrogen: phosphorus pentoxide: potas- sium oxide [N: P2O5:K2O] = 14: 7: 9). Manual weed- ing was performed periodically and no herbicides or insecticides were used throughout the growth period of wheat. The degree of soil-borne disease progression was determined based on field observations of the wheat streak mosaic in the infected leaves as described by Kojima et  al. (2015). In March 2020, wheat mosaic virus disease occurred in the susceptible wheat culti- var (returning green period), while resistant varieties did not develop the disease. Rhizosphere soil (defined as those tightly attached to the roots) samples were collected from each of the cultivars by shaking the roots; the corresponding root samples were clipped and immediately placed into ice bags. Five rhizo- sphere soil samples from each plot were randomly selected and were pooled into one sample. A total of 20 rhizosphere soil samples were obtained (2 wheat cultivars × 10 blocks). The samples to be used for microbial analysis were stored at −80 °C. Soil phys- icochemical characteristics (Table  S1) were deter- mined as described (Wu et al. 2021a) and are outlined in “Method S1”. DNA extraction, quantitative PCR analysis, and amplicon sequencing Rhizosphere soil DNA was extracted using the DNeasy PowerSoil kit (Qiagen, Hilden, Germany) 336 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) according to manufacturer’s instructions. DNA con- centration and quality were assessed both by 1.5% agarose (w/v) gel electrophoresis and spectropho- tometry (NanoDrop Technologies, Wilmington, DE, USA). DNA concentration and quality (ratio of absorbance, A260: A280) were in the range of 15–65 ng/µL and 1.8–2.0, respectively. The abundance of P. graminis was determined using real-time quantitative polymerase chain reac- tion (RT-qPCR) with specific primer sets, Pg.F2- F and Pg.R2-R (Xu et  al. 2018). The primer sets used were: 515  F/806R, targeting the V4 region of bacterial 16  S rRNA gene (Walters et  al. 2016), ITS5-1737  F/ITS2-2043R, targeting fungal internal transcribed spacer 1 regions (Jiao et al. 2018)d TAR- euk454FWD1/R-TAReukREV3, targeting the V4 region of eukaryotic 18  S rRNA gene (Stoeck et  al. 2010). PCR amplification conditions and primer sequences are given in Table S1. Sequencing was per- formed at Novogene Biopharm Technology Co., Ltd. (Tianjing, China) using Illumina MiSeq PE250 (Illu- mina, San Diego, CA, USA) with a paired-end pro- tocol. Raw DNA sequence data have been submitted to the National Center for Biotechnology Information (NCBI, Bethesda, MD, USA) Sequence Read Archive (SRA) database under the BioProject accession num- ber PRJNA825734. Details of quantitative PCR anal- ysis and amplicon sequencing are described in the supplementary material “Method S1.” Bioinformatics analysis The raw sequences were merged and filtered using barcodes from Quantitative Insights into Microbial Ecology (QIIME2) (Bolyen et al. 2019) as per previ- ously established protocols (Wu et al. 2021a). Briefly, raw sequence data were demultiplexed and quality fil- tered using the q2-demux plugin followed by denois- ing with DADA2 (Callahan et  al. 2016) (via q2‐ dada2) to remove the errors and chimeric sequences and identify all observed amplicon sequence variants (ASVs). Next, all ASVs were aligned with MAFFT (Katoh and Standley 2013) (via q2‐alignment) and used to construct a phylogenetic tree with fasttree2 (Price et  al. 2010) (via q2‐phylogeny). Bacterial, fungal, and protistan representative sequences were analyzed using the q2‐feature‐classifier (Bokulich et al. 2018) and the classify-sklearn naive Bayes tax- onomy classifier (Pedregosa et  al. 2011) trained and classified based on the silva132 database (McDonald et  al. 2012), UNITE (v8.0, https:// unite. ut. ee/) data- base, and Protist Ribosomal Reference (PR2) data- base (Guillou et al. 2013), respectively. To obtain an equivalent sequencing depth for later analyses, all samples were rarefied to 56,271 reads in bacteria, 52,863 reads in fungi, and 14,383 reads in protists using the “Vegan” package in R v.4.0.2 (Dixon 2003). We further assigned protistan ASVs to different func- tional groups, including phagotrophs, phototrophs, parasites, plant pathogens, saprotrophs, mixotrophs, and unknown protists, according to their nutritional mode (Dumack et al. 2020; Xiong et al. 2019). Statistical analysis Differences in physicochemical properties and P. graminis abundance in the FSW and FRW rhizos- phere soils were assessed via a nonparametric t-test using R software. The alpha diversity indices (Rich- ness and Shannon) of microbial communities were measured using the “Vegan” package and further analyzed for significance using the nonparametric t-test in R software. Beta diversity was visualized with Bray–Curtis similarity matrices using principal coordinate analysis (PCoA); analysis of similarities (ANOSIM) was applied to assess the differences in microbial community structures between FSW and FRW rhizosphere soils using the “anosim” function in R. We selected the microbial community (includ- ing richness, Shannon index, and structure (PCoA1)) index of bacteria, fungi, and protists to reduce dimen- sionality by factor analysis (KMO > 0.8, Bartlett test p < 0.05) in SPSS 22.0. We extracted three variables as microbial predictors and used multiple regression by lineal models in R to calculate the significance of the correlation between microbial predictors and P. graminis abundance (all data was standardized by “scale” function in R). Multiple regression analy- ses were performed using “relaimpo” package in R to infer the relative importance of microbial predic- tors on P. graminis abundance (Groemping 2006). Within-cultivar community dissimilarity of micro- bial populations (bacteria, fungi, and protist) for each cultivar was calculated using mean Bray-Curtis dis- tance of all pairwise comparisons within a cultivar; the relationship between community dissimilarity with P. graminis abundance was calculated using lin- ear correlation. Redundancy analysis was performed https://unite.ut.ee/ 337Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) to examine correlations between physicochemi- cal properties and microbial communities (based on ASV level) using the “ggrepel” and “ggpubr” pack- ages in R. A forward selection procedure was used to select significant variables. Linear discriminant analysis (LDA) effect size (LEfSe) was determined (Kruskal–Wallis test p < 0.05, logarithmic LDA score > 2.0) to identify microbial biomarkers for dif- ferent wheat cultivars (Segata et al. 2011). We then developed co-occurrence networks to infer the potential interactions between protistan, bac- terial, and fungal taxa and identified ecological mod- ules of strongly correlated taxa. The network was pro- duced using a Spearman correlation matrix calculated with the “psych” package in R (Langfelder and Hor- vath 2012). Network nodes represented ASVs, and edges connecting different nodes corresponded to sig- nificant correlations between ASVs. To reduce false positive results, we adjusted all p-values for multiple correlations using Benjamini and Hochberg false dis- covery rate (FDR) (Benjamini and Hochberg 1995). Robust correlations with the Spearman correlation coefficients > 0.80 and FDR adjusted p-values < 0.01 were selected to construct the co-occurrence net- works, which was visualized using Gephi 0.9.2 (Bastian et  al. 2009). The algorithm of fast greedy modularity optimization was used to detect and iso- late modules via directly optimizing the Modularity score (Deng et al. 2012). The nodes with high degree and closeness centrality values were identified as hub nodes in multitrophic networks (Agler et  al. 2016; van der Heijden and Hartmann 2016). The relative abundance of each module was calculated by aver- aging the standard relative abundances (z-score) of all taxa under each module using “scale” function in R (Delgado-Baquerizo et  al. 2018). The relation- ships between ecological modules, hub nodes, and P. graminis abundance were then tested using linear regression. Structural equation modeling (SEM) was performed using Amos 20.0 to explore and quantify links between physicochemical soil properties, microbial community composition (including phagotrophic protists, bacte- ria, and fungi), and P. graminis abundance. All of the measured soil physicochemical property indices were reduced in dimensions by factor analysis (KMO > 0.8, Bartlett test p < 0.05) to the obtained physicochemi- cal variable. Microbial community composition, such as alpha diversity (including richness and Shannon indices) and structure (PCoA1 value) indices of bacte- rial communities, were reduced in dimensions to form bacterial variables. All variables were standardized via Z transformation to improve normality using the “scale” function in R 4.0.2 (Zhao et  al. 2019). The covariance matrix was fitted to the model using maxi- mum likelihood estimation. The following parameter ranges were measured to ensure model fitting: chi- square (P > 0.05), goodness-of-fit index (GFI > 0.90), and root mean square error of approximation (RMSEA < 0.05) (Grace and Keeley 2006). Results Rhizosphere soil physiochemical properties and polymyxa graminis abundance Comparative analysis showed that the rhizosphere soil of the FSW contained higher levels of phospho- rus (AP), total phosphorus (TP), and soil organic carbon (SOC) than FRW plants (p < 0.05, Table S2), whereas the rhizosphere soil of the FRW plants con- tained higher total magnesium (TMg) and total iron (TFe) contents than FSW plants (p < 0.05, Table S2) upon disease onset. The rhizosphere soil pH value and other nutrient element contents did not differ sig- nificantly between the two cultivars. RT-qPCR analysis showed that the abundance of P. graminis in the rhizosphere soil of the susceptible cultivar ranged from 6.60 × 106 to 2.72 × 107 dur- ing disease onset, and was significantly higher than that in the resistant cultivar (4.56 × 105 to 5.35 × 106) (p = 2.097e-5, Fig.  1a). However, Multiple linear regression analysis showed that none of the physi- ochemical soil properties correlated significantly with P. graminis abundance in the rhizosphere soil samples (p > 0.1435, Table S3). The relationship between Polymyxa graminis abundance and microbial diversity in rhizosphere soil Microbial diversity of the rhizosphere soil differed significantly between the resistant and susceptible cul- tivars (p < 0.05, Fig. S1). Rhizosphere alpha diversity (including bacteria, fungi, and protists) in FRW plants was lower than that in FSW plants (p < 0.05, Fig. S1a, b). After the disease outbreak, we detected significant differences in rhizosphere microbial communities 338 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) containing bacteria, fungi, and protists between the two wheat cultivars, with the strongest differences observed in the protistan community (ANOSIM test, protist: R = 0.4837, p = 0.00021; Fig.  S1c). Further- more, redundancy analysis revealed that the physico- chemical parameters of SOC level exerted the strongest influence on the bacterial and protistan communities (Fig. S2a, c); NO3 − content had the greatest influence on the fungal community in the wheat rhizosphere soil upon disease onset (Fig. S2b). In this study, we found that microbial alpha diver- sity (including bacteria, fungi, and protist) was positively correlated with P. graminis abundance (Fig.  S3a, b), whereas the dissimilarity in bacterial and protistan communities had a negative effect on P. graminis abundance (Fig. S3c). It is worth mention- ing that the microbial diversity and community dis- similarity indexes of protists strongly correlated with P. graminis abundance (Fig, S3), indicating that P. graminis abundance was highly influenced by the pro- tistan community. Further prediction analysis showed that bacterial and protistan diversity significantly pre- dicted P. graminis abundance (p < 0.001) in all rhizo- sphere soil samples from both cultivars, accounting for 13.47% and 9.97% of the observed variations, respectively (Fig.  1b). In contrast, fungal diversity was not significantly associated with the abundance of P. graminis (p > 0.05; Fig. 1b). The phagotrophic protist community was signifi- cantly different between the resistant and suscepti- ble cultivars (ANOSIM test, R = 0.4637, p < 0.001; Fig. 2). Moreover, P. graminis abundance was nega- tively correlated with relative total phagotroph abun- dance in rhizosphere soil (R = − 0.52, p = 0.018; Fig.  2). The above results suggest that rhizosphere protistan communities (including phagotrophic Fig. 1 Polymyxa graminis abundance in wheat rhizosphere soil from susceptible and resistant cultivars (a). Prediction of the Polymyxa graminis abundance by microbial parameters (b). FRW, For-resistant wheat; FSW, For-susceptible wheat. The p value indicates the significance between FRW and FSW based on the Student’s t test. Bacteria, fungi, and protist includes alpha diversity indices (Shannon and Richness) and beta diver- sity (PCoA1), respectively. * p < 0.05 Fig. 2 Community struc- ture of wheat rhizosphere phagotrophs (a). Linear relationships between Poly- myxa graminis abundance and the relative abundance of phagotrophic protists in diseased and healthy plants across the two wheat culti- vars (b). FRW, For-resistant wheat; FSW, For-suscep- tible wheat. The ellipses represent 80% confidence intervals of each cultivar 339Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) protists) showed greater dissimilarity among cultivars than bacterial and fungal communities. LDA effect size (LEfSe) analysis revealed that 47 protistan ASVs were significantly enriched in the rhizosphere community of the FSW, and 36 protistan biomarkers were found in the FRW (Fig. S4); 39 pro- tistan ASVs indicative of diseased plants and 33 pro- tistan ASVs indicative of healthy plants were identi- fied as phagotrophs, most of which were predators of other microbes. Subsequently, correlation-based network analysis revealed that nine biomarker ASVs were significantly correlated with P. graminis abun- dance in the FSW plants (Fig. 3a). The analysis also revealed seven biomarker ASVs exhibiting strong links with P. graminis abundance in the rhizosphere community of the FRW (Fig.  3b). Of these, seven protistan ASVs indicative of infected plants (with only four in healthy plants) were classified as phago- trophs with significant negative links to P. graminis abundance during disease onset (Fig. 3). This means that overall, some phagotrophs tend to only be present in susceptible cultivars, and when they are present, their relative abundance is negatively associated with that of P. graminis. The above results indicate that the phagotrophic protist community, as well as some phagotrophic taxa, determine virus-vector abundance, based on the community structure of phagotrophs and phagotrophic indicator ASVs; significant nega- tive correlations exist between phagotrophic protistan ASVs and P. graminis in correlation-based networks upon disease outbreaks. Ecological module linking of P. graminis abundance in multitrophic networks The multitrophic network was dominated by protists, bacteria, and fungi, accounting for 46%, 39%, and 15% of total microbial nodes, respectively (Fig. 4a). We used the ecological network to identify modules of microbial taxa highly correlated with each other, and therefore, potentially linked to P. graminis abun- dance. We detected six ecological modules divided from the multitrophic network (Fig. 4b and Fig. S5). Over these modules, we found that the relative abun- dance of Module #4 was negatively correlated with the P. graminis abundance, and more interestingly, the proportion of protistan ASVs in Module 4 was greater than that of other microbial taxa (Fig. 4g and Fig.  S5). The ecological module #4 was dominated by bacteria (e.g., Proteobacteria, Actinobacteria, and Bacteroidetes), fungi (e.g., Ascomycota, Basidiomy- cota, Chytridiomycota, and unknown taxa), and pro- tists (e.g., Phagotroph, Phototroph, and Parasite). A complete list with microbial taxa within the module can be found in the supplementary Table S4. We further selected some “Hub nodes” (nodes with high values of degree (> 60) and closeness central- ity (> 0.42)) to illustrate the link between hub microbes Fig. 3 Networks of indicator amplicon sequence variants (ASVs) linked with Polymyxa graminis vector in FSW cultivar (a) and FRW cultivar (b). Circles represent microbial ASVs, and the circle colors correspond to taxonomic features. Green and red solid lines indicate significantly positive and negative correlations (p < 0.05), respectively 340 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) and P. graminis abundance in the multitrophic net- work (Fig. 4c and Table S5). We found these hub nodes belonged to modules #1–4, and were mainly composed of the bacterial phyla Proteobacteria, Actinobacte- ria, fungal Ascomycota, and protistan functional guild Phagotroph (Table  S5). Specifically, we found that P. Fig. 4 Distribution patterns of the “hub nodes” and ecological modules based on multitrophic networks. a  Network diagram with nodes colored according to each of the three taxonomic taxa (bacteria, fungi, and protists). b  Network diagram with nodes colored according to each of the six ecological module (Modules #1–6). c Distribution patterns of the “hub nodes” of multitrophic networks based on all rhizosphere soil samples. d- i The regression relationships between the relative abundance of ecological module (Module #1–6) and Polymyxa graminis abundance. “Hub” nodes were identified as those which were significantly more central and more connected than other nodes within multitrophic networks. The relative abundance of each module was calculated by averaging the standard relative abundances (z-score) of all taxa belonging to each module 341Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) graminis abundance was positively correlated with the relative abundance of Bac_ASV1173, Bac_ASV1307, Bac_ASV1312, Bac_ASV1566, Fun_ASV10, Fun_ ASV18, Pro_ASV460, and Pro_ASV693 in module #2 (Fig. S6). It is worth noting that Pro_ASV693 was clas- sified as P. graminis, indicating that P. graminis could occupy a higher living space in the rhizosphere soil of diseased plants to propagate and infect wheat roots. The P. graminis abundance was negatively linked to the rela- tive abundance of Bac_ASV316, Bac_ASV317, and Bac_ASV319 (all belong to Actinobacteria) in module #2 (Fig. S6). The relative abundance of these phagotrophs (e.g., Pro_ASV1069, Pro_ASV1183, Pro_ASV818, and Pro_ASV971) in module #4 were also negatively asso- ciated with P. graminis abundance (Fig. S6). The results suggest that the significant negative correlation between module #4 and P. graminis abundance may be due to the regulation of phagocytic protists in the multitrophic network. Underlying drivers of P. graminis abundance SEM was performed to determine the contribution of each potential influencing factor (including soil prop- erties, phagotrophic protistan community, and bacte- rial and fungal communities) to the difference in P. graminis abundance among the different wheat culti- vars upon disease onset (Fig. 5a, b). These predictors explained 71.6% of the variance in P. graminis abun- dance (Fig. 5a). The model showed that phagotrophic protists had direct or indirect negative correlations (via negative interactions with fungi) with P. graminis abundance, whereas fungi had a direct positive cor- relation. Soil properties also had an indirect correla- tion with P. graminis abundance by negatively affect- ing the phagotrophic protists (Fig. 5a). The standard total effects for P. graminis abundance indicated that phagotrophic protists exerted a stronger correlation with P. graminis abundance than other factors in the rhizosphere communities between FSW and FRW cultivars (Fig.  5b). Our SEM findings show that the phagotrophic protistan community may be the pri- mary regulator of P. graminis abundance. Discussion Sensitivity of protistan communities and other microbes to wheat cultivar during disease onset Elucidating the assembly of the crop rhizosphere microbiome and the mechanisms by which it inter- acts with the environment is a central requirement for maximizing agricultural production (Singh and Trivedi 2017). To achieve this goal, we first analyzed the alpha diversity and structure within rhizosphere communities of wheat cultivars with different disease resistance levels. The results showed significant dif- ferences in the rhizosphere communities of both culti- vars; particularly, higher alpha diversity was observed Fig. 5 Path analysis illustrating the link across microbial com- munities affecting Polymyxa graminis abundance (a). Con- tribution of biotic and abiotic factors to the changes of Poly- myxa graminis abundance (b). Continuous and dashed arrows represent significant and nonsignificant relationships, respec- tively. Green and red arrows indicate positive and negative relationships, respectively. r2 values indicate the proportion of variance explained by each variable. Standardized total effects (direct plus indirect effects) calculated using SEM. *p < 0.05, **p < 0.01, and ***p < 0.001 342 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) in the rhizosphere community of the susceptible cul- tivar. Plant-associated microbiomes were shaped by multiple host and environmental factors, such as host genetics and edaphic factors. It is in accordance with Wen et  al. (2020) who showed how plant phenotype changed the root exudate profiles which significantly affected the rhizosphere microbial diversity. We pro- pose that the incidence of diseases weakens the effects of the host plant and reduces its ability to filter rhizo- sphere microorganisms, which leads to an increase in rhizosphere microbial diversity in susceptible culti- vars. Soil nutrients are also important driving factors of change in rhizosphere communities, as confirmed by soil ecosystem studies (Shi et  al. 2018; Xiong et al. 2021). In this study, in addition to the influence of plant genetics, the higher AP and SOC contents in rhizosphere soil of susceptible cultivars could be con- tributing factors to the high microbial diversity of sus- ceptible cultivars. Protists are taxonomically the most diverse eukary- otes and occupy all key functional roles in soil food webs (Geisen et  al. 2018). We found that the planting of wheat cultivars with different levels of resistance (FSW and FRW) led to greater variation in protist com- munity structure than in that of other microbial com- munities (bacteria and fungi) in rhizosphere soils. This may have resulted from the involvement of protists in the hormonal regulation of plants, strongly affecting the plant metabolome (Gao et al. 2019). Protists stimu- late lateral root branching in plants by promoting auxin production, increasing cytokinin concentration (Krome et al. 2010), and stimulating the secretion of antimicro- bial substances (Brazelton et  al. 2008). In addition, in this study, environmental factors had a stronger impact on the community structure of protists than bacteria and fungi (Fig. S2). the niche breadth of protists may be lower, suggesting that protists are less tolerant to envi- ronmental changes than other microbes. The infection of wheat mosaic virus caused by P. graminis might also affect host rhizosphere communities. Infection of host plant with Polymyxa betae promoted the development of plasmodia and sporangia, and stimulated the expression of glutathione S-transferase gene (Decroës et al. 2022), which is involved in the regulation of protist communi- ties interacting with plants (Gullner et al. 2018). These observations suggest that rhizosphere community assembly is co-regulated by the host, viral infection, and soil factors; the protists respond more strongly to envi- ronmental differences than fungi and bacteria. In our study, we identified protistan predatory taxa and their importance in regulating P. graminis. Since there was no significant correlation between soil physic- ochemical properties and the abundance of P. graminis, we only analyzed the association between biotic factors and P. graminis, and used microbial communities as biological indicators to predict P. graminis abundance (Fig.  1). We also showed that the protist community, especially that of phagotrophic taxa, is likely involved in the suppression of P. graminis abundance (Fig. 2). These findings confirm the pivotal role of rhizosphere phago- trophs, a major functional group of protists in soil as a key microbiome link in agricultural systems responsible for plant health (Guo et al. 2021). The role of predatory protists inhibiting viral transmission vectors might be a disease suppression phenomenon in rhizosphere soils; such predatory protists act as keystone species and are worth exploring as biocontrol agents to increase sustain- able soil management. The important ecological role of phagotrophic taxa in the co-occurrence network Microbial communities could also be grouped into assemblies with particular trait combinations based on different co-occurrence or association patterns, offering new insights into complex microbial community struc- ture and soil function; rhizosphere microbial networks are extremely sensitive to pathogen infection (Bar- beran et al. 2012; Carrion et al. 2019; Fan et al. 2021, Fernandez-Gonzalez et al. 2020). In this study, we used multitrophic ecological networks to detect microbial clusters and hub species. The relative abundance of key ecological module and hub phagotrophic taxa poten- tially determine P. graminis abundance following dis- ease onset (Fig. 4 and Fig. S6). In particular, the relative abundance of module #4 showed a negative correlation with P. graminis abundance, indicating that the decrease in P. graminis abundance probably resulted from the increase in relative abundance of taxa within module #4. Most of ASVs within module #4 were from Burkholde- riales, Actinobacteria, and Cercozoa (Phagotrophic pro- tist) (Table S4) play an important role in maintain plant health. For instance, Burkholderiales showed significant inhibition of pathogen growth through the production of various secondary metabolites (Depoorter et  al.  2016) such as volatile organic compounds (VOCs), or Actino- bacteria as antagonistic bacteria producing antifungal compounds to compete for resources through high niche 343Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) overlapping (Essarioui et  al. 2017). In particular, Cer- cozoa, which are microbe-consuming protists, improve plant growth by preying on plant pathogen; they also increase the performance of plant growth-promoting rhizobacteria by preying on their competitors (Gao et al. 2019, Jousset  2017). Interestingly, the relative abun- dance of some hub species belonging to phagocytic protists from module #4 also showed negative correla- tions with P. graminis abundance, and could be classi- fied as microbe-consuming protists for plant health. For instance, the hub protistan species Pro_ASV1069, Pro_ ASV1183, Pro_ASV818, and Pro_ASV971 belong to Cercozoa (phagocytic protists), which are known to feed on bacteria, fungi, and even some eukaryotes (Dumack et  al. 2020); the microbes with strong competitiveness screened out by this protozoan predation stress may also effectively resist the pathogen infection (Guo et al. 2022). These results further suggest that key ecological clusters and phagotrophic protists potentially inhibit the growth of the viral vector and help maintain host health. Role of phagotrophic protists toward P. graminis abundance As a vector of plant viruses, P. graminis infects the roots of plants and exist in the soil for extended peri- ods in the form of dormant spores (Adams 1991). One sustainable biological strategy is to mobilize indigenous microorganisms and antagonizing path- ogens to prevent a disease outbreak. Notably, P. graminis abundance was higher in FSW rhizosphere soils than in FRW soils. However, relative phago- troph abundance was negatively correlated with P. graminis abundance among all rhizosphere samples, possibly due to predation by phagotrophic protists (Geisen et al. 2016). Previous studies have indicated that phagotrophs prey on or lyse the hyphae and spores of a variety of pathogens, thereby decreas- ing the risk of pathogenic infection (Chakraborty and Old  1982; Nikoljuk  1969). LDA effect size and network analyses showed more negative corre- lations between P. graminis and protistan ASVs in FRW than in FSW soil during disease onset. Most protistan ASVs have been identified as phagotrophs, which are predators of other microbes (Dumack et  al. 2020). Alternatively, the negative impacts of phagotrophic protists on P. graminis could result from interactions with competitors, as the niche breadth of protists is relatively low in complex agro-ecosystems (Wu et al. 2018). Our SEM results showed a significant link between phagotrophic protists and fungi, as shown previously (Guo et  al. 2021; Huang et al. 2021), highlighting phagotrophic protists as the key determinants of P. graminis abun- dance. Overall, our results suggest that phagotrophic protists represent keystone taxa, as they exhibit a strong negative correlation, and are therefore poten- tial drivers of P. graminis abundance. We demonstrate that phagotrophic protists may regulate virus-vector development throughout plant growth, as the decline in relative abundance of rhizosphere phagotrophs coincided with soil-borne disease outbreaks. Although the viral vector was present in the rhizosphere soil of healthy plants, a stable and high relative abundance of phagotrophic protists might have helped mitigate the transmission of the disease. Future studies should focus on the isolation of phagotrophic protists and P. graminis and assess the relationship between the two taxa. In future studies, we intend to utilize phagotrophic pro- tists to prevent and control the spread of soil-borne mosaic virus in the rhizosphere. Overall, this study suggests that phagotrophic protists may control pathogen vector and improve plant health by influ- encing changes in rhizosphere microbial commu- nity composition and function through multitrophic network regulatory strategies in complex farmland ecosystems. Herein, we reported the potential eco- logical functions of phagotrophic protists to con- trol pathogen spread and improve plant health in highly managed agro-ecosystems. Our findings highlight the complexity of rhizosphere community networks, reflecting the co-occurrence patterns of multi-trophic level microbes in virtual networks and strengthening the association between soil microbial diversity and plant health. Acknowledgements We thank Mr. Liu and Yufei Ning for their assistance in field work and soil sampling, and Dr. Anhui Ge and Dr. Chao Xiong for their generous help in data analysis. This study was financially supported by National key research and development program (2022YFA1304400),  Ningbo Sci- ence and Technology Bureau (2021Z101), China Agriculture Research System from the Ministry of Agriculture of P.R. China (CARS-03) and sponsored by the K.C. Wong Magna Fund of Ningbo University. Author contribution Tida Ge, Jian Yang, and Jianping Chen conceived and designed the study. Chuanfa Wu conducted the experiments. Chuanfa Wu, Fangyan Wang, and Haoqing Zhang 344 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) conducted the field investigation. Zhenke Zhu performed data analysis. Chuanfa Wu, Didier Lesueur and Tida Ge wrote the manuscript. Chaonan Ge participated in the revision and dis- cussion of the manuscript. All authors read and approved the manuscript. Declarations Conflict of interest The authors declare that they have no conflict of interest. References Adams M (1991) Transmission of plant viruses by fungi. Ann Appl Biol 118:479–492. https:// doi. org/ 10. 1111/j. 1744- 7348. 1991. tb056 49.x Adams MJ, Jacquier C (1994) Infection of cereals and grasses by isolates of Polymyxa graminis (Plasmodiophorales). Ann Appl Biol 125:53–60. https:// doi. org/ 10. 1111/j. 1744- 7348. 1994. tb049 46.x Agler MT, Ruhe J, Kroll S, Morhenn C, Kim ST, Weigel D, Kemen EM (2016) Microbial hub taxa link host and abi- otic factors to plant microbiome variation. PLoS Biol 14:e1002352. https:// doi. org/ 10. 1371/ journ al. pbio. 10023 52 Bakker PA, Berendsen RL, Doornbos RF, Wintermans PC, Pieterse CM (2013) The rhizosphere revisited: root micro- biomics. Front Plant Sci 4:165. https:// doi. org/ 10. 3389/ fpls. 2013. 00165 Barberan A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6:343–351. https:// doi. org/ 10. 1038/ ismej. 2011. 119 Bastian M, Heymann S, Jacomy M (2009) Gephi: an open- source software for exploring and manipulating networks. Proceedings of the international AAAI conference on web and social media, pp 361–362 Benjamini Y, Hochberg Y (1995) Controlling the false discov- ery rate - a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300. https:// doi. org/ 10. 1111/j. 2517- 6161. 1995. tb020 31.x Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Gregory Caporaso J (2018) Opti- mizing taxonomic classification of marker-gene ampli- con sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6:1–17. https:// doi. org/ 10. 1186/ s40168- 018- 0470-z Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al- Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodriguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonza- lez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS 2, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Var- gas F, Vazquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG (2019) Reproducible, interactive, scalable and extensi- ble microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. https:// doi. org/ 10. 1038/ s41587- 019- 0209-9 Bongiorno G (2020) Novel soil quality indicators for the evalu- ation of agricultural management practices: a biological perspective. Front Agric Sci Eng 7:257–274. https:// doi. org/ 10. 15302/j- fase- 20203 23 Brazelton JN, Pfeufer EE, Sweat TA, Gardener BB, Coenen C (2008) 2,4-diacetylphloroglucinol alters plant root development. Mol Plant Microbe Interact 21:1349–1358. https:// doi. org/ 10. 1094/ MPMI- 21- 10- 1349 Callahan B, McMurdie P, Rosen M, Han W, Johnson A, Hol- mes S (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https:// doi. org/ 10. 1038/ nmeth. 3869 Carrion VJ, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, Mendes LW, van Ijcken WFJ, Gomez-Exposito R, Elsayed SS, Mohanraju P, Arifah A, van der Oost J, Paulson JN, Mendes R, van Wezel GP, Medema MH, Raaijmakers JM (2019) Pathogen-induced activation of disease-suppressive functions in the endo- phytic root microbiome. Science 366:606–612. https:// doi. org/ 10. 1126/ scien ce. aaw92 85 Chakraborty S, Old K (1982) Mycophagous soil amoeba: inter- actions with three plant pathogenic fungi. Soil Biol Bio- chem 14:247–255. https:// doi. org/ 10. 1016/ 0038- 0717(82) 90034-7 Cui Z, Li P, Gao G, Kan H, Du Z, Zhang F, Sun M, Li X (2017) Effect of wheat yellow mosaic viral disease on biological traits and grain yield of different wheat culti- vars. J Triticeae Crops (in Chinese with English abstract) 37:1175–1180 Decroës A, Mahillon M, Genard M, Lienard C, Lima Men- dez G, Gilmer D, Bragard C, Legreve A (2022) Rhi- zomania: hide and seek of Polymyxa betae and the Beet necrotic yellow vein virus with Beta vulgaris. Mol Plant Microbe  Interact. https:// doi. org/ 10. 1094/ MPMI- 03- 22- 0063-R Delgado-Baquerizo M, Reith F, Dennis PG, Hamonts K, Pow- ell JR, Young A, Singh BK, Bissett A (2018) Ecological drivers of soil microbial diversity and soil biological net- works in the Southern Hemisphere. Ecology 99:583–596. https:// doi. org/ 10. 1002/ ecy. 2137 Depoorter E, Bull MJ, Peeters C, Coenye T, Vandamme P, Mahenthiralingmam E (2016) Burkholderia: an update on taxonomy and biotechnological potential as antibiotic producers. Appl Microbiol Biotechnol 100:5215–5229. https:// doi. org/ 10. 1007/ s00253- 016- 7520-x Deng Y, Jiang YH, Yang Y, He Z, Luo F, Zhou J (2012) Molec- ular ecological network analyses. BMC Bioinform 13:113. https:// doi. org/ 10. 1186/ 1471- 2105- 13- 113 https://doi.org/10.1111/j.1744-7348.1991.tb05649.x https://doi.org/10.1111/j.1744-7348.1991.tb05649.x https://doi.org/10.1111/j.1744-7348.1994.tb04946.x https://doi.org/10.1111/j.1744-7348.1994.tb04946.x https://doi.org/10.1371/journal.pbio.1002352 https://doi.org/10.3389/fpls.2013.00165 https://doi.org/10.3389/fpls.2013.00165 https://doi.org/10.1038/ismej.2011.119 https://doi.org/10.1038/ismej.2011.119 https://doi.org/10.1111/j.2517-6161.1995.tb02031.x https://doi.org/10.1111/j.2517-6161.1995.tb02031.x https://doi.org/10.1186/s40168-018-0470-z https://doi.org/10.1186/s40168-018-0470-z https://doi.org/10.1038/s41587-019-0209-9 https://doi.org/10.15302/j-fase-2020323 https://doi.org/10.15302/j-fase-2020323 https://doi.org/10.1094/MPMI-21-10-1349 https://doi.org/10.1038/nmeth.3869 https://doi.org/10.1126/science.aaw9285 https://doi.org/10.1126/science.aaw9285 https://doi.org/10.1016/0038-0717(82)90034-7 https://doi.org/10.1016/0038-0717(82)90034-7 https://doi.org/10.1094/MPMI-03-22-0063-R https://doi.org/10.1094/MPMI-03-22-0063-R https://doi.org/10.1002/ecy.2137 https://doi.org/10.1007/s00253-016-7520-x https://doi.org/10.1186/1471-2105-13-113 345Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) Diao A, Chen J, Ye R, Zheng T, Yu S, Antoniw JF, Adams MJ (1999) Complete sequence and genome properties of chi- nese wheat mosaic virus, a new furovirus from China. J Gen Virol 80(Pt 5):1141–1145. https:// doi. org/ 10. 1099/ 0022- 1317- 80-5- 1141 Dixon P (2003) VEGAN, a package of R functions for com- munity ecology. J Veg Sci 14:927–930. https:// doi. org/ 10. 1111/j. 1654- 1103. 2003. tb022 28.x Dumack K, Fiore-Donno AM, Bass D, Bonkowski M (2020) Making sense of environmental sequencing data: ecologi- cally important functional traits of the protistan groups Cercozoa and Endomyxa (Rhizaria). Mol Ecol Resour 20:398–403. https:// doi. org/ 10. 1111/ 1755- 0998. 13112 Duran P, Thiergart T, Garrido-Oter R, Agler M, Kemen E, Schulze-Lefert P, Hacquard S (2018) Microbial interking- dom interactions in roots promote Arabidopsis survival. Cell 175:973-983e914. https:// doi. org/ 10. 1016/j. cell. 2018. 10. 020 Essarioui A, LeBlanc N, Kistler HC, Kinkel LL (2017) Plant community richness mediates inhibitory interactions and resource competition between Streptomyces and Fusarium populations in the rhizosphere. Microb Ecol 74:157–167. https:// doi. org/ 10. 1007/ s00248- 016- 0907-5 Fan KK, Delgado-Baquerizo M, Guo XS, Wang DZ, Zhu YG, Chu HY (2021) Biodiversity of key-stone phylotypes determines crop production in a 4-decade fertilization experiment. ISME J 15:550–561. https:// doi. org/ 10. 1038/ s41396- 020- 00796-8 Fernandez-Gonzalez AJ, Cardoni M, Gomez-Lama Cabanas C, Valverde-Corredor A, Villadas PJ, Fernandez-Lopez M, Mercado-Blanco J (2020) Linking belowground microbial network changes to different tolerance level towards Verti- cillium wilt of olive. Microbiome 8:11. https:// doi. org/ 10. 1186/ s40168- 020- 0787-2 Fournier B, Steiner M, Brochet X, Degrune F, Mammeri J, Carvalho DL, Siliceo SL, Bacher S, Peña-Reyes CA, Heger TJ (2022) Toward the use of protists as bioindica- tors of multiple stresses in agricultural soils: a case study in vineyard ecosystems. Ecol Indic 139:108955. https:// doi. org/ 10. 1016/j. ecoli nd. 2022. 108955 Fu L, Xiong W, Dini-andreote F, Wang B, Tao C, Ruan Y, Shen Z, Li R, Shen Q (2020) Changes in bulk soil affect the disease-suppressive rhizosphere microbiome against Fusarium wilt disease. Front Agric Sci Eng 7:307–316. https:// doi. org/ 10. 15302/j- fase- 20203 28 Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A (2019) Protists: Puppet masters of the rhizosphere microbiome. Trends Plant Sci 24:165–176. https:// doi. org/ 10. 1016/j. tplan ts. 2018. 10. 011 Geisen S, Koller R, Hunninghaus M, Dumack K, Urich T, Bonkowski M (2016) The soil food web revisited: diverse and widespread mycophagous soil protists. Soil Biol Biochem 94:10–18. https:// doi. org/ 10. 1016/j. soilb io. 2015. 11. 010 Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, Fernández LD, Jousset A, Krashevska V, Singer D, Spiegel FW, Walochnik J, Lara E (2018) Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev 42:293–323. https:// doi. org/ 10. 1093/ fem- sre/ fuy006 Grace JB, Keeley JE (2006) A structural equation model analysis of postfire plant diversity in California shrublands. Ecol Appl 16:503–514. https:// doi. org/ 10. 1890/ 1051- 0761(2006) 016[0503: asemao] 2.0. co;2 Groemping U (2006) Relative importance for linear regres- sion in R: the package relaimpo. J Stat Softw 17:1–27. https:// doi. org/ 10. 18637/ jss. v017. i01 Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, Boutte C, Burgaud G, de Vargas C, Decelle J, Del Campo J, Dolan JR, Dunthorn M, Edvardsen B, Holz- mann M, Kooistra WH, Lara E, Le Bescot N, Logares R, Mahe F, Massana R, Montresor M, Morard R, Not F, Pawlowski J, Probert I, Sauvadet AL, Siano R, Stoeck T, Vaulot D, Zimmermann P, Christen R (2013) The Protist Ribosomal reference database (PR2): a catalog of uni- cellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res 41:D597-604. https:// doi. org/ 10. 1093/ nar/ gks11 60 Gullner G, Komives T, Király L, Schröder P (2018) Glu- tathione S-transferase enzymes in plant-pathogen inter- actions. Front Plant Sci 9:1836. https:// doi. org/ 10. 3389/ fpls. 2018. 01836 Guo LM, He J, Li J, Chen JP, Zhang HM (2019) Chinese wheat mosaic virus: a long-term threat to wheat in China. J Integr Agric 18:821–829. https:// doi. org/ 10. 1016/ S2095- 3119(18) 62047-7 Guo S, Tao CY, Jousset A, Xiong W, Wang Z, Shen ZZ, Wang BB, Xu ZH, Gao ZL, Liu SS, Li R, Ruan YZ, Shen QR, Kowalchuk GA, Geisen S (2022) Trophic interactions between predatory protists and pathogen- suppressive bacteria impact plant health. ISME J, 1–12. https:// doi. org/ 10. 1038/ s41396- 022- 01244-5 Guo S, Xiong W, Hang X, Gao Z, Jiao Z, Liu H, Mo Y, Zhang N, Kowalchuk GA, Li R, Shen Q, Geisen S (2021) Protists as main indicators and determinants of plant performance. Microbiome 9:64. https:// doi. org/ 10. 1186/ s40168- 021- 01025-w Hassani MA, Duran P, Hacquard S (2018) Microbial inter- actions within the plant holobiont. Microbiome 6:58. https:// doi. org/ 10. 1186/ s40168- 018- 0445-0 He L, Jin P, Chen X, Zhang TY, Zhong KL, Liu P, Chen JP, Yang J (2021) Comparative proteomic analysis of Nico- tiana benthamiana plants under chinese wheat mosaic virus infection. BMC Plant Biol 21:51. https:// doi. org/ 10. 1186/ s12870- 021- 02826-9 Huang X, Wang JJ, Dumack K, Liu WP, Zhang QC, He Y, Di HJ, Bonkowski M, Xu JM, Li Y (2021) Protists modu- late fungal community assembly in paddy soils across climatic zones at the continental scale. Soil Biol Bio- chem 160:108358. ARTN 108358. https:// doi. org/ 10. 1016/j. soilb io. 2021. 108358 ISSS, ISRIC FAO (1998) World reference base for soil resources, Wageningen/Rome, 1–68 Jiao S, Chen W, Wang J, Du N, Li Q, Wei G (2018) Soil micro- biomes with distinct assemblies through vertical soil pro- files drive the cycling of multiple nutrients in reforested ecosystems. Microbiome 6:146. https:// doi. org/ 10. 1186/ s40168- 018- 0526-0 Jousset A (2017) Application of protists to improve plant growth in sustainable agriculture. In: Mehnaz S (ed) Rhizotrophs: plant growth promotion to bioremediation. Springer, Singapore, pp 263–273 https://doi.org/10.1099/0022-1317-80-5-1141 https://doi.org/10.1099/0022-1317-80-5-1141 https://doi.org/10.1111/j.1654-1103.2003.tb02228.x https://doi.org/10.1111/j.1654-1103.2003.tb02228.x https://doi.org/10.1111/1755-0998.13112 https://doi.org/10.1016/j.cell.2018.10.020 https://doi.org/10.1016/j.cell.2018.10.020 https://doi.org/10.1007/s00248-016-0907-5 https://doi.org/10.1038/s41396-020-00796-8 https://doi.org/10.1038/s41396-020-00796-8 https://doi.org/10.1186/s40168-020-0787-2 https://doi.org/10.1186/s40168-020-0787-2 https://doi.org/10.1016/j.ecolind.2022.108955 https://doi.org/10.1016/j.ecolind.2022.108955 https://doi.org/10.15302/j-fase-2020328 https://doi.org/10.1016/j.tplants.2018.10.011 https://doi.org/10.1016/j.tplants.2018.10.011 https://doi.org/10.1016/j.soilbio.2015.11.010 https://doi.org/10.1093/femsre/fuy006 https://doi.org/10.1093/femsre/fuy006 https://doi.org/10.1890/1051-0761(2006)016[0503:asemao]2.0.co;2 https://doi.org/10.1890/1051-0761(2006)016[0503:asemao]2.0.co;2 https://doi.org/10.18637/jss.v017.i01 https://doi.org/10.1093/nar/gks1160 https://doi.org/10.3389/fpls.2018.01836 https://doi.org/10.3389/fpls.2018.01836 https://doi.org/10.1016/S2095-3119(18)62047-7 https://doi.org/10.1016/S2095-3119(18)62047-7 https://doi.org/10.1038/s41396-022-01244-5 https://doi.org/10.1186/s40168-021-01025-w https://doi.org/10.1186/s40168-021-01025-w https://doi.org/10.1186/s40168-018-0445-0 https://doi.org/10.1186/s12870-021-02826-9 https://doi.org/10.1186/s12870-021-02826-9 https://doi.org/10.1016/j.soilbio.2021.108358 https://doi.org/10.1016/j.soilbio.2021.108358 https://doi.org/10.1186/s40168-018-0526-0 https://doi.org/10.1186/s40168-018-0526-0 346 Plant Soil (2023) 485:333–347 1 3 Vol:. (1234567890) Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in perfor- mance and usability. Mol Biol Evol 30:772–780. https:// doi. org/ 10. 1093/ molbev/ mst010 Kojima H, Nishio Z, Kobayashi F, Saito M, Sasaya T, Kiribu- chi-Otobe C, Seki M, Oda S, Nakamura T (2015) Identi- fication and validation of a quantitative trait locus associ- ated with wheat yellow mosaic virus pathotype I resistance in a japanese wheat variety. Plant Breed 134:373–378. https:// doi. org/ 10. 1111/ pbr. 12279 Kramer S, Dibbern D, Moll J, Huenninghaus M, Koller R, Krueger D, Marhan S, Urich T, Wubet T, Bonkowski M, Buscot F, Lueders T, Kandeler E (2016) Resource parti- tioning between bacteria, fungi, and protists in the detri- tusphere of an agricultural soil. Front Microbiol 7:1524. https:// doi. org/ 10. 3389/ fmicb. 2016. 01524 Krome K, Rosenberg K, Dickler C, Kreuzer K, Ludwig-Muller J, Ullrich-Eberius C, Scheu S, Bonkowski M (2010) Soil bacteria and protozoa affect root branching via effects on the auxin and cytokinin balance in plants. Plant Soil 328:191–201. https:// doi. org/ 10. 1007/ s11104- 009- 0101-3 Langfelder P, Horvath S (2012) Fast R functions for robust cor- relations and hierarchical clustering. J Stat Softw 46:1–17 Liu C, Suzuki T, Mishina K, Habekuss A, Ziegler A, Li C, Sakuma S, Chen G, Pourkheirandish M, Komat- suda T (2016) Wheat yellow mosaic virus resistance in wheat cultivar madsen acts in roots but not in leaves. J Gen Plant Pathol 82:261–267. https:// doi. org/ 10. 1007/ s10327- 016- 0674-7 Liu H, Li J, Carvalhais LC, Percy CD, Prakash Verma J, Schenk PM, Singh BK (2021) Evidence for the plant recruitment of beneficial microbes to suppress soil-borne pathogens. New Phytol 229:2873–2885. https:// doi. org/ 10. 1111/ nph. 17057 Manici LM, Caputo F (2009) Fungal community diversity and soil health in intensive potato cropping systems of the east Po valley, northern Italy. Ann Appl Biol 155:245–258. https:// doi. org/ 10. 1111/j. 1744- 7348. 2009. 00335.x McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSan- tis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P (2012) An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618. https:// doi. org/ 10. 1038/ ismej. 2011. 139 Mendes R, Garbeva P, Raaijmakers JM (2013) The rhizosphere microbiome: significance of plant beneficial, plant patho- genic, and human pathogenic microorganisms. FEMS Microbiol Rev 37:634–663. https:// doi. org/ 10. 1111/ 1574- 6976. 12028 Nikoljuk V (1969) Some aspects of the study of soil protozoa. Acta Protozool 7:1–37 Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Olivier G, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830 Price MN, Dehal PS, Arkin AP (2010) FastTree 2 - approxi- mately maximum-likelihood trees for large alignments. PLoS ONE 5. https:// doi. org/ 10. 1371/ journ. pone. 00094 90 Sanguin H, Sarniguet A, Gazengel K, Moenne-Loccoz Y, Grundmann GL (2009) Rhizosphere bacterial communities associated with disease suppressiveness stages of take-all decline in wheat monoculture. New Phytol 184:694–707. https:// doi. org/ 10. 1111/j. 1469- 8137. 2009. 03010.x Sapp M, Ploch S, Fiore-Donno AM, Bonkowski M, Rose LE (2018) Protists are an integral part of the Arabidop- sis thaliana microbiome. Environ Microbiol 20:30–43. https:// doi. org/ 10. 1111/ 1462- 2920. 13941 Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C (2011) Metagenomic biomarker dis- covery and explanation. Genome Biol 12:R60. https:// doi. org/ 10. 1186/ gb- 2011- 12-6- r60 Shi Y, Li Y, Xiang X, Sun R, Yang T, He D, Zhang K, Ni Y, Zhu YG, Adams JM, Chu H (2018) Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain. Microbiome 6:27. https:// doi. org/ 10. 1186/ s40168- 018- 0409-4 Singh BK, Trivedi P (2017) Microbiome and the future for food and nutrient security. Microb Biotechnol 10:50–53. https:// doi. org/ 10. 1111/ 1751- 7915. 12592 Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner HW, Richards TA (2010) Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol 19(Suppl 1):21–31. https:// doi. org/ 10. 1111/j. 1365- 294X. 2009. 04480.x Van der Heijden MGA, Hartmann M (2016) Networking in the plant microbiome. PLoS Biol 14:e1002378 Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, Gilbert JA, Jansson JK, Caporaso JG, Fuhr- man JA, Apprill A, Knight R (2016) Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community sur- veys. Msystems 1:e00009-00015. https:// doi. org/ 10. 1128/ mSyst ems. 00009- 15 Wang E, He D, Zhao Z, Smith CJ, Macdonald BC (2020) Using a systems modeling approach to improve soil management and soil quality. Front Agr Sci Eng 7(3):289–295. https:// doi. org/ 10. 15302/J- FASE- 20203 37 Wang H, Wu C, Zhang H, Xiao M, Ge T, Zhou Z, Liu Y, Peng S, Peng P, Chen J (2022) Characterization of the below- ground microbial community and co-occurrence networks of tobacco plants infected with bacterial wilt disease. World J Microbiol Biotechnol 38:155. https:// doi. org/ 10. 1007/ s11274- 022- 03347-9 Wen T, Yuan J, He X, Lin Y, Huang Q, Shen Q (2020) Enrich- ment of beneficial cucumber rhizosphere microbes medi- ated by organic acid secretion. Hortic Res 7:154. https:// doi. org/ 10. 1038/ s41438- 020- 00380-3 Wen T, Zhao M, Yuan J, Kowalchuk GA, Shen Q (2021) Root exudates mediate plant defense against foliar pathogens by recruiting beneficial microbes. Soil Ecol Lett 3:42–51. https:// doi. org/ 10. 1007/ s42832- 020- 0057-z Wu B, Jiang S, Zhang M, Wang S, Zhao J, Xin X (2017) Resistance of wheat cultivars to wheat yellow mosaic virus in shandong. J Triticeae Crops (in Chinese with English abstract) 37:332–336. https:// doi. org/ 10. 7606/j. issn. 1009- 1041 Wu W, Lu HP, Sastri A, Yeh YC, Gong GC, Chou WC, Hsieh CH (2018) Contrasting the relative importance of species https://doi.org/10.1093/molbev/mst010 https://doi.org/10.1093/molbev/mst010 https://doi.org/10.1111/pbr.12279 https://doi.org/10.3389/fmicb.2016.01524 https://doi.org/10.1007/s11104-009-0101-3 https://doi.org/10.1007/s10327-016-0674-7 https://doi.org/10.1007/s10327-016-0674-7 https://doi.org/10.1111/nph.17057 https://doi.org/10.1111/nph.17057 https://doi.org/10.1111/j.1744-7348.2009.00335.x https://doi.org/10.1038/ismej.2011.139 https://doi.org/10.1038/ismej.2011.139 https://doi.org/10.1111/1574-6976.12028 https://doi.org/10.1111/1574-6976.12028 https://doi.org/10.1371/journ.pone.0009490 https://doi.org/10.1371/journ.pone.0009490 https://doi.org/10.1111/j.1469-8137.2009.03010.x https://doi.org/10.1111/j.1469-8137.2009.03010.x https://doi.org/10.1111/1462-2920.13941 https://doi.org/10.1186/gb-2011-12-6-r60 https://doi.org/10.1186/gb-2011-12-6-r60 https://doi.org/10.1186/s40168-018-0409-4 https://doi.org/10.1186/s40168-018-0409-4 https://doi.org/10.1111/1751-7915.12592 https://doi.org/10.1111/j.1365-294X.2009.04480.x https://doi.org/10.1111/j.1365-294X.2009.04480.x https://doi.org/10.1128/mSystems.00009-15 https://doi.org/10.1128/mSystems.00009-15 https://doi.org/10.15302/J-FASE-2020337 https://doi.org/10.15302/J-FASE-2020337 https://doi.org/10.1007/s11274-022-03347-9 https://doi.org/10.1007/s11274-022-03347-9 https://doi.org/10.1038/s41438-020-00380-3 https://doi.org/10.1038/s41438-020-00380-3 https://doi.org/10.1007/s42832-020-0057-z https://doi.org/10.7606/j.issn.1009-1041 https://doi.org/10.7606/j.issn.1009-1041 347Plant Soil (2023) 485:333–347 1 3 Vol.: (0123456789) sorting and dispersal limitation in shaping marine bacte- rial versus protist communities. ISME J 12(2):485–494. https:// doi. org/ 10. 1038/ ismej. 2017. 183 Wu CF, Wang FY, Ge AH, Zhang HQ, Chen GX, Deng YW, Yang J, Chen JP, Ge TD (2021a) Enrichment of microbial taxa after the onset of wheat yellow mosaic disease. Agric Ecosyst Environ 322:107651. ARTN 107651. https:// doi. org/ 10. 1016/j. agee. 2021. 107651 Wu CF, Wang FY, Zhang HQ, Chen GX, Deng YW, Chen JP, Yang J, Ge TD (2021b) Enrichment of beneficial rhizo- sphere microbes in chinese wheat yellow mosaic virus- resistant cultivars. Appl Microbiol Biotechnol 105:9371– 9383. https:// doi. org/ 10. 1007/ s00253- 021- 11666-4 Xiong C, Zhu YG, Wang JT, Singh B, Han LL, Shen JP, Li PP, Wang GB, Wu CF, Ge AH, Zhang LM, He JZ (2021) Host selection shapes crop microbiome assembly and network complexity. New Phytol 229:1091–1104. https:// doi. org/ 10. 1111/ nph. 16890 Xiong W, Li R, Guo S, Karlsson I, Jiao ZX, Xun WB, Kowal- chuk GA, Shen QR, Geisen S (2019) Microbial amend- ments alter protist communities within the soil microbi- ome. Soil Biol Biochem 135:379–382. https:// doi. org/ 10. 1016/j. soilb io. 2019. 05. 025 Xiong W, Song Y, Yang K, Gu Y, Wei Z, Kowalchuk GA, Xu Y, Jousset A, Shen Q, Geisen S (2020) Rhizosphere pro- tists are key determinants of plant health. Microbiome 8:27. https:// doi. org/ 10. 1186/ s40168- 020- 00799-9 Xu Y, Hu L, Li L, Zhang Y, Sun B, Meng X, Zhu T, Sun Z, Hong G, Chen Y, Yan F, Yang J, Li J, Chen J (2018) Ribotypes of Polymyxa graminis in wheat samples infected with soilborne wheat viruses in china. Plant Dis 102:948– 954. https:// doi. org/ 10. 1094/ PDIS- 09- 17- 1394- RE Ye R, Zheng T, Chen J, Diao A, Adams MJ, Yu S, Antoniw JF (1999) Characterization and partial sequence of a new furovirus of wheat in China. Plant Pathol 48:379–387. https:// doi. org/ 10. 1046/j. 1365- 3059. 1999. 00358.x Zhao ZB, He JZ, Geisen S, Han LL, Wang JT, Shen JP, Wei WX, Fang YT, Li PP, Zhang LM (2019) Protist com- munities are more sensitive to nitrogen fertilization than other microorganisms in diverse agricultural soils. Microbiome 7:1–16.  ARTN 33.  https:// doi. org/ 10. 1186/ s40168- 019- 0647-0 Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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/10.1038/ismej.2017.183 https://doi.org/10.1016/j.agee.2021.107651 https://doi.org/10.1016/j.agee.2021.107651 https://doi.org/10.1007/s00253-021-11666-4 https://doi.org/10.1111/nph.16890 https://doi.org/10.1111/nph.16890 https://doi.org/10.1016/j.soilbio.2019.05.025 https://doi.org/10.1016/j.soilbio.2019.05.025 https://doi.org/10.1186/s40168-020-00799-9 https://doi.org/10.1094/PDIS-09-17-1394-RE https://doi.org/10.1046/j.1365-3059.1999.00358.x https://doi.org/10.1186/s40168-019-0647-0 https://doi.org/10.1186/s40168-019-0647-0 Phagotrophic protist-mediated control of Polymyxa graminis in the wheat rhizosphere Abstract Purpose Methods Results Conclusion Introduction Materials and methods Field site, plant material, and sampling DNA extraction, quantitative PCR analysis, and amplicon sequencing Bioinformatics analysis Statistical analysis Results Rhizosphere soil physiochemical properties and polymyxa graminis abundance The relationship between Polymyxa graminis abundance and microbial diversity in rhizosphere soil Ecological module linking of P. graminis abundance in multitrophic networks Underlying drivers of P. graminis abundance Discussion Sensitivity of protistan communities and other microbes to wheat cultivar during disease onset The important ecological role of phagotrophic taxa in the co-occurrence network Role of phagotrophic protists toward P. graminis abundance Acknowledgements Anchor 23 References