The soil protists are affected by rhizocompartment and wheat variety, and co-occur with prokaryotes

The soil protists are affected by rhizocompartment and wheat variety, and co-occur with prokaryotes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The soil protists are affected by rhizocompartment and wheat variety, and co-occur with prokaryotes Christine Lorenzen Elberg, Rumakanta Sapkota, Athanasios Zervas, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7381449/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims: Soil protist communities and their interaction with prokaryotes in the rhizocompartment influence plant growth. However, the drivers of protist diversity and their co-occurrence with prokaryotic communities in dynamic rhizocompartments and between wheat varieties are not well understood. We hypothesized that rhizocompartment and wheat varieties, differing in root structure and pathogen resistance, impact protist community structure and diversity. Additionally, the co-occurrence of prokaryotes and protists was hypothesized to depend on wheat varieties selecting for different key protist-prokaryote interactions. Methods: We studied the protist community composition of four wheat varieties in three rhizocompartments: rhizoplane, rhizosphere, and bulk soil, and their co-occurrence with prokaryote communities. In soil DNA from a greenhouse pot experiment, protist abundance was determined using qPCR, and community composition was described by metabarcoding of 18S rRNA and 16S rRNA genes. Results: Protist community structure and abundance were significantly affected by the rhizocompartment and wheat varieties. Protist richness increased with distance from the root surface. Protist abundance was higher in the rhizocompartments of the Rembrandt wheat variety, while amoeba abundance was lower in the Kvium variety. Colpodea was more abundant in the rhizosphere, and Filosa-Sarcomonadea in the rhizoplane, compared to bulk soil. A co-occurrence network analysis showed an intricate network with more nodes in the bulk soil. Conclusion: Rhizocompartment and wheat variety drive protist communities, consistent with the drivers of prokaryotic communities, demonstrating the interconnectivity of protist-prokaryotic interactions in the soil rhizosphere. 18S rRNA qPCR co-occurrence network predictor species bacteria microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Plant roots host diverse soil microbes, and the rhizosphere is a hot spot for soil microbial activity. Carbon-rich root exudates excreted by the root serve as a major energy source, driving the high activity and diversity of microorganisms in the rhizosphere (Nguyen, 2003; Bonkowski, 2004; Geisen et al., 2018). In addition, root exudates along with the root network also facilitate surface areas for microbial attachment and biofilm formation (Fujishige, Kapadia and Hirsch, 2006). Soil microorganisms influence plant performance and fitness with effects that range from beneficial and neutral to detrimental/pathogenic ways (Hayat et al., 2010; Nadeem et al., 2014; Jung et al., 2020). For instance, plant pathogenic bacteria and fungi can reduce crop health and productivity through the production of toxins (Oerke and Dehne, 2004), whereas mycorrhizal fungi improve plant growth in P-limited soils by improving nutrient acquisition (Koide, 1991). Similarly, root colonizing bacteria have been shown to promote root growth, increasing nutrient uptake and plant growth (Olanrewaju, Glick and Babalola, 2017). In the important wheat crop, prokaryotic and fungal microbiomes were found to vary across year, rhizocompartment, and growth stage, and less so to wheat variety (Quiza et al. 2023). Simonin et al. (2020) also found wheat genotype to have less influence on the rhizosphere microbiome, while soil type and agricultural practices had larger effects. While prokaryotes and fungi are widely studied in the plant holobiont, other organisms, most notably protists, are less studied, despite playing important roles in soil and rhizosphere environments. Protists cover the unicellular eukaryotic microorganisms that are not plants, animals, or fungi, making them a paraphyletic group (Schlegel and Hulsmann, 2007; Burki, 2014) across highly diverse groups (Bullerwell and Gray, 2004). Protists are highly abundant in soil and range over multiple functional groups, including free-living and often phototrophic to pathogenic/parasitic or predatory organisms (Oliverio et al., 2020). Notably, predatory protists dominate soil protist communities with abundances of 43%–52% of the protist community (Wang et al., 2023). Through predation on prokaryotes and fungi, protists influence the soil microbial community composition and nutrient cycling, thus also affecting plant health and growth (Geisen et al., 2018; Gao et al., 2019; Guo et al., 2021). Despite the widely recognized ecological role of protists, our understanding of their community compositions and their interactions with prokaryotes in different rhizocompartments (bulk soil, rhizosphere, rhizoplane) is limited. Recently, protist co-occurrence networks from arid soil in Chile identified Rhogostoma, Euplotes, and Neobodo as key species (Acosta, Nitsche and Arndt, 2024). Simonin et al. (2020), studying core wheat rhizosphere microbiomes across Africa and Europe, identified 177 core taxa, including 31 protists. Rossmann et al. (2020) found wheat rhizosphere microbial networks to vary depending on wheat cultivar and connectedness among certain cercozoan to bacteria and fungi. Based on these observations, we hypothesize that in addition to wheat rhizosphere core protists, rhizocompartment and wheat varieties with different root morphology and structures, coupled with varying plant pathogen resistance profiles, impact the protist community structure and diversity, and that the co-occurrence of prokaryotes and protists depends on the wheat varieties selecting for different key protist-prokaryote interactions. To test this, we (i) characterized differences in protist communities between three different rhizocompartments of four different wheat varieties, and (ii) identified key protist players and their associations in the rhizocompartments and the four different wheat varieties using co-occurrence network analysis. Materials and Methods 2.1 Experimental design, plant growth, and soil sampling A plant growth experiment was performed as earlier described by Zervas et al. (2022). In brief, four varieties (cv.s Sheriff, Heerup, Kvium, and Rembrandt) of winter wheat ( Triticum aestivum L.) (Sejet Plant Breeding, Denmark) were grown in 24 x 7 cm PVC pots filled with sandy loam soil (0 – 25 cm) mixed with sand (DANSAND; filter sand no. 2) in a 2:1 ratio. The varieties were high-yielding with some resistance to above-ground fungal pathogens (SortInfo) and showed different resistance to common pathogens with Sheriff being resistant to Septoria , mildew, and yellow rust, Kvium having good resistance against mildew and yellow rust, Rembrandt having a high resistance against Septoria , and Heerup having a high resistance to mildew (Sejet, 2017; Jørgensen, 2020; Sejet, 2020, 2021a, b; Jørgensen, 2023). Further, Rembrandt showed a lower root density than the other varieties (K. Thorup-Kristensen pers. com). The soil was a sandy loam collected from the plough layer (0-25 cm) in the University of Copenhagen’s Long Term Nutrient Depletion Experiment located at Højbakkegård in Høje Taastrup, Denmark (55°40’ N, 12°17’ E) (van der Bom et al., 2018, 2017). The soil was collected from a treatment that had received mineral NPK fertilizer at a rate of 120 kg nitrogen, 20 kg phosphorus, and 120 kg potassium ha -1 y -1 for the past 25 years and consisted of 170 g kg −1 clay, 174 g kg −1 silt, 362 g kg −1 fine sand, 255 g kg −1 coarse sand and 40 g kg −1 organic matter. The soil had a pH CaCl2 of 5.4, an Olsen-P content of 11.4 mg kg -1 , and a water holding capacity (WHC) of 33 %. The soil was air-dried and sieved to 8 mm before the experiment. Each pot contained one plant, and 420 plants were grown under controlled greenhouse conditions at the University of Copenhagen, Denmark, in the fall of 2020. A vernalization period with temperatures of 6 °C/4°C day/night and a light intensity of 150 µmol m 2 s -1 was applied 3 weeks after sowing for 12 weeks (week 4-15). Before (week 1-3) and after (week 16-20) vernalization the plants were grown at 19°C day/15°C night; 16 h day/8 h night; light intensity 300 µmol m 2 s -1 . Pots were regularly rotated to ensure randomness and watered to ca. 70 % WHC by weighing. All plant heights were scored by harvest, and the pots with the five shortest and five tallest plants were sampled for further experiments to cover the range of plant growth. Material from three rhizocompartments with different distances to the root of the plant was sampled; i) the plant was removed from the pot and the remaining soil was labeled bulk soil compartment; ii) the rhizosphere compartment was collected by hand shaking the plant roots inside a falcon tube containing 25 ml of sterile water; iii) the rhizoplane compartment was collected by sonication in an ultrasonic bath and further shaking of the washed roots after transfer to a new falcon tube containing 25 ml of sterile water. Sonication is often used for opening cells before DNA extraction, and hence, the protist cells might have been damaged. As the samples were kept frozen or freeze-dried until DNA extraction, the sonication is not considered to affect the DNA extraction efficiency. All samples were flash-frozen, freeze-dried, transported to Aarhus University, Roskilde, Denmark, and kept at 4˚C until extraction. In total, 120 samples were used for DNA extraction. 2.2 Soil microbial DNA extraction, library preparation, sequencing, and quantification The microbial DNA used in this study was extracted earlier by Zervas et al. (2022) using the NucleoBond RNA Soil Mini Kit (item no. 740142.50) with its DNA co-extraction accompanying set (item no. 740143.50) (Macherey-Nagel, Germany), and DNA concentrations were measured on Qubit 4 (Thermo-Fisher Scientific) using the 1xdsDNA High-Sensitivity assay. The extracted DNA was stored at -20 o C until metabarcoding and qPCR of protists. 18S rRNA gene metabarcoding was performed using the 1380F and 1510R primer set (Amaral-Zettler et al., 2009) (TAG Copenhagen A/S) targeting the V9 region of the protist 18S rRNA gene using a 2-step PCR. PCR amplification was done in technical duplicates in a BioRad T100 Thermal Cycler using the following PCR reagents: 12.5 µl Ultra Mix polymerase (PCRBIOSYSTEMS), 0.5 µl forward primer, 0.5 µl reverse primer, 6 µl nuclease-free water, 0.5 µl BSA (BIORON, Germany), and 5 µl template DNA (1-10ng/µl). After initial denaturation at 95˚C for 2 min, the PCR program of 33 cycles of 95˚C for 15 s, 55˚C for 15 s, and 72˚C for 40 s was run with a final elongation step of 72˚C for 4 min. The PCR product from technical duplicates was pooled. A second PCR was performed using 5 µl of pooled product from the previous PCR amplification and barcodes for Illumina sequencing using a combination of dual indexing primers. The PCR program included an initial denaturation temperature of 98˚C for 1 min, then 13 cycles of 98˚C for 10 s, 55˚C for 20 s, and 72˚C for 40 s followed by a final elongation step of 72˚C for 4 min. After each PCR run, 1.5% agarose gels were used to verify successful amplification. After the second PCR, clean-up was performed using the HighPrep PCR Clean-up System (MAGBIO, USA). DNA concentrations were measured using the High-Sensitivity DNA assay, Qubit 4.0 (Thermo-Fischer Scientific). Finally, DNA of each sample was pooled equimolarly and quantified on Qubit. Before sequencing, the library was also inspected on a 4150 Tapestation (Agilent Technologies, Waldbronn, Germany) using D1000 reagents and screentape. Finally, the library was sequenced on an Illumina Nextseq (Environmental Science Sequencing Center, Aarhus University) using the v2.5 chemistry 300-cycles kit in 151 bp pair-end mode. Protist sequence files are deposited in the NCBI sequence read archive under the SRA accession number PRJNA1079549. The 16S rRNA amplicon data was obtained from Genbank Accession number PRJNA806868 (Zervas et al., 2022). 2.3 Quantification of protist abundance Protist abundance was quantified by qPCR using both general protist primers and amoeba-specific primers. The general protist primer set (1380F and 1510R) targeted the V9 region of the 18S rRNA gene of all eukaryotes (176 bp) (Amaral-Zettler et al., 2009) and the amoeba-specific 18S rRNA gene primer set (Amo_1400_F: and Amo_1540_R) (130 bp) (Le Calvez et al., 2012) (TAG Copenhagen A/S). Both reactions contained 1 µl DNA template (DNA concentration 1-10 ng/µl), 4.5 µl 5x HOT FIREPol® EvaGreen® qPCR Supermix (SOLIS BIODYNE), 1 µl forward primer (10 µM), 1 µl reverse primer (10 µM), and 12.5 µl nuclease-free water with a final volume of 20 µl pr sample. QPCR reactions of all samples were obtained by using the BioRad CFX Connect Real-Time System and the qPCR program: 95˚C for 10 min, 95˚C for 15 s, 60˚C for 15 s, and 72˚C for 45 cycles for the general primers and the program: 95˚C for 10 min, 95˚C for 15 s, 62˚C for 15 s, and 72˚C for 45 cycles for the amoeba-specific primers. The standard curves were produced using DNA extracted with the DNeasy PowerLyzer PowerSoil Kit (QIAGEN) from three cultured protists: Cercomonas sp (ATCC 50334), Tetrahymena pyriformis (ATCC 30005), and Acanthamoeba castellanii (ATCC 50373). DNA from all three organisms was amplified using the general primers, while A. castellanii DNA was also amplified using the amoeba-specific primers. Successful amplification of the individual protist’s DNA was tested by running 1.5% agarose gels of PCR products, and the band of the expected size was extracted using the QIAquick Gel Extraction Kit (Qiagen). The DNA concentration was measured on Qubit; the PCR products were pooled equimolarly and used for ten-fold serial dilutions. Two different standard curves were produced in each qPCR run, targeting all protists and amoebae, respectively, using the previously described thermal cycle program. 2.4 Bioinformatic Analysis of protist sequencing data was done using QIIME2 (v. 2021.8) (Bolyen et al., 2019) via DADA2 plugin (Callahan et al., 2016) using default parameters for the filtering, merging, checking of chimeras, dereplicating, and assigning ASVs. Taxonomy of representative sequences for each ASV was assigned using the PR2 database (v. 5.0) (Guillou et al., 2013). 2.5 Statistical analysis Microbial data were imported into R (v. 4.3.1) using phyloseq (v.1.40.0) for statistical analysis and data visualization (R Core Team, 2021) using ggplot2 package (Wickham, 2016). After taxonomy assignment, unassigned ASVs at the kingdom level were discarded. Further, ASVs assigned to fungi, Metazoa, Streptophyta, Rhodophyta, eukaryotic mitochondria, and plasmids from Gyrista, haptophyte, Stramenopiles, eukaryota mitochondria, and eukaryota plasmids were excluded. Alpha diversity was estimated using observed richness, Shannon index and Simpson indices, where the ASV table was thus rarified 100 times at a depth of 4019 reads for protists and 9417 reads for prokaryotes (sample with lowest number of reads), and then the mean of the diversity estimates of 100 trials was used. Alpha diversity was transformed to Hill numbers using richness (Hill number q = 0) and Shannon diversity (Hill number q = 1, calculated as the exponential of Shannon entropy) via the R package hilldiv ( Alberdi and Gilbert 2019a) . Hill numbers calculate the alpha diversity for DNA-based data and offer a more interpretable framework for biodiversity metrics ( Alberdi and Gilbert 2019b) . Beta diversity was estimated using Bray Curtis dissimilarity matrix and visualized using non-metric multidimensional scaling (NMDS). Permutational multivariate analysis of variance (PERMANOVA) was performed using ‘adonis2’ function from the R package vegan (v. 2.6-4). Pairwise comparison was obtained using the pairwise Adonis test using the ‘pairwiseAdonis’ function and the p values were adjusted using the Bonferroni correction. Further statistical analysis was carried out using the non-parametric Wilcoxon test, one-way ANOVA, and Kruskal-Wallis’s tests with the p values adjusted using the Bonferroni correction. Differential abundance analysis was performed using the DESeq2 (v. 1.36.0) package with a significant threshold set at p< 0.01 to identify differentially abundant microbial taxa across wheat rhizocompartment and wheat variety. Low-abundant taxa were filtered before using the model. To identify top protist taxa that predict the effect of rhizocompartment and wheat variety, we employed Random Forest (RF) classification using the ‘RandomForest’ (v. 4.7-1.1) package with a prune scale of 0.0001 and 500 trees. Further, using RF model, we identified the top protist predictors ASVs and ranked them based on Mean Decrease in Gini index. Also, we selected top predictors ASVs and examined their relative abundance across treatments. Co-occurrence network analysis was performed on all samples using Spearman’s correlation as described earlier (Sapkota et al., 2020). In brief, Spearman’s correlation was calculated on all ASV pairs using the ‘rcorr’ function from the Hmisc package (v. 5.0-1) and the edgeR (v. 3.38.4). Any significant correlation (p<0.001) and with a correlation coefficient either above 0.6 or below -0.6 was used for the network analysis. Network visualizations were done using the igraph (v. 1.4.2) package in R. Species within the top five percent of node degree values were identified and described as connector species in the interactions. Results 3.1 Data Characteristics Samples were obtained from three different rhizocompartments (rhizoplane, rhizosphere, and bulk soil) of the five tallest and the five shortest plants of four different wheat varieties (Heerup, Kvium, Rembrandt, and Sheriff), resulting in a total of 120 samples. Protist amplicons resulted in 3,525,264 reads after quality control. After the taxonomy assignment, we found that primers used in this study also amplify fungi, metazoans, Gyrista plasmids, haptophyte plasmids, Stramenopiles plasmids, Streptophyta, Rhodophyta, eukaryotic mitochondria, and eukaryotic plasmids. These groups, along with unassigned ASVs at the kingdom level, were trimmed from the dataset. Further, we removed two samples with fewer than 3,000 reads. The final number of reads was 1,743,167 with an average of 14,773 per sample, covering 5,037 protist ASVs, which were the basis of further analysis. 3.2 Abundance of protists Real-time qPCR with two different primer sets targeting amoebae and overall protist community, respectively, showed significant differences in protist and amoebae copy number between the wheat varieties (Figure 1), but not between rhizocompartments nor plant height (data not shown) when tested by one-way ANOVA. Furthermore, pairwise comparison among varieties identified significantly higher protist copy numbers in Rembrandt compared to Kvium and Sheriff. The amoebae copy numbers were significantly lower in Kvium compared to the other three varieties, while Rembrandt had a lower copy number than Heerup. 3.3 Drivers of protist and prokaryote community composition Table 1: Significant differences (PERMANOVA) between community structure of protists from rhizocompartments, plant height, and wheat varieties. Dataset: Df SS R 2 P Rhizocompartment 2 2.1267 0.0773 0.001 Plant height 1 0.4395 0.0160 0.001 Variety 3 1.9376 0.0704 0.001 A non-metric multidimensional scaling (NMDS) plot of the protist amplicon sequencing data revealed a separate clustering of the bulk soil samples (Supplementary Figure 1). Significant differences were found between the protist community diversity of the different rhizocompartments and wheat varieties (Table 1). Despite the proximity of rhizocompartments in the pot experiment, the rhizocompartment explained 7.7% of the variance, while the wheat variety explained 7.0% of the variance, respectively. Bulk soils harbor a higher number of protist ASVs (HillQ0) and have a higher alpha diversity measured by Shannon diversity (HillQ1) and inverted Simpson indices (HillQ2) compared to rhizosphere and rhizoplane, irrespective of the wheat variety (Table 2). Furthermore, alpha diversity indices (HillQ0-2) were significantly different between wheat varieties (Table 2), while plant height correlated with observed richness (HillQ0) but not the other two Hill diversity measures (data not shown). Due to the low explanation of variance and no impact on Hill diversity indices, plant height was not considered a parameter in further testing. Similar diversity indexes of the prokaryotic community were determined based on data from Zervas et al. (2022) (Table 2). A similar pattern of significant differences between rhizocompartments and wheat varieties was found for prokaryotes. Table 2: Mean alpha diversity calculated as Hill numbers using species richness when q=0, Shannon diversity when q=1, and Simpson when q=2 of protists and prokaryotes. Statistically significant differences between alpha diversity are indicated using different letters (Kruskal-Wallis and pairwise Wilcox test). RP: rhizoplane; RS: rhizosphere; BS: bulk soil. 3.4 Protist community structure Cercozoa and Gyrista were the dominating protist subdivisions in all samples (Figure 2). All the subdivisions above 2% relative abundance were significantly different across rhizocompartments except for the Rotosphaerida (Suppl. Table 1). Chlorophyta was statistically relatively more abundant in bulk soil at the expense of the subdivisions Ciliophora and Discosea (Figure 2A). The dominating classes were Peronosporomycetes, Filosa-Thecofilosea, Filosa-Sarcomandea, and Colpodea (Suppl. Figure 2). Across wheat varieties, we found the relative abundance of the following subdivisions Bigyra, Centroplasthelida, Chlorophyta, Discosea, Evosea, Gyrista, Haptophyta, Ichthyosporea, and Tubulinea to be significantly different (Suppl. Table 1). Particularly, the protist community of the wheat variety Rembrandt differed from the other varieties by having a high relative abundance of the classes Peronosporomycetes and Nucleariidea with the latter showing <2% relative abundance in the other varieties (Suppl. Figure 2B). 3.5 Differential abundance analyses and random forest model Differences in the relative abundance of specific ASVs between rhizocompartments and wheat varieties were identified by differential abundance analyses (Suppl. Figure 3). Enrichment analysis of the rhizosphere revealed 15 ASVs being differentially abundant compared to bulk soil, with ASVs belonging to the classes Endomyxa-Phytomyxea, Chrysophycea, and Chlorophyceae being relatively more abundant in bulk soil, ASVs of Rhogostoma spp., Flabellinia, Colpodea, and Eumycetozoa being relatively more abundant in the rhizosphere, while two different Peronosporomycetes (oomycetes) ASVs were either less or more abundant in the rhizosphere (Suppl. Figure 3A). Similarly, when bulk soil and rhizoplane were analyzed, only two ASVs were significantly different (Suppl. Figure 3B), while there was no significant difference between rhizoplane and rhizosphere. Similarly, differential abundance analysis across wheat varieties showed that Heerup hosted a higher number of differentially abundant ASVs, especially Chlorophyseae ASVs, compared to Rembrandt (Supp. Fig. 3G), while only one or two ASVs were differentially abundant between the other combinations of wheat varieties (Suppl. Fig. 3C-F). By random forest modeling, we identified the ten most important ASVs contributing to the difference in the protist community based on the rhizocompartment and wheat variety (Suppl. Table 2, Suppl. Figure 4). Within the rhizocompartments (Suppl. Table 2A, Suppl. Figure 4A), two Filosa-Sarcomonadea were top predictors for rhizocompartment, showing the highest relative abundance in the rhizoplane, while two Colpodea ASVs followed as third and fifth predictors with the highest relative abundance in the rhizosphere. For the wheat varieties, five ASVs assigned as Peronosporomycetes (oomycetes) dominated the top ten list of predictors, followed by fewer differences between the varieties, four of which were most abundant in Rembrandt (Suppl. Table 2B, Suppl. Figure 4B). 3.6 Network analysis The major microbial players in the rhizocompartments were identified by a correlation-based co-occurrence network analysis. The 18S rRNA gene amplicon dataset of protists was combined with the 16S rRNA gene amplicon dataset of prokaryotes reported by Zervas et al. (2022). These two datasets are based on the same DNA extractions, extracted from the same soil samples. Hence, they are directly comparable. The 18S rRNA and the 16S rRNA ASVs were used as nodes and correlations as edges. Our network analysis revealed a closer and more intricate network with a higher degree of nodes in bulk soil than in rhizosphere and rhizoplane (Suppl. Table 3, Figure 3). Edges were primarily positive with a few negative (Figure 3). The prokaryotes with a higher number of connections in the bulk soil were identified as Gammaproteobacteria, Saccharimonadia, Alphaproteobacteria, and Acidobacteria, including Subgroup 6, while the protists with the highest connections were identified as Rhizaria. We also analyzed wheat co-occurrence networks based on wheat varieties (Figure 4) and found similar network properties consisting of one core hub network with positive correlations and a smaller hub with negative correlations. The Heerup variety microbiome had the highest number of nodes while Rembrandt had the lowest number of nodes (Suppl. Table 3). Rhizaria had a high number of nodes in Heerup, Stramenophiles had a high number of nodes in Rembrandt, Discosea had a high number of nodes in Sheriff, while Kvium had no dominating protist (Suppl. Table 4). Discussion We tested two hypotheses: i) rhizocompartment and wheat varieties impact the protist community structure and diversity, ii) the co-occurrence of prokaryotes and protists depends on the wheat varieties that select for different key protist-prokaryote interactions. A total of 120 soil samples from four different wheat varieties and three different rhizocompartments were analyzed by 18S rRNA gene metabarcoding targeting protists. In addition, protist abundance was quantified by qPCR targeting both general eukaryotes and amoebae. The driving ASVs of the different rhizocompartments and wheat varieties were tentatively identified by differential abundance analysis and Random Forest modeling. Further, co-occurrence network analyses between 16S rRNA and 18S rRNA gene amplicons identified prokaryotic and protist species co-occurring in the rhizocompartments and wheat varieties. 4.1. Rhizocompartments drive protist communities The overall community structure of protists was significantly different between the three rhizocompartments of bulk soil, rhizosphere, and rhizoplane, in agreement with previous studies (Guo et al., 2018; Fiore-Donno et al., 2019; Zhang et al., 2021) with protist communities of bulk soil separating from rhizosphere and rhizoplane. Across the rhizocompartments, the protist subdivisions Gyrista, Cercozoa, Ciliophora, and Chlorophyta dominated as previously shown in soil (Rossmann et al. 2020, Sandin and Jamy, 2021; Singer et al., 2021; Guan et al., 2023). Using protist-prokaryote co-occurrence networks, we found that the three rhizocompartments affect the overall complex microbial networks. We observed a higher number of nodes and closer and more intricate networks in bulk soil compared to the rhizosphere and rhizoplane. This supports earlier findings in soybean plants showing reduced interactions in the rhizosphere, likely the result of increased resources in the rhizosphere, leading to lower microbial diversity closer to the root, therefore supporting fewer interactions (Zhang et al., 2021). Co-occurrence networks are affected by multiple biases during the data processing, especially when combining two microbial amplicon datasets. Our study is based on DNA extracted from 0.5 g of soil, and despite the small volume, the co-occurrence of microorganisms does not explicitly guarantee that the organisms ever met or interacted. It has been shown that the variable region selected for sequencing will alter the microbial community and thereby affect the outcome of the network. Due to the paraphyletic nature of protists, no universal primers are available, and all primers will have limitations to their amplification (Hugerth et al., 2014; Zheng et al., 2022). Hence, when discussing co-occurrence networks, the origin of the scientific dataset and the ecology surrounding the samples should be considered when distinguishing predator-prey interactions and co-occurrence driven by the environment (Guseva et al., 2022). Our co-occurrence networks are solely based on correlations and reflect only co-occurrence driven by the environment. Despite these uncertainties, the networks can help us hypothesize on microbial interactions and guide us toward new insights into cross-kingdom interactions. 4.2 Wheat variety effects on protist and prokaryote community structure Wheat varieties hosted different protist abundance and communities, which might be due to the difference in pathogen resistance, exudate composition, and e.g., root morphology (Sejet, 2017; Jørgensen, 2020; Sejet, 2020, 2021a, b; Jørgensen, 2023). qPCR results showed that Kvium hosted significantly fewer amoebae copy numbers than the other wheat varieties, while Rembrandt hosted a higher protist gene copy number than the other varieties. Water use efficiency varies between wheat varieties (Hafeez et al., 2024) which could modify water content under similar conditions and affect protist community structure. Specifically, higher water content is reported to increase protist abundance (Geisen et al., 2014). It is known that broadly targeted primers might not only amplify related taxa, but also non-target taxa. Such non-target amplification is more common when we target diverse eukaryotic lineages. In our qPCR assays of protists and amoebae, it cannot be ruled out that the primers also captured other eukaryotic organisms. Despite such a lack of taxonomic specificity, the data still provides the approximation of abundance and allows comparative insights across treatments. In addition to different pathogen resistances, Heerup plants have been reported to have a higher average root diameter than Sheriff, which could result in relative differences in the abundance of bacterial groups (Kavamura et al., 2020; Guan et al., 2023). Particularly, Heerup and Rembrandt supported specific protist communities differing from each other and the other two varieties. For example, Spirotrichea had a higher relative abundance in Heerup than in both Sheriff and Rembrandt rhizocompartments according to the differential abundance analysis. By 16S rRNA gene amplicon sequencing, significant differences in prokaryotic communities between the wheat varieties were observed by Zervas et al. (2022). Using this data, we found alpha diversity measures of prokaryote communities to vary significantly between the wheat varieties, as found for protist communities. Plant roots and associated microbiome interactions are complex and multilayered, and thus effects on the prokaryote community could be linked to the changes in the predatory protist community (Shi et al., 2011; Rossmann et al. 2020; Zhao et al., 2021; Fiore-Donno et al., 2022) by selecting prokaryotic species with different palatability to protists. The selection of specific protist groups through prey availability might explain the differences in protist communities observed between varieties. Plant variety is a widely reported driver of microbial networks (Jiang et al. 2017, Quiza et al. 2023), and we found that wheat variety affects the protist-prokaryote co-occurrence networks, changing the number of positive and negative correlations. The highest number of nodes was found in the Heerup network, while the highest number of correlations was found in the Sheriff network. Such differences in the networks can be due to slight variations in the root physiology or root exudates, that, as mentioned above, can affect the water availability and the root exudation (Gaete et al., 2021). Connector protist species co-occurring with the bacteria were identified as belonging to the classes Discosea, Rhizaria, and Stramenophiles, which follows other studies (Zhang et al., 2021; Romdhane et al., 2022). 4.4 Predictor protists Certain protists, such as the commonly found testate amoebae of the Rhogostoma family, were detected in multiple of our analyses, suggesting their potential significance in the community structures. Based on the random forest modeling, this family is proposed to be a predictor species in Rembrandt samples compared to other wheat varieties. The presence of this group of omnivorous thecate amoebae is highly affected by factors like moisture, pH, and total plant biomass, suggesting that Rembrandt roots might provide good environmental conditions for this protist (Dumack et al., 2017; Oztoprak et al., 2020). Supporting this, two Rhogostoma spp. ASVs (eASV 4 and eASV 7) are found to be differentially abundant in samples closer to the root. However, two Rhogostoma spp. ASVs were more abundant in Heerup vs Rembrandt rhizocompartment. Acosta et al. (2024) similarly found Rhogostoma spp. in co-occurrence network analysis of soil and Simoni et al. (2020) found Rhogostoma spp. to be highly prevalent in wheat rhizosphere. Similarly, two ASVs belonging to the Group-Te orders were found to be more abundant in the rhizoplane compared to the bulk soil and were also identified as predictor ASV for the rhizoplane compartment. The Group-Te protist order has been reported earlier in the rhizosphere of wheat, Arabidopsis, and maize (Sapp et al., 2018; Taerum et al., 2022; Guan et al., 2023), and as core taxa with a high prevalence in wheat rhizosphere microbiome(Simonin et al., 2020). This supports the enrichment of Rhogostoma as well as Group-Te protists in specific rhizocompartments. Peronosporomycetes, also known as oomycetes, are a strong predictor in wheat varieties with six different ASVs among the top 10 of the random forest modeling. Four of these six ASVs were found to be most abundant in Rembrandt rhizocompartment which corresponds to the relatively high abundance of Peronosporomycetes in Rembrandt, and the finding of Peronosporomycetes as a connector species in the Rembrandt network. Peronosporomycetes are plant pathogens or saprophytes decaying organic matter and are widely abundant in soil (Sapkota and Nicolaisen, 2015; Sapkota and Nicolaisen, 2018). Confirming this, all six ASVs in question were found to be plant pathogens. Our findings indicate that the wheat variety Rembrandt host a relatively higher abundance of more pathogenic Peronosporomycetes than the other varieties. This difference might be due to differences in disease resistance. It would be relevant to further link plant performance and disease resistance to Peronosporomycetes abundance of the wheat varieties (Jørgensen, 2020, 2023). Conclusion In conclusion, we report rhizocompartment and wheat variety as factors affecting protist community structure with dynamics selecting for specific protists. Using qPCR and relative abundances, amoebae, especially of the family Rhogostoma and the class Peronosporomycetes were shown to vary between the four wheat varieties. Through co-occurrence network analyses possible interactions between prokaryotes and protists in the rhizocompartments of four wheat varieties were found, and connector protist species were identified from the subdivisions Discosea, Rhizaria, and Stramenophiles. This supports the hypothesis that predatory protists play a pivotal role in the structuring of the rhizosphere wheat microbiome. Our study contributes to the understanding of microbiome interactions in the soil and wheat rhizosphere. Declarations 7. Acknowledgements This study is part of the INTERACT project funded by the Novo Nordisk Foundation (Grant number: NNF19SA0059360). We thank Tina Thane and Tanja Begovic for their valuable assistance in the laboratory. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Christine Lorenzen Elberg, Rumakanta Sapkota, Athanasios Zervas, Dorette S. Müller-Stöver, Mette Haubjerg Nicolaisen, Rosanna Catherine Hennessy, and Anne Winding. The first draft of the manuscript was written by Christine Lorenzen Elberg and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability Protist sequence files are deposited in the NCBI sequence read archive under the SRA accession number PRJNA1079549. 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Müller-Stöver","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dorette","middleName":"S.","lastName":"Müller-Stöver","suffix":""},{"id":510636369,"identity":"b32dc7df-8812-41c9-83a8-8bbfbe53f0da","order_by":4,"name":"Mette Haubjerg Nicolaisen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mette","middleName":"Haubjerg","lastName":"Nicolaisen","suffix":""},{"id":510636370,"identity":"834a6874-3a2c-4637-983d-74992ccc3100","order_by":5,"name":"Rosanna Catherine Hennessy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rosanna","middleName":"Catherine","lastName":"Hennessy","suffix":""},{"id":510636371,"identity":"00d9c78d-4ba1-45d8-8211-19fee57b7418","order_by":6,"name":"Anne Winding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYFAC5gNwpmQDcVrYEkjWwmNAohb+GTkfHxe21QEZCYw3ZxCjReJG7mbjmW2HgYwEZssNRLnrdu42aZ4zBxgYbiSwST4gRof87Zznv3nO1DHIE63F4HYOGzNPBTODAUgLUQ4zvP/MWJqn4jCP4ZmHzZZEeV/uzOGHn3kM6uTkjicfvNlDjBYY4GFgYGwgRcMoGAWjYBSMAnwAAA4lMJ7BTcOSAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7491-4548","institution":"Aarhus University: Aarhus Universitet","correspondingAuthor":true,"prefix":"","firstName":"Anne","middleName":"","lastName":"Winding","suffix":""}],"badges":[],"createdAt":"2025-08-15 12:41:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7381449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7381449/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91099184,"identity":"c001eea7-9ae0-4b31-b23e-2472d8e92c4e","added_by":"auto","created_at":"2025-09-11 14:37:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287195,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance (qPCR) of protists and amoebae in combined rhizocompartments of the four wheat varieties. Significant differences were tested by one-way ANOVA and the \u003cem\u003ep\u003c/em\u003e values were adjusted using the Bonferroni correction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7381449/v1/bc6295db5abdff1fc486a9af.png"},{"id":91098741,"identity":"e1af80c9-302e-4d7e-933c-1abb8901088c","added_by":"auto","created_at":"2025-09-11 14:29:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":456381,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of protist communities (n=120). Top ten protists’ taxa at subdivision taxonomic level are shown while low abundant protists taxa are pooled together and shown as \u0026lt;2%.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7381449/v1/3b7465de4ab7b1236bb3dcf6.png"},{"id":91097580,"identity":"cde324a9-e9a8-462c-8598-f10cf1d23388","added_by":"auto","created_at":"2025-09-11 14:21:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2454830,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network of protists and prokaryotes in the three rhizocompartments. Nodes represent individual protists and bacterial taxa, while edge color signifies positive (gray) or negative (red) associations. Node size reflects the significance of the taxon.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7381449/v1/a2d09f9d9b5632b0b2daafe2.png"},{"id":91097577,"identity":"ac7c8fca-47fa-45f8-99ab-31d58e22498f","added_by":"auto","created_at":"2025-09-11 14:21:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2522406,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network of protists and prokaryotes in rhizocompartments of four wheat varieties. Nodes represent individual protists and bacterial taxa, while edge color signifies positive (gray) or negative (red) associations. Node size reflects the significance of the taxon.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7381449/v1/8a77adbaad28fb975f9520ca.png"},{"id":91969921,"identity":"7caa9a2f-5063-4cc2-ae4d-a44c4e12a337","added_by":"auto","created_at":"2025-09-23 08:59:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6798718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7381449/v1/9632dcf0-cea4-49f7-a6ce-56c49f58c4ae.pdf"},{"id":91097584,"identity":"ad346a1b-f149-4e7c-a1db-fc2782a1e49b","added_by":"auto","created_at":"2025-09-11 14:21:56","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":713288,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialR1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7381449/v1/0f19714c03b001baf7aa6ba6.docx"}],"financialInterests":"","formattedTitle":"The soil protists are affected by rhizocompartment and wheat variety, and co-occur with prokaryotes","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant roots host diverse soil microbes, and the rhizosphere is a hot spot for soil microbial activity. Carbon-rich root exudates excreted by the root serve as a major energy source, driving the high activity and diversity of microorganisms in the rhizosphere\u0026nbsp;(Nguyen, 2003; Bonkowski, 2004; Geisen et al., 2018). In addition, root exudates along with the root network also facilitate surface areas for microbial attachment and biofilm formation (Fujishige, Kapadia and Hirsch, 2006). Soil microorganisms influence plant performance and fitness with effects that range from beneficial and neutral to detrimental/pathogenic ways (Hayat et al., 2010; Nadeem et al., 2014; Jung et al., 2020). For instance, plant pathogenic bacteria and fungi can reduce crop health and productivity through the production of toxins (Oerke and Dehne, 2004), whereas mycorrhizal fungi improve plant growth in P-limited soils by improving nutrient acquisition (Koide, 1991). Similarly, root colonizing bacteria have been shown to promote root growth, increasing nutrient uptake and plant growth (Olanrewaju, Glick and Babalola, 2017).\u0026nbsp;In the important wheat crop, prokaryotic and fungal microbiomes were found to vary across year, rhizocompartment, and growth stage, and less so to wheat variety (Quiza et al. 2023). Simonin et al. (2020) also found wheat genotype to have less influence on the rhizosphere microbiome, while soil type and agricultural practices had larger effects. \u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc149146926\"\u003eWhile prokaryotes and fungi are widely studied in the plant holobiont, other organisms, most notably protists, are less studied, despite playing important roles in soil and rhizosphere environments. Protists cover the unicellular eukaryotic microorganisms that are not plants, animals, or fungi, making them a paraphyletic group\u0026nbsp;(Schlegel and Hulsmann, 2007; Burki, 2014) across highly diverse groups (Bullerwell and Gray, 2004). Protists are highly abundant in soil and range over multiple functional groups, including free-living and often phototrophic to pathogenic/parasitic or predatory organisms (Oliverio et al., 2020). Notably, predatory protists dominate soil protist communities with abundances of 43%\u0026ndash;52% of the protist community (Wang et al., 2023). Through predation on prokaryotes and fungi, protists influence the soil microbial community composition and nutrient cycling, thus also affecting plant health and growth (Geisen et al., 2018; Gao et al., 2019; Guo et al., 2021). Despite the widely recognized ecological role of protists, our understanding of their community compositions and their interactions with prokaryotes in different rhizocompartments (bulk soil, rhizosphere, rhizoplane) is limited. Recently, protist co-occurrence networks from arid soil in Chile identified \u003cem\u003eRhogostoma, Euplotes,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Neobodo\u003c/em\u003e as key species (Acosta, Nitsche and Arndt, 2024). Simonin et al. (2020), studying core wheat rhizosphere microbiomes across Africa and Europe, identified 177 core taxa, including 31 protists. Rossmann et al. (2020) found wheat rhizosphere microbial networks to vary depending on wheat cultivar and connectedness among certain cercozoan to bacteria and fungi. Based on these observations, we hypothesize that in addition to wheat rhizosphere core protists, rhizocompartment and wheat varieties with different root morphology and structures, coupled with varying plant pathogen resistance profiles, impact the protist community structure and diversity, and that the co-occurrence of prokaryotes and protists depends on the wheat varieties selecting for different key protist-prokaryote interactions. To test this, we (i) characterized differences in protist communities between three different rhizocompartments of four different wheat varieties, and (ii) identified key protist players and their associations in the rhizocompartments and the four different wheat varieties using co-occurrence network analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e2.1 Experimental design, plant growth, and soil sampling\u003c/p\u003e\n\u003cp id=\"_Toc149146931\"\u003eA plant growth experiment was performed as earlier described by Zervas et al. (2022). In brief, four varieties (cv.s Sheriff, Heerup, Kvium, and Rembrandt) of winter wheat (\u003cem\u003eTriticum aestivum \u003c/em\u003eL.) (Sejet Plant Breeding, Denmark) were grown in 24 x 7 cm PVC pots filled with sandy loam soil (0 \u0026ndash; 25 cm) mixed with sand (DANSAND; filter sand no. 2) in a 2:1 ratio. The varieties were high-yielding with some resistance to above-ground fungal pathogens (SortInfo) and showed different resistance to common pathogens with Sheriff being resistant to \u003cem\u003eSeptoria\u003c/em\u003e, mildew, and yellow rust, Kvium having good resistance against mildew and yellow rust, Rembrandt having a high resistance against \u003cem\u003eSeptoria\u003c/em\u003e, and Heerup having a high resistance to mildew (Sejet, 2017; J\u0026oslash;rgensen, 2020; Sejet, 2020, 2021a, b; J\u0026oslash;rgensen, 2023). Further, Rembrandt showed a lower root density than the other varieties (K. Thorup-Kristensen pers. com). The soil was a sandy loam collected from the plough layer (0-25 cm) in the University of Copenhagen\u0026rsquo;s Long Term Nutrient Depletion Experiment located at H\u0026oslash;jbakkeg\u0026aring;rd in H\u0026oslash;je Taastrup, Denmark (55\u0026deg;40\u0026rsquo; N, 12\u0026deg;17\u0026rsquo; E) (van der Bom et al., 2018, 2017). The soil was collected from a treatment that had received mineral NPK fertilizer at a rate of 120 kg nitrogen, 20 kg phosphorus, and 120 kg potassium ha\u003csup\u003e-1\u003c/sup\u003ey\u003csup\u003e-1\u003c/sup\u003e for the past 25 years and consisted of 170 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e clay, 174 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e silt, 362 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e fine sand, 255 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e coarse sand and 40 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e organic matter. The soil had a pH\u003csub\u003eCaCl2\u003c/sub\u003e of 5.4, an Olsen-P content of 11.4 mg kg\u003csup\u003e-1\u003c/sup\u003e, and a water holding capacity (WHC) of 33 %. The soil was air-dried and sieved to 8 mm before the experiment.\u003c/p\u003e\n\u003cp\u003eEach pot contained one plant, and 420 plants were grown under controlled greenhouse conditions at the University of Copenhagen, Denmark, in the fall of 2020. A vernalization period with temperatures of 6 \u0026deg;C/4\u0026deg;C day/night and a light intensity of 150 \u0026micro;mol m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e-1 \u003c/sup\u003ewas applied 3 weeks after sowing for 12 weeks (week 4-15). Before (week 1-3) and after (week 16-20) vernalization the plants were grown at 19\u0026deg;C day/15\u0026deg;C night; 16 h day/8 h night; light intensity 300 \u0026micro;mol m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e. Pots were regularly rotated to ensure randomness and watered to ca. 70 % WHC by weighing. All plant heights were scored by harvest, and the pots with the five shortest and five tallest plants were sampled for further experiments to cover the range of plant growth. Material from three rhizocompartments with different distances to the root of the plant was sampled; i) the plant was removed from the pot and the remaining soil was labeled bulk soil compartment; ii) the rhizosphere compartment was collected by hand shaking the plant roots inside a falcon tube containing 25 ml of sterile water; iii) the rhizoplane compartment was collected by sonication in an ultrasonic bath and further shaking of the washed roots after transfer to a new falcon tube containing 25 ml of sterile water. Sonication is often used for opening cells before DNA extraction, and hence, the protist cells might have been damaged. As the samples were kept frozen or freeze-dried until DNA extraction, the sonication is not considered to affect the DNA extraction efficiency. All samples were flash-frozen, freeze-dried, transported to Aarhus University, Roskilde, Denmark, and kept at 4˚C until extraction. In total, 120 samples were used for DNA extraction.\u003c/p\u003e\n\u003cp id=\"_Toc149146932\"\u003e2.2 Soil microbial DNA extraction, library preparation, sequencing, and quantification\u003c/p\u003e\n\u003cp id=\"_Toc149146933\"\u003eThe microbial DNA used in this study was extracted earlier by Zervas et al. (2022) using the NucleoBond RNA Soil Mini Kit (item no. 740142.50) with its DNA co-extraction accompanying set (item no. 740143.50) (Macherey-Nagel, Germany), and DNA concentrations were measured on Qubit 4 (Thermo-Fisher Scientific) using the 1xdsDNA High-Sensitivity assay. The extracted DNA was stored at -20 \u003csup\u003eo\u003c/sup\u003eC until metabarcoding and qPCR of protists. 18S rRNA gene metabarcoding was performed using the 1380F and 1510R primer set (Amaral-Zettler et al., 2009) (TAG Copenhagen A/S) targeting the V9 region of the protist 18S rRNA gene using a 2-step PCR.\u003c/p\u003e\n\u003cp id=\"_Toc149146934\"\u003ePCR amplification was done in technical duplicates in a BioRad T100 Thermal Cycler using the following PCR reagents: 12.5 \u0026micro;l Ultra Mix polymerase (PCRBIOSYSTEMS), 0.5 \u0026micro;l forward primer, 0.5 \u0026micro;l reverse primer, 6 \u0026micro;l nuclease-free water, 0.5 \u0026micro;l BSA (BIORON, Germany), and 5 \u0026micro;l template DNA (1-10ng/\u0026micro;l). After initial denaturation at 95˚C for 2 min, the PCR program of 33 cycles of 95˚C for 15 s, 55˚C for 15 s, and 72˚C for 40 s was run with a final elongation step of 72˚C for 4 min. The PCR product from technical duplicates was pooled. A second PCR was performed using 5 \u0026micro;l of pooled product from the previous PCR amplification and barcodes for Illumina sequencing using a combination of dual indexing primers. The PCR program included an initial denaturation temperature of 98˚C for 1 min, then 13 cycles of 98˚C for 10 s, 55˚C for 20 s, and 72˚C for 40 s followed by a final elongation step of 72˚C for 4 min. After each PCR run, 1.5% agarose gels were used to verify successful amplification. After the second PCR, clean-up was performed using the HighPrep PCR Clean-up System (MAGBIO, USA). DNA concentrations were measured using the High-Sensitivity DNA assay, Qubit 4.0 (Thermo-Fischer Scientific). Finally, DNA of each sample was pooled equimolarly and quantified on Qubit. Before sequencing, the library was also inspected on a 4150 Tapestation (Agilent Technologies, Waldbronn, Germany) using D1000 reagents and screentape. Finally, the library was sequenced on an Illumina Nextseq (Environmental Science Sequencing Center, Aarhus University) using the v2.5 chemistry 300-cycles kit in 151 bp pair-end mode. Protist sequence files are deposited in the NCBI sequence read archive under the SRA accession number PRJNA1079549. The 16S rRNA amplicon data was obtained from Genbank Accession number PRJNA806868 (Zervas et al., 2022).\u003c/p\u003e\n\u003cp\u003e2.3 Quantification of protist abundance \u003c/p\u003e\n\u003cp id=\"_Toc149146936\"\u003eProtist abundance was quantified by qPCR using both general protist primers and amoeba-specific primers. The general protist primer set (1380F and 1510R) targeted the V9 region of the 18S rRNA gene of all eukaryotes (176 bp) (Amaral-Zettler et al., 2009) and the amoeba-specific 18S rRNA gene primer set (Amo_1400_F: and Amo_1540_R) (130 bp) (Le Calvez et al., 2012) (TAG Copenhagen A/S). Both reactions contained 1 \u0026micro;l DNA template (DNA concentration 1-10 ng/\u0026micro;l), 4.5 \u0026micro;l 5x HOT FIREPol\u0026reg; EvaGreen\u0026reg; qPCR Supermix (SOLIS BIODYNE), 1 \u0026micro;l forward primer (10 \u0026micro;M), 1 \u0026micro;l reverse primer (10 \u0026micro;M), and 12.5 \u0026micro;l nuclease-free water with a final volume of 20 \u0026micro;l pr sample. QPCR reactions of all samples were obtained by using the BioRad CFX Connect Real-Time System and the qPCR program: 95˚C for 10 min, 95˚C for 15 s, 60˚C for 15 s, and 72˚C for 45 cycles for the general primers and the program: 95˚C for 10 min, 95˚C for 15 s, 62˚C for 15 s, and 72˚C for 45 cycles for the amoeba-specific primers. The standard curves were produced using DNA extracted with the DNeasy PowerLyzer PowerSoil Kit (QIAGEN) from three cultured protists: \u003cem\u003eCercomonas\u003c/em\u003e sp (ATCC 50334), \u003cem\u003eTetrahymena pyriformis\u003c/em\u003e (ATCC 30005), and \u003cem\u003eAcanthamoeba castellanii\u003c/em\u003e (ATCC 50373). DNA from all three organisms was amplified using the general primers, while \u003cem\u003eA. castellanii\u003c/em\u003e DNA was also amplified using the amoeba-specific primers. Successful amplification of the individual protist\u0026rsquo;s DNA was tested by running 1.5% agarose gels of PCR products, and the band of the expected size was extracted using the QIAquick Gel Extraction Kit (Qiagen). The DNA concentration was measured on Qubit; the PCR products were pooled equimolarly and used for ten-fold serial dilutions. Two different standard curves were produced in each qPCR run, targeting all protists and amoebae, respectively, using the previously described thermal cycle program. \u003c/p\u003e\n\u003cp id=\"_Toc149146937\"\u003e2.4 Bioinformatic\u003c/p\u003e\n\u003cp id=\"_Toc149146938\"\u003eAnalysis of protist sequencing data was done using QIIME2 (v. 2021.8) (Bolyen et al., 2019) via DADA2 plugin (Callahan et al., 2016) using default parameters for the filtering, merging, checking of chimeras, dereplicating, and assigning ASVs. Taxonomy of representative sequences for each ASV was assigned using the PR2 database (v. 5.0) (Guillou et al., 2013). \u003c/p\u003e\n\u003cp id=\"_Toc149146939\"\u003e2.5 Statistical analysis\u003c/p\u003e\n\u003cp\u003eMicrobial data were imported into R (v. 4.3.1) using phyloseq (v.1.40.0) for statistical analysis and data visualization (R Core Team, 2021) using ggplot2 package (Wickham, 2016). After taxonomy assignment, unassigned ASVs at the kingdom level were discarded. Further, ASVs assigned to fungi, Metazoa, Streptophyta, Rhodophyta, eukaryotic mitochondria, and plasmids from Gyrista, haptophyte, Stramenopiles, eukaryota mitochondria, and eukaryota plasmids were excluded. Alpha diversity was estimated using observed richness, Shannon index and Simpson indices, where the ASV table was thus rarified 100 times at a depth of 4019 reads for protists and 9417 reads for prokaryotes (sample with lowest number of reads), and then the mean of the diversity estimates of 100 trials was used. Alpha diversity was transformed to Hill numbers using richness (Hill number q = 0) and Shannon diversity (Hill number q = 1, calculated as the exponential of Shannon entropy) via the R package \u003cem\u003ehilldiv (\u003c/em\u003eAlberdi and Gilbert 2019a)\u003cem\u003e. \u003c/em\u003eHill numbers calculate the alpha diversity for DNA-based data and offer a more interpretable framework for biodiversity metrics\u003cem\u003e (\u003c/em\u003eAlberdi and Gilbert 2019b)\u003cem\u003e. \u003c/em\u003e Beta diversity was estimated using Bray Curtis dissimilarity matrix and visualized using non-metric multidimensional scaling (NMDS). Permutational multivariate analysis of variance (PERMANOVA) was performed using \u0026lsquo;adonis2\u0026rsquo; function from the R package vegan (v. 2.6-4). Pairwise comparison was obtained using the pairwise Adonis test using the \u0026lsquo;pairwiseAdonis\u0026rsquo; function and the \u003cem\u003ep\u003c/em\u003e values were adjusted using the Bonferroni correction. Further statistical analysis was carried out using the non-parametric Wilcoxon test, one-way ANOVA, and Kruskal-Wallis\u0026rsquo;s tests with the \u003cem\u003ep\u003c/em\u003e values adjusted using the Bonferroni correction. \u003c/p\u003e\n\u003cp id=\"_Toc149146940\"\u003eDifferential abundance analysis was performed using the DESeq2 (v. 1.36.0) package with a significant threshold set at p\u0026lt; 0.01 to identify differentially abundant microbial taxa across wheat rhizocompartment and wheat variety. Low-abundant taxa were filtered before using the model. To identify top protist taxa that predict the effect of rhizocompartment and wheat variety, we employed Random Forest (RF) classification using the \u0026lsquo;RandomForest\u0026rsquo; (v. 4.7-1.1) package with a prune scale of 0.0001 and 500 trees. Further, using RF model, we identified the top protist predictors ASVs and ranked them based on Mean Decrease in Gini index. Also, we selected top predictors ASVs and examined their relative abundance across treatments. \u003c/p\u003e\n\u003cp id=\"_Toc149146942\"\u003eCo-occurrence network analysis was performed on all samples using Spearman\u0026rsquo;s correlation as described earlier (Sapkota et al., 2020). In brief, Spearman\u0026rsquo;s correlation was calculated on all ASV pairs using the \u0026lsquo;rcorr\u0026rsquo; function from the Hmisc package (v. 5.0-1) and the edgeR (v. 3.38.4). Any significant correlation (p\u0026lt;0.001) and with a correlation coefficient either above 0.6 or below -0.6 was used for the network analysis. Network visualizations were done using the igraph (v. 1.4.2) package in R. Species within the top five percent of node degree values were identified and described as connector species in the interactions. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Data Characteristics\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc149146945\"\u003eSamples were obtained from three different rhizocompartments (rhizoplane, rhizosphere, and bulk soil) of the five tallest and the five shortest plants of four different wheat varieties (Heerup, Kvium, Rembrandt, and Sheriff), resulting in a total of 120 samples. Protist amplicons resulted in 3,525,264 reads after quality control. After the taxonomy assignment, we found that primers used in this study also amplify fungi, metazoans, Gyrista plasmids, haptophyte plasmids, Stramenopiles plasmids, Streptophyta, Rhodophyta, eukaryotic mitochondria, and eukaryotic plasmids. These groups, along with unassigned ASVs at the kingdom level, were trimmed from the dataset. Further, we removed two samples with fewer than 3,000 reads. The final number of reads was 1,743,167 with an average of 14,773 per sample, covering 5,037 protist ASVs, which were the basis of further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 Abundance of protists\u003c/p\u003e\n\u003cp\u003eReal-time qPCR with two different primer sets targeting amoebae and overall protist community, respectively, showed significant differences in protist and amoebae copy number between the wheat varieties (Figure 1), but not between rhizocompartments nor plant height (data not shown) when tested by one-way ANOVA. Furthermore, pairwise comparison among varieties identified significantly higher protist copy numbers in Rembrandt compared to Kvium and Sheriff. The amoebae copy numbers were significantly lower in Kvium compared to the other three varieties, while Rembrandt had a lower copy number than Heerup.\u003c/p\u003e\n\u003cp\u003e3.3 Drivers of protist and prokaryote community composition\u003c/p\u003e\n\u003cp\u003eTable 1: Significant differences (PERMANOVA) between community structure of protists from rhizocompartments, plant height, and wheat varieties.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"340\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.8235%;\"\u003e\n \u003cp\u003eDataset:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.82353%;\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3529%;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.8235%;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.1765%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.8235%;\"\u003e\n \u003cp\u003eRhizocompartment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.82353%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3529%;\"\u003e\n \u003cp\u003e2.1267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8235%;\"\u003e\n \u003cp\u003e0.0773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.1765%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36.1765%;\"\u003e\n \u003cp\u003ePlant height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8235%;\"\u003e\n \u003cp\u003e0.4395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8235%;\"\u003e\n \u003cp\u003e0.0160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.1765%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.8235%;\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.82353%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3529%;\"\u003e\n \u003cp\u003e1.9376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8235%;\"\u003e\n \u003cp\u003e0.0704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.1765%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA non-metric multidimensional scaling (NMDS) plot of the protist amplicon sequencing data revealed a separate clustering of the bulk soil samples (Supplementary Figure 1). Significant differences were found between the protist community diversity of the different rhizocompartments and wheat varieties (Table 1). Despite the proximity of rhizocompartments in the pot experiment, the rhizocompartment explained 7.7% of the variance, while the wheat variety explained 7.0% of the variance, respectively. Bulk soils harbor a higher number of protist ASVs (HillQ0) and have a higher alpha diversity measured by Shannon diversity (HillQ1) and inverted Simpson indices (HillQ2) compared to rhizosphere and rhizoplane, irrespective of the wheat variety (Table 2). Furthermore, alpha diversity indices (HillQ0-2) were significantly different between wheat varieties (Table 2), while plant height correlated with observed richness (HillQ0) but not the other two Hill diversity measures (data not shown). Due to the low explanation of variance and no impact on Hill diversity indices, plant height was not considered a parameter in further testing. Similar diversity indexes of the prokaryotic community were determined based on data from Zervas et al. (2022) (Table 2). A similar pattern of significant differences between rhizocompartments and wheat varieties was found for prokaryotes.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u0026nbsp;Table 2: Mean alpha diversity calculated as Hill numbers using species \u0026nbsp;richness when q=0, \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShannon diversity when q=1, and Simpson when q=2 of protists and prokaryotes. Statistically significant differences between alpha diversity are indicated using different letters (Kruskal-Wallis and pairwise Wilcox test). RP: rhizoplane; RS: rhizosphere; BS: bulk soil.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e3.4 Protist community structure\u003c/p\u003e\n\u003cp\u003eCercozoa and Gyrista\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere the dominating protist subdivisions in all samples (Figure 2). All the subdivisions above 2% relative abundance were significantly different across rhizocompartments except for the Rotosphaerida (Suppl. Table 1). Chlorophyta was statistically relatively more abundant in bulk soil at the expense of the subdivisions Ciliophora and Discosea (Figure 2A). The dominating classes were Peronosporomycetes, Filosa-Thecofilosea, Filosa-Sarcomandea, and Colpodea (Suppl. Figure 2). Across wheat varieties, we found the relative abundance of the following subdivisions Bigyra, Centroplasthelida, Chlorophyta, Discosea, Evosea, Gyrista, Haptophyta, Ichthyosporea, and Tubulinea to be significantly different (Suppl. Table 1). Particularly, the protist community of the wheat variety Rembrandt differed from the other varieties by having a high relative abundance of the classes Peronosporomycetes and Nucleariidea with the latter showing \u0026lt;2% relative abundance in the other varieties (Suppl. Figure 2B). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.5 Differential abundance analyses and random forest model\u003c/p\u003e\n\u003cp\u003eDifferences in the relative abundance of specific ASVs between rhizocompartments and wheat varieties were identified by differential abundance analyses (Suppl. Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnrichment analysis of the rhizosphere revealed 15 ASVs being differentially abundant compared to bulk soil, with ASVs belonging to the classes Endomyxa-Phytomyxea, Chrysophycea, and Chlorophyceae being relatively more abundant in bulk soil, ASVs of \u003cem\u003eRhogostoma\u003c/em\u003e spp., Flabellinia, Colpodea, and Eumycetozoa being relatively more abundant in the rhizosphere, while two different Peronosporomycetes (oomycetes) ASVs were either less or more abundant in the rhizosphere (Suppl. Figure 3A). Similarly, when bulk soil and rhizoplane were analyzed, only two ASVs were significantly different (Suppl. Figure 3B), while there was no significant difference between rhizoplane and rhizosphere.\u003c/p\u003e\n\u003cp\u003eSimilarly, differential abundance analysis across wheat varieties showed that Heerup hosted a higher number of differentially abundant ASVs, especially Chlorophyseae ASVs, compared to Rembrandt (Supp. Fig. 3G), while only one or two ASVs were differentially abundant between the other combinations of wheat varieties (Suppl. Fig. 3C-F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy random forest modeling, we identified the ten most important ASVs contributing to the difference in the protist community based on the rhizocompartment and wheat variety (Suppl. Table 2, Suppl. Figure 4). Within the rhizocompartments (Suppl. Table 2A, Suppl. Figure 4A), two Filosa-Sarcomonadea were top predictors for rhizocompartment, showing the highest relative abundance in the rhizoplane, while two Colpodea ASVs followed as third and fifth predictors with the highest relative abundance in the rhizosphere. For the wheat varieties, five ASVs assigned as Peronosporomycetes\u003cem\u003e\u0026nbsp;\u003c/em\u003e(oomycetes)\u003cem\u003e\u0026nbsp;\u003c/em\u003edominated the top ten list of predictors, followed by fewer differences between the varieties, four of which were most abundant in Rembrandt (Suppl. Table 2B, Suppl. Figure 4B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.6 Network analysis\u003c/p\u003e\n\u003cp\u003eThe major microbial players in the rhizocompartments were identified by a correlation-based co-occurrence network analysis. The 18S rRNA gene amplicon dataset of protists was combined with the 16S rRNA gene amplicon dataset of prokaryotes reported by Zervas et al. (2022). These two datasets are based on the same DNA extractions, extracted from the same soil samples. Hence, they are directly comparable. The 18S rRNA and the 16S rRNA ASVs were used as nodes and correlations as edges. Our network analysis revealed a closer and more intricate network with a higher degree of nodes in bulk soil than in rhizosphere and rhizoplane (Suppl. Table 3, Figure 3). Edges were primarily positive with a few negative (Figure 3). The prokaryotes with a higher number of connections in the bulk soil were identified as Gammaproteobacteria, Saccharimonadia, Alphaproteobacteria, and Acidobacteria, including Subgroup 6, while the protists with the highest connections were identified as Rhizaria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also analyzed wheat co-occurrence networks based on wheat varieties (Figure 4) and found similar network properties consisting of one core hub network with positive correlations and a smaller hub with negative correlations. The Heerup variety microbiome had the highest number of nodes while Rembrandt had the lowest number of nodes (Suppl. Table 3). Rhizaria had a high number of nodes in Heerup, Stramenophiles had a high number of nodes in Rembrandt, Discosea had a high number of nodes in Sheriff, while Kvium had no dominating protist (Suppl. Table 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe tested two hypotheses: i) rhizocompartment and wheat varieties impact the protist community structure and diversity, ii) the co-occurrence of prokaryotes and protists depends on the wheat varieties that select for different key protist-prokaryote interactions. A total of 120 soil samples from four different wheat varieties and three different rhizocompartments were analyzed by 18S rRNA gene metabarcoding targeting protists. In addition, protist abundance was quantified by qPCR targeting both general eukaryotes and amoebae. The driving ASVs of the different rhizocompartments and wheat varieties were tentatively identified by differential abundance analysis and Random Forest modeling. Further, co-occurrence network analyses between 16S rRNA and 18S rRNA gene amplicons identified prokaryotic and protist species co-occurring in the rhizocompartments and wheat varieties.\u003c/p\u003e\n\u003cp\u003e4.1. Rhizocompartments drive protist communities\u003c/p\u003e\n\u003cp\u003eThe overall community structure of protists was significantly different between the three rhizocompartments of bulk soil, rhizosphere, and rhizoplane, in agreement with previous studies (Guo et al., 2018; Fiore-Donno et al., 2019; Zhang et al., 2021) with protist communities of bulk soil separating from rhizosphere and rhizoplane. Across the rhizocompartments, the protist subdivisions Gyrista, Cercozoa, Ciliophora, and Chlorophyta dominated as previously shown in soil (Rossmann et al. 2020, Sandin and Jamy, 2021; Singer et al., 2021; Guan et al., 2023). Using protist-prokaryote co-occurrence networks, we found that the three rhizocompartments affect the overall complex microbial networks. We observed a higher number of nodes and closer and more intricate networks in bulk soil compared to the rhizosphere and rhizoplane. This supports earlier findings in soybean plants showing reduced interactions in the rhizosphere, likely the result of increased resources in the rhizosphere, leading to lower microbial diversity closer to the root, therefore supporting fewer interactions (Zhang et al., 2021). Co-occurrence networks are affected by multiple biases during the data processing, especially when combining two microbial amplicon datasets. Our study is based on DNA extracted from 0.5 g of soil, and despite the small volume, the co-occurrence of microorganisms does not explicitly guarantee that the organisms ever met or interacted. It has been shown that the variable region selected for sequencing will alter the microbial community and thereby affect the outcome of the network. Due to the paraphyletic nature of protists, no universal primers are available, and all primers will have limitations to their amplification (Hugerth et al., 2014; Zheng et al., 2022). Hence, when discussing co-occurrence networks, the origin of the scientific dataset and the ecology surrounding the samples should be considered when distinguishing predator-prey interactions and co-occurrence driven by the environment (Guseva et al., 2022). Our co-occurrence networks are solely based on correlations and reflect only co-occurrence driven by the environment. Despite these uncertainties, the networks can help us hypothesize on microbial interactions and guide us toward new insights into cross-kingdom interactions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.2 Wheat variety effects on protist and prokaryote community structure\u003c/p\u003e\n\u003cp\u003eWheat varieties hosted different protist abundance and communities, which might be due to the difference in pathogen resistance, exudate composition, and e.g., root morphology (Sejet, 2017; J\u0026oslash;rgensen, 2020; Sejet, 2020, 2021a, b; J\u0026oslash;rgensen, 2023). qPCR results showed that Kvium hosted significantly fewer amoebae copy numbers than the other wheat varieties, while Rembrandt hosted a higher protist gene copy number than the other varieties. Water use efficiency varies between wheat varieties (Hafeez et al., 2024) which could modify water content under similar conditions and affect protist community structure. Specifically, higher water content is reported to increase protist abundance (Geisen et al., 2014). It is known that broadly targeted primers might not only amplify related taxa, but also non-target taxa. Such non-target amplification is more common when we target diverse eukaryotic lineages. In our qPCR assays of protists and amoebae, it cannot be ruled out that the primers also captured other eukaryotic organisms. Despite such a lack of taxonomic specificity, the data still provides the approximation of abundance and allows comparative insights across treatments.\u003c/p\u003e\n\u003cp\u003eIn addition to different pathogen resistances, Heerup plants have been reported to have a higher average root diameter than Sheriff, which could result in relative differences in the abundance of bacterial groups (Kavamura et al., 2020; Guan et al., 2023). Particularly, Heerup and Rembrandt supported specific protist communities differing from each other and the other two varieties. For example, Spirotrichea had a higher relative abundance in Heerup than in both Sheriff and Rembrandt rhizocompartments according to the differential abundance analysis. By 16S rRNA gene amplicon sequencing, significant differences in prokaryotic communities between the wheat varieties were observed by Zervas et al. (2022). Using this data, we found alpha diversity measures of prokaryote communities to vary significantly between the wheat varieties, as found for protist communities. Plant roots and associated microbiome interactions are complex and multilayered, and thus effects on the prokaryote community could be linked to the changes in the predatory protist community (Shi et al., 2011; Rossmann et al. 2020; Zhao et al., 2021; Fiore-Donno et al., 2022) by selecting prokaryotic species with different palatability to protists. The selection of specific protist groups through prey availability might explain the differences in protist communities observed between varieties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlant variety is a widely reported driver of microbial networks (Jiang et al. 2017, Quiza et al. 2023), and we found that wheat variety affects the protist-prokaryote co-occurrence networks, changing the number of positive and negative correlations. The highest number of nodes was found in the Heerup network, while the highest number of correlations was found in the Sheriff network. Such differences in the networks can be due to slight variations in the root physiology or root exudates, that, as mentioned above, can affect the water availability and the root exudation (Gaete et al., 2021). Connector protist species co-occurring with the bacteria were identified as belonging to the classes Discosea, Rhizaria, and Stramenophiles, which follows other studies (Zhang et al., 2021; Romdhane et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.4 Predictor protists\u003c/p\u003e\n\u003cp\u003eCertain protists, such as the commonly found testate amoebae of the Rhogostoma\u003cem\u003e\u0026nbsp;\u003c/em\u003efamily, were detected in multiple of our analyses, suggesting their potential significance in the community structures. Based on the random forest modeling, this family is proposed to be a predictor species in Rembrandt samples compared to other wheat varieties. The presence of this group of omnivorous thecate amoebae is highly affected by factors like moisture, pH, and total plant biomass, suggesting that Rembrandt roots might provide good environmental conditions for this protist (Dumack et al., 2017; Oztoprak et al., 2020). Supporting this, two\u0026nbsp;\u003cem\u003eRhogostoma\u003c/em\u003e spp. ASVs (eASV 4 and eASV 7) are found to be differentially abundant in samples closer to the root. However, two\u0026nbsp;\u003cem\u003eRhogostoma\u003c/em\u003e spp. ASVs were more abundant in Heerup vs Rembrandt rhizocompartment. Acosta et al. (2024) similarly found\u0026nbsp;\u003cem\u003eRhogostoma\u0026nbsp;\u003c/em\u003espp. in co-occurrence network analysis of soil and Simoni et al. (2020) found\u0026nbsp;\u003cem\u003eRhogostoma\u0026nbsp;\u003c/em\u003espp. to be highly prevalent in wheat rhizosphere. Similarly, two ASVs belonging to the Group-Te orders were found to be more abundant in the rhizoplane compared to the bulk soil and were also identified as predictor ASV for the rhizoplane compartment. The Group-Te protist order has been reported earlier in the rhizosphere of wheat,\u0026nbsp;\u003cem\u003eArabidopsis,\u003c/em\u003e and maize\u0026nbsp;(Sapp et al., 2018; Taerum et al., 2022; Guan et al., 2023), and as core taxa with a high prevalence in wheat rhizosphere microbiome(Simonin et al., 2020). This supports the enrichment of\u0026nbsp;\u003cem\u003eRhogostoma\u0026nbsp;\u003c/em\u003eas well as Group-Te protists in specific rhizocompartments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePeronosporomycetes, also known as oomycetes, are a strong predictor in wheat varieties with six different ASVs among the top 10 of the random forest modeling. Four of these six ASVs were found to be most abundant in Rembrandt rhizocompartment which corresponds to the relatively high abundance of Peronosporomycetes in Rembrandt, and the finding of Peronosporomycetes as a connector species in the Rembrandt network. Peronosporomycetes are plant pathogens or saprophytes decaying organic matter and are widely abundant in soil (Sapkota and Nicolaisen, 2015; Sapkota and Nicolaisen, 2018). Confirming this, all six ASVs in question were found to be plant pathogens. Our findings indicate that the wheat variety Rembrandt host a relatively higher abundance of more pathogenic Peronosporomycetes than the other varieties. This difference might be due to differences in disease resistance. It would be relevant to further link plant performance and disease resistance to Peronosporomycetes abundance of the wheat varieties (J\u0026oslash;rgensen, 2020, 2023).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we report rhizocompartment and wheat variety as factors affecting protist community structure with dynamics selecting for specific protists. Using qPCR and relative abundances, amoebae, especially of the family Rhogostoma and the class Peronosporomycetes were shown to vary between the four wheat varieties. Through co-occurrence network analyses possible interactions between prokaryotes and protists in the rhizocompartments of four wheat varieties were found, and connector protist species were identified from the subdivisions Discosea, Rhizaria, and Stramenophiles. This supports the hypothesis that predatory protists play a pivotal role in the structuring of the rhizosphere wheat microbiome. Our study contributes to the understanding of microbiome interactions in the soil and wheat rhizosphere.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e7. Acknowledgements\u003c/p\u003e\n\u003cp\u003eThis study is part of the INTERACT project funded by the Novo Nordisk Foundation (Grant number: NNF19SA0059360). We thank Tina Thane and Tanja Begovic for their valuable assistance in the laboratory.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Christine Lorenzen Elberg, Rumakanta Sapkota, Athanasios Zervas, Dorette S. M\u0026uuml;ller-St\u0026ouml;ver, Mette Haubjerg Nicolaisen, Rosanna Catherine Hennessy, and Anne Winding. The first draft of the manuscript was written by Christine Lorenzen Elberg and all authors commented on previous versions of the manuscript. 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Water Biology and Security 1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"18S rRNA, qPCR, co-occurrence network, predictor species, bacteria, microbiome","lastPublishedDoi":"10.21203/rs.3.rs-7381449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7381449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and Aims: Soil protist communities and their interaction with prokaryotes in the rhizocompartment influence plant growth. However, the drivers of protist diversity and their co-occurrence with prokaryotic communities in dynamic rhizocompartments and between wheat varieties are not well understood. We hypothesized that rhizocompartment and wheat varieties, differing in root structure and pathogen resistance, impact protist community structure and diversity. Additionally, the co-occurrence of prokaryotes and protists was hypothesized to depend on wheat varieties selecting for different key protist-prokaryote interactions.\u003c/p\u003e\n\u003cp\u003eMethods: We studied the protist community composition of four wheat varieties in three rhizocompartments: rhizoplane, rhizosphere, and bulk soil, and their co-occurrence with prokaryote communities. In soil DNA from a greenhouse pot experiment, protist abundance was determined using qPCR, and community composition was described by metabarcoding of 18S rRNA and 16S rRNA genes.\u003c/p\u003e\n\u003cp\u003eResults: Protist community structure and abundance were significantly affected by the rhizocompartment and wheat varieties. Protist richness increased with distance from the root surface. Protist abundance was higher in the rhizocompartments of the Rembrandt wheat variety, while amoeba abundance was lower in the Kvium variety. Colpodea was more abundant in the rhizosphere, and Filosa-Sarcomonadea in the rhizoplane, compared to bulk soil. A co-occurrence network analysis showed an intricate network with more nodes in the bulk soil.\u003c/p\u003e\n\u003cp\u003eConclusion: Rhizocompartment and wheat variety drive protist communities, consistent with the drivers of prokaryotic communities, demonstrating the interconnectivity of protist-prokaryotic interactions in the soil rhizosphere.\u003c/p\u003e","manuscriptTitle":"The soil protists are affected by rhizocompartment and wheat variety, and co-occur with prokaryotes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 14:21:52","doi":"10.21203/rs.3.rs-7381449/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3b1a140-6447-4c42-b1a3-8ed2ce5a20c2","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T08:51:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 14:21:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7381449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7381449","identity":"rs-7381449","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}