Rhizosphere Cercozoa reflect the physiological response of wheat plants to salinity stress

Highlights

	● Plant salinity stress index correlates with rhizosphere Cercozoa.
	● Salinity stress alleviation promotes predation of rhizosphere Cercozoa.
	● Cercomonas strain inoculation assists alleviation of salinity stress.

Abstract

Protists are essential components of the rhizosphere microbiome, which is crucial for plant growth, but little is known about the relationship between plant growth and rhizosphere protists under salinity stress. Here we investigated wheat (Triticum aestivum L.) rhizosphere protistan communities under naturally occurring salinity (NOS) and irrigation-reduced salinity (IRS), and linked a plant salinity stress index (PSSI) to different protistan groups in a nontidal coastal saline soil. We found that the PSSI was significantly correlated with rhizosphere cercozoan communities (including bacterivores, eukaryvores, and omnivores) and that these communities were important predictors of the PSSI. Structural equation modeling suggested that root exudation-induced change in bacterial community composition affected the communities of bacterivorous and omnivorous Cercozoa, which were significantly associated with the PSSI across wheat cultivars. Network analysis indicated more complex connections between rhizosphere bacteria and their protistan predators under IRS than under NOS, implying that alleviation of salinity stress promotes the predation of specific cercozoans on bacteria in rhizospheres. Moreover, the Cercomonas directa inoculation was conducive to alleviation of salinity stress. Taken together, these results suggest that the physiological response of wheat plants to salinity stress is closely linked to rhizosphere Cercozoa through trophic regulation within the rhizosphere microbiome.

Graphical abstract

Keywords

plant growth / soil salinity / rhizosphere microbiome / trophic interactions / protists / Cercozoa.

1 Introduction

The rhizosphere microbiome, known as the “second genome of plants,” is crucial for plant growth, health, and productivity (Berendsen et al., 2012; Trivedi et al., 2020). Protists, which are essential components of the rhizosphere microbiome, are increasingly being recognized as key determinants of plant growth (Geisen et al., 2018; Gao et al., 2019; Oliverio et al., 2020). Protists may play central roles in stimulating plant growth through microbiome interactions (Guo et al., 2021; Hawxhurst et al., 2023). Previous studies have found that rhizosphere protists contribute greatly to plant growth and health under biotic stresses, such as soil-borne pathogen infection (Xiong et al., 2020; Guo et al., 2022). Little is known, however, about the role of rhizosphere protists in boosting plant growth under abiotic stresses.

Soil salinity is a major abiotic stress that suppresses plant growth and productivity (Munns and Tester, 2008). Soil salinization removes approximately 1.5 million hectares of cropland from production and decreases production potential by up to 46 million hectares per year globally (FAO, 2021). Salinity-affected agricultural soils are increasing by as much as 10% per year (McFarlane et al., 2016). Plants may drive the rhizosphere bacterial community reassembly by altering root exudation patterns (Zhalnina et al., 2018). Plants are likely to recruit specific subsets of the bacterial consortium that enhance their adaptability to salinity stress (Li et al., 2021). Soil protists, particularly predatory protists (formerly known as protozoa), digest bacterial consortia, releasing nitrogen and carbon into the soil. This process accelerates the microbial loop and promotes plant growth (Geisen et al., 2018). Therefore, disentangling relationships between plant physiological traits and rhizosphere protists is crucial for harnessing the rhizosphere microbiome to improve crop tolerance and adaptability to salinity stress.

Salinity stress significantly reduces the abundance and richness of predatory protists, leading to shifts in protistan community composition in marine ecosystems (Ali et al., 2024). As a major factor influencing protistan community structure, salinity prompts these organisms to form cysts or adjust their osmotic pressure through “salt-in” and “salt-out” strategies to cope with elevated salt levels (Czech and Bremer, 2018). Notably, protistan communities differ markedly between soil and marine environments: soil protists are predominantly cercozoans, while marine protists are mainly radiolarians (Singer et al., 2021). This divergence suggests that protists exhibit differential responses to salinity stress depending on their habitat. While extensive research has explored the responses of marine protists to salinity stress, such as enhanced ion transport, stress responses, and increased prey capture capabilities, potentially acquired through lateral gene transfer from bacterial prey (Czech and Bremer, 2018; Ji et al., 2024). However, studies examining the effects of salinity stress on soil protists remain relatively scarce.

The Yellow River Delta in China is one of the most active deltas in the world, with significant land–ocean interactions. This region has experienced significant salinization as a result of both natural and artificial factors, including its location proximity to the coast, as well as land management practices and irrigation methods (Chi et al., 2018; Wu et al., 2019). Wheat (Triticum aestivum L.) is one of the most important crops planted in this area. In this study, we conducted field and glasshouse experiments to investigate wheat rhizosphere protistan communities under two types of salinity stress in the Yellow River Delta and explore relationships between plant physiological traits and different protistan groups. Given inextricable connections among plant growth, rhizosphere bacteria, and predatory protists, we hypothesize that specific predatory protists reflect the physiological response of wheat plants to salinity stress. To our knowledge, this is the first report of rhizosphere protists associated with crop plant growth under salinity stress.

2 Materials and methods

2.1 Field experiment

A field experiment was carried out at the Shandong Dongying Institute of Geographic Sciences of the Chinese Academy of Sciences, Dongying, China (37°40′ N, 118°54′ E). The site, located in the Yellow River Delta, experiences an annual precipitation of 550–600 mm and an annual average temperature of 12.9 °C. The experimental soil, a nontidal coastal saline soil with a sandy loam texture, was classified as a Marinic Aqui-Orthic Halosol according to the Chinese Soil Taxonomy (Cooperative Research Group on Chinese Soil Taxonomy, 2001). The soil was subjected to two types of salinity stresses: naturally occurring salinity (hereafter referred to as NOS) and irrigation-reduced salinity (hereafter referred to as IRS). The NOS treatment was irrigated using the local lightly-saline water, while the IRS treatment mitigated the salinity by irrigation using fresh water from the Yellow River. The two treatments received irrigation with the same amount of water. During the wheat growing season, fertilizer was applied at the following rates: 270 kg N ha^–1, 118 kg P ha^–1, and 224 kg K ha^–1.

Nineteen samples of soils and plants under NOS or IRS were collected from seven 1200 m long × 60 m wide fields of wheat cultivar Xiaoyan_60. For each sample, nine subsamples from a 90–100 m long × 50–60 m wide plot were aggregated into one composite sample, and five wheat plants were extracted from each 5-m diameter subsampling area (Fig. S1). The loose bulk soils were collected by thoroughly shaking roots, and the rhizosphere soils tightly adhering to root surfaces were obtained by carefully brushing roots (Angst et al., 2018; Fan et al., 2021). Bulk soils were used to measure soil chemical properties, and rhizosphere soils were used for DNA isolation and amplicon sequencing. Wheat plants were used to determine plant physiological traits. Details of the soil and plant analyses were available in the supporting information.

2.2 Wheat cultivar experiment

To better understand the effects of salinity stress on plant physiological traits and rhizosphere protistan communities across different wheat cultivars, we established a more detailed analysis of the relationships between salinity stress, plant growth, and protistan communities. Here we designed a glasshouse experiment using the NOS and IRS soils and four wheat cultivars (Jinan_177, Shanrong_3, Xiaoyan_6, and Yanjian_14). These wheat cultivars are the main cultivars planted in the Yellow River Delta. The experiment included eight treatments (2 salinity stresses × 4 wheat cultivars), with eight replicates each. One kilogram of air-dried soil uniformly supplemented with 104 mg N, 45 mg P, and 86 mg K (equivalent to field application rates) was added to each pot. Wheat seeds were surface sterilized by washing in 30% H₂O₂ and then germinated under sterile conditions for 1 week. Similar-sized seedlings were transplanted into the pots (two seedlings per pot). All 64 pots were placed in a controlled glasshouse under 25 °C/14-h light (intensity 3500 lx) and 18 °C/10-h dark conditions and 70% relative humidity. Plants were watered uniformly using sterile deionized water as needed and harvested at the jointing stage. Rhizosphere soils tightly adhering to root surfaces were collected by carefully brushing roots. Determinations of plant physiological traits and analyses of rhizosphere protistan and bacterial communities were carried out as described above.

Rhizosphere metabolites were extracted from 50 mg of rhizosphere soils using 1 mL of extractant [methanol:acetonitrile:water = 2:2:1 (v/v/v)], freeze dried, and dissolved with solvent [acetonitrile:water = 1:1 (v/v)]. Non-target metabolomes were analyzed using an Acquity I-Class PLUS ultra-high-performance liquid chromatography (UPLC) system combined with a Xevo G2-XS QTOF high-resolution mass spectrometer (MS) (Waters, Milford, MA, USA). An Acquity UPLC HSS T3 column (1.8 μm, 2.1 × 100 mm) (Waters) was used for the UPLC analysis. The MS data were collected using MassLynx software and then processed using Progenesis QI software. Metabolites were identified using the METLIN database in Progenesis QI. We next removed metabolites likely produced from microbial turnover based on available databases and libraries, thereby retaining those metabolites mainly derived from root exudation. All metabolite data were normalized and standardized prior to statistical analysis.

2.3 Cercozoan inoculation experiment

We further conducted the second glasshouse experiment with a cercozoan strain inoculation to explore the direct link between predatory Cercozoa and salinity stress alleviation. The NOS and IRS soils and two wheat cultivars (Shanrong_3 and Xiaoyan_6) were used. The experiment included eight treatments (2 salinity stresses × 2 wheat cultivars × with/without Cercomonas directa inoculation), with four replicates each. The Cercomonas directa strain was obtained by following the steps below. 1.0 g of rhizosphere soils were suspended in 20 mL of phosphate buffered saline, and 1 μL of soil suspension was mixed with 9 μL of Escherichia coli-OP50 (1.0×10⁷ cells E. coli) and 90 μL of 1/300 tryptic soytone broth (TSB) in 96-well plates and incubated at 15 °C. The Cercomonas directa strain was isolated using multiple 1/300 TSB incubation. The Cercomonas directa strain was observed using an inverted biological microscope (Mshot, Guangzhou, China), and identified using the primers 1813F (5′-CTGCGTGAGAGGTGAAAT-3′)/2646R (5′-GCTACCTTGTTACGACTTT-3′). Pure culture of the Cercomonas directa strain was completed by adding 100 μL of E. coli and 10 mL of 1/300 TSB. The Cercomonas directa strain was inoculated into the rhizospheres at a level of 1.0×10² cells g^–1 dry soil at the seedling stage, and the uninoculated treatments were given as the controls. The planting operations and control conditions of glasshouse experiment were the same as the first glasshouse experiment. Plants were harvested at the tillering stage, and rhizosphere soils were collected. Plant physiological traits and the rhizosphere protistan and bacterial communities were analyzed as described above.

2.4 Statistical analysis

Differences in soil and plant properties, the richness and relative abundance of different protistan groups, and the relative intensity of rhizosphere metabolites between NOS and IRS were analyzed by independent-sample t-tests. Community differentiation of different protistan groups between NOS and IRS was assessed by permutational multivariate analysis of variance (PERMANOVA) using the vegan package. The effects of salinity and cultivar on rhizosphere metabolite composition were also analyzed by PERMANOVA. Specific protists responsible for community differentiation were selected by differential abundance analysis with a likelihood ratio test using the edgeR package. We normalized OTU sequence counts using the ‘trimmed means of M’ method and expressed the normalized counts in counts per million (Hartman et al., 2018). We adjusted P-values for multiple testing using false discovery rate (FDR) correction (Benjamini and Yekutieli, 2001). Protists with relative abundance fold-changes > 2.0 or < –2.0 (FDR-corrected P < 0.01) were selected as differentially abundant protists. The normality of the data was checked by the Shapiro–Wilk test, and non-normal data were log or log (x+1) transformed (McDonald, 2009).

Correlations between the plant salinity stress index (PSSI) and the community composition of different protistan groups and between protistan and bacterial community compositions were determined by Mantel testing. We used random forest analysis to determine key protistan groups for predicting the PSSI. Random forest modeling was carried out using the randomForest package, and the significance of each variable and all variables collectively was calculated using the rfPermute and A3 packages, respectively. FDR-corrected Spearman’s rank correlations, calculated using the psych package, were used to link the PSSI with rhizosphere metabolite composition, bacterial community composition, and cercozoan groups. Structural equation modeling (SEM) was used to quantitatively assess direct and indirect relationships among relevant variables. The SEM analysis was performed using the maximum likelihood estimation method. Model fitness was examined by a non-significant Chi-square (${\chi }^2$) test (P > 0.05), and the optimal model with the minimum critical ratio (${\chi }^2$/df < 3.0), high goodness-of-fit index (GFI) (GFI > 0.9), and low root mean square error of approximation (RMSEA) (RMSEA < 0.08) was established (Hooper et al., 2008). Dominant bacterivorous Cercozoa, omnivorous Cercozoa, and bacterial taxa, i.e., those with relative abundances of more than 0.1% at the genus level, were selected for network analysis. To avoid spurious correlations that arise in compositional data, we corrected the compositional data by centered log-ratio transformation (Quinn et al., 2018). Significant correlations (Spearman’s r > 0.7 and FDR-corrected P < 0.01) were used for network construction. Network topological characteristics were calculated using the igraph package. Differences in plant physiological traits between treatments with and without Cercomonas directa inoculation were analyzed by independent-sample t-tests. Specific rhizosphere protists and bacteria enriched by the Cercomonas directa inoculation were identified by differential abundance analysis, and Spearman’s rank correlations between the PSSI and the richness and relative abundance of these enriched taxa were determined. All statistical analyses were performed in the R (v. 4.2.2).

3 Results and discussion

Water soluble salt content and electrical conductivity significantly decreased under IRS compared with NOS (Table S1), which indicates that soil salinity had been mitigated through fresh water irrigation. Although there was a marginal but significant difference in soil pH between the NOS and IRS, the reduction of pH was much smaller than that of salinity (almost threefold reduction of electrical conductivity or water-soluble salt content) under the IRS compared with the NOS (Table S1). Accordingly, the mitigation of salinity rather than alkalinity (or pH) plays a substantial role in protistan community succession. Plants had a higher aboveground biomass but a lower leaf Na⁺ accumulation or Na⁺:K⁺ ratio under IRS relative to NOS, and plant physiological response to salinity stress was concisely reflected in the PSSI between NOS and IRS (P < 0.001) (Table S1). We found that Cercozoa was the most diverse and abundant group of rhizosphere protists, especially under IRS (Fig.1 and Fig.1). Most Cercozoa taxa were phagotrophs, which overwhelmed other functional groups regarding both richness and relative abundance (Fig.1 and Fig.1). Distinct taxonomic (Amoebozoa and Cercozoa), functional group (parasites and phagotrophs), and cercozoan (bacterivores, eukaryvores, and omnivores) communities were observed between NOS and IRS (Table S2). We identified specific sets of protists responsible for the community differentiation between NOS and IRS and found that 21.7% and 69.2% of these protists belonged to Cercozoa under the two respective treatments (Fig.1 and S2). These results imply that the distribution patterns of protistan communities are closely related to salinity stress and that mitigation of salinity stress is favorable for the enrichment of Cercozoa in the rhizosphere. Cercozoa is an extremely diverse group that includes nearly all trophic levels and ecologically important functional traits (Dumack et al., 2020). Many members of Cercozoa are phagotrophs, such as heterotrophic free-living flagellates and amoebae (Burki and Keeling, 2014). Cercozoa has previously been identified as a major group of rhizosphere protists in agricultural systems (Sapp et al., 2018; Degrune et al., 2019; Rossmann et al., 2020; Sun et al., 2021). We have thus broadened understanding of the dominance of Cercozoa among rhizosphere protists in stressed agroecosystems. Rhizosphere cercozoans can therefore be useful bioindicators of saline conditions in agricultural soils.

Fig.1 (A–B) Species richness (A) and relative abundance (B) of major protists assigned to different taxonomic and functional groups. *, **, and *** indicate significant differences between naturally occurring salinity (NOS) and irrigation-reduced salinity (IRS) conditions at probability levels of 0.05, 0.01, and 0.001, respectively. (C) Specific sets of differentially abundant protists enriched in rhizospheres under IRS (green triangles) relative to NOS (orange triangles). (D–E) Spearman’s rank correlations between the plant salinity stress index and rhizosphere Cercozoa community similarity (D) and species richness (E). Similarity = 1 – Bray-Curtis dissimilarity. ** and *** denote significance correlations at probability levels of 0.01 and 0.001, respectively. (F) Random forest modeling to determine important predictors of the plant salinity stress index, with the y-axis representing the percentage of the increase in mean square error (% IncMSE). The community composition of protists, phagotrophs, and Cercozoa is represented by the first principal coordinate of corresponding taxa, and significant predictors at probability levels of 0.05 and 0.01 are labeled with * and **, respectively.

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The PSSI was highly correlated (P < 0.01) with the community composition of total Cercozoa, bacterivorous Cercozoa, eukaryvorous Cercozoa, and omnivorous Cercozoa, as well as their richness (except eukaryvorous Cercozoa) (Fig.1 and Fig.1). The PSSI was also highly correlated (P < 0.01) with the relative abundance of total Cercozoa and omnivorous Cercozoa (Fig. S3A). Bacterial community composition was significantly correlated (P < 0.001) with the community composition of bacterivorous Cercozoa and omnivorous Cercozoa (Fig. S3B). Random forest modeling revealed that the community composition and richness of bacterivorous Cercozoa and omnivorous Cercozoa could well predict the PSSI (Fig.1). Taken together, these results suggest the existence of an intimate relationship between plant growth and rhizosphere bacteria and their protistan predators under salinity stress.

The first glasshouse experiment demonstrated that soil salinity strongly (P < 0.001) affected plant physiological traits, except for leaf K⁺ content, across wheat cultivars (Fig. S4). Soil salinity strongly affected the richness and relative abundance of total Cercozoa, bacterivorous Cercozoa, and omnivorous Cercozoa (except for the relative abundance of bacterivorous Cercozoa), and these variables were significantly promoted under IRS compared with NOS, independent of wheat cultivar (Fig. S5). Soil salinity and wheat cultivar had prominent effects on rhizosphere metabolite composition (PERMANOVA, P < 0.001) (Fig.2). In terms of overall or individual wheat cultivars, the PSSI was significantly correlated (FDR-corrected P < 0.05) with rhizosphere metabolite composition, bacterial community composition, and the community composition and richness of bacterivorous Cercozoa (mainly Allapsidae, Paracercomonas, and Sandona) and omnivorous Cercozoa (mainly Cercomonas, Eocercomonas, and Rhogostoma) (Fig.2–Fig.2). Further, SEM suggested that root exudation-induced change in bacterial community composition affected the communities of bacterivorous and omnivorous Cercozoa, which were significantly associated with the PSSI across wheat cultivars (Fig.2). Plant tolerance to salinity stress modulates patterns of root exudation (Badri and Vivanco, 2009; Dodd and Pérez-Alfocea, 2012). Root exudation drives the rhizosphere bacterial community assembly (Chen et al., 2016; Zhalnina et al., 2018), which further mediates specific protistan groups by bottom-up trophic regulation (Gao et al., 2019; Thakur and Geisen, 2019). Previous investigations have revealed similar predator–prey relationships of phyllosphere Cercozoa and bacteria (Flues et al., 2017; Sun et al., 2021). Some members of Paracercomonas and Sandona (naked flagellates) can affect the soil bacterial community via grazing (Howe et al., 2009; Sapp et al., 2018). Many members of Cercomonas and Eocercomonas (naked ameboflagellates) are able to feed on most bacteria and, to some extent, fungi and algae in soils (Geisen et al., 2016; Dumack et al., 2020). We therefore speculate that plant growth probably shapes the community structure of rhizosphere bacteria and their protistan predators by modulating beneficial bacteria patterns under salinity stress. Guo et al. (2022) found that inoculation of predatory protists (e.g., Cercomonas lenta) not only increases the relative density of fungal-suppressive Bacillus spp. and the fitness of other beneficial bacteria, but also reduces the density of the pathogenic Fusarium oxysporum in the rhizosphere. Rhizosphere cercozoans probably facilitate wheat plant growth under salinity stress via predating pathogenic microbes. This speculation needs to be further explored. Network analysis has been widely used to indicate potential inter-kingdom interactions at multitrophic levels (Morriën et al., 2017; Rossmann et al., 2020; Sun et al., 2021). In our study, network analysis indicated more complex connections (e.g., number of edges, network density, average number of neighbors, and edges linking omnivores to bacteria) between rhizosphere bacteria and their protistan predators under IRS than under NOS (Fig.3–Fig.3, Table S3). The results imply that alleviation of salinity stress promotes the predation of specific cercozoans on bacteria in rhizospheres. We observed a slight increase in the percentage of positive edges under IRS relative to NOS (Fig.3), thus indicating that alleviation of salinity stress is likely to facilitate the proliferation of rhizosphere cercozoans and bacteria through predator–prey relationships. Presumably, specific predatory cercozoans, in turn, may modulate salinity stress response in wheat plants. To test this, we conducted the second glasshouse experiment using the wheat cultivars Shanrong_3 and Xiaoyan_6 with inoculation of the Cercomonas directa strain (Fig. S6). The first glasshouse experiment has shown that the PSSI of the two cultivars significantly correlated with the richness or relative abundance of Cercomonas (Fig.2). The Cercomonasdirecta inoculation had a positive effect on aboveground biomass and negative effects on leaf Na⁺ accumulation and PSSI, especially for the wheat cultivar Shanrong_3 under IRS (Fig.4–Fig.4). Many members of rhizosphere Cercozoa and Bacteroidota were enriched by the Cercomonasdirecta inoculation, and the PSSI was negatively correlated with the richness or relative abundance of these members (Figs. S7 and 4F–I). These results confirmed that specific predatory cercozoans are able to modulate salinity stress response in wheat plants. Our findings collectively suggest that plant physiological response to salinity stress is intimately associated with rhizosphere Cercozoa, most likely through trophic regulation within the rhizosphere microbiome. Nevertheless, relationships between plants and plant-associated microbiomes are dynamic and highly complex (Shi et al., 2016; Fierer, 2017). In-depth investigations will be required to reveal how crop plants affect rhizosphere protistan communities and how their variations in turn influence plant growth under saline conditions. The present study has provided support for future attempt to promote the potential of rhizosphere protists to improve crop tolerance or adaptability to salinity stress.

Fig.2 (A) Relative intensity of major rhizosphere metabolites. *, **, and *** indicate significant differences between naturally occurring salinity (NOS) and irrigation-reduced salinity (IRS) conditions at probability levels of 0.05, 0.01, and 0.001, respectively. The effects of salinity and cultivar on rhizosphere metabolite composition were analyzed by permutational multivariate analysis of variance (PERMANOVA). (B–D) Spearman’s rank correlations (false discovery rate corrected) of the plant salinity stress index with rhizosphere metabolite composition, bacterial community composition, and cercozoan groups (B) and with the richness and relative abundance (RA) of bacterivorous Cercozoa (C) and omnivorous Cercozoa (D). (E) Structural equation modeling revealing direct and indirect relationships among relevant variables. Red and blue arrows indicate significant positive and negative relationships, respectively. Numbers beside arrows are standardized path coefficients, and *, **, and *** denote significant relationships at probability levels of 0.05, 0.01, and 0.001, respectively.

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Fig.3 (A–B) Co-occurrence networks of bacterivorous Cercozoa, omnivorous Cercozoa, and bacteria in rhizospheres under naturally occurring salinity (NOS) (A) and irrigation-reduced salinity (IRS) (B). The size of each node is proportional to the degree (number of edges) of the corresponding node, and the width of each edge is proportional to the correlation weight of the corresponding edge. Gray and blue edges indicate positive and negative correlations, respectively. (C–D) Number and percentage of nodes assigned to bacterivores, omnivores, and bacteria (C) and edges linking bacterivores to bacteria, edges linking omnivores to bacteria, and other edges (D). (E) Percentage of positive and negative edges.

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Fig.4 (A–E) Aboveground biomass (A), leaf Na+ content (B), leaf K+ content (C), leaf Na+: K+ ratio (D), and plant salinity stress index (E) of wheat cultivars Shanrong_3 (SR) and Xiaoyan_6 (XY) under naturally occurring salinity (NOS) and irrigation-reduced salinity (IRS). The treatments with and without Cercomonas directa inoculation are distinguished by green and orange colors, respectively. * indicates significant differences for the treatments with and without inoculation at a probability level of 0.05. (F–G) Species richness of specific protistan groups (F) and bacterial phyla (G) enriched by the Cercomonas directa inoculation in the SR and XY rhizospheres. These taxa were identified by differential abundance analysis based on a likelihood ratio test (P < 0.01, false discovery rate corrected). (H–I) Spearman’s rank correlations of the plant salinity stress index with the richness of total Cercozoa, bacterivorous Cercozoa, and omnivorous Cercozoa (H) and total bacteria and Bacteroidota (I) enriched by the Cercomonas directa inoculation. * denotes significant correlations at a probability level of 0.05.

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Acknowledgements

The authors are grateful to the staff from the Shandong Dongying Institute of Geographic Sciences of the Chinese Academy of Sciences for the assistance of sample collection. This study was financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos. XDA24020104, XDA28110100, XDA28020203), the National Key R&D Program of China (Grant Nos. 2022YFD1500203, 2022YFD1500401), the China Agriculture Research System (Grant Nos. CARS-03, CARS-52), the National Natural Science Foundation of China (Grant No. 42177332), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2023325).

Conflict of interest statement

The authors declare no conflicts of interest.

Data availability statement

All sequence data from the field and glasshouse experiments have been deposited in the National Center of Biotechnology Information (NCBI) Sequence Read Archive under accession number PRJNA961219.

Electronic supplementary material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s42832-024-0268-9 and is accessible for authorized users.

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