Predator-driven microbial feedback loops promote plant health

Domain: biology
Published: 2026-03-14
Editorial outcome: minor_revision

Abstract
Top-down trophic interactions are major drivers of microbiome dynamics, yet their outcomes are difficult to predict and their consequences for pathogen control remain unclear. We combine synthetic bacterial communities of varying complexity with field studies and microcosm assays to test whether microbivorous nematodes reorganize microbiomes to suppress soilborne disease. Field studies show stronger nematode-microbe associations around healthy plants, and microcosm assays confirm that nematode presence produces stable suppression, whereas microbe-only communities collapse under pathogen invasion. Nematode predation depletes non-preferred bacterial taxa and enriches metabolically versatile taxa within Proteobacteria, increasing community-level antagonistic potential and promoting complementary resource-use interactions linked to pathogen inhibition, yielding suppression beyond individual or pairwise effects. A minimal four-component feedback loop linking a nematode predator, plant pathogens, and two plant-associated bacteria with complementary functions accounts for the emergent outcome. Together, these results reveal an animal-mediated pathway of microbiome assembly that enhances resistance to pathogen invasion and provide a trophically informed framework for designing stable, disease-suppressive microbiomes in agriculture.

Introduction
Soil food webs play a critical role in pathogen suppression through complex trophic interactions1-5. While microbe-microbe interactions have recently received increasing attention6-8, the ecological roles of faunal-microbial interactions in soils remain poorly resolved due to conceptual and methodological challenges9. For example, faunal predation on microbes is embedded in microbial communities shaped by mutualism, competition, and antagonism, making it difficult to disentangle direct trophic effects from broader community interactions. As a result, microbial trophic networks remain difficult to predict and quantify, which precludes us from fully leverage the soil microbiome for agricultural purposes.

Here, we investigate how nematodes, the dominant microbial grazers in soil food webs10-13, shape root-associated microbiomes to suppress plant pathogens. We focus on Ralstonia solanacearum, a globally distributed soilborne pathogen that causes bacterial wilt in over 200 plant species14. Using a regional survey and controlled experiments with defined synthetic bacterial communities (SynComs) and representative nematode species, we first assess how nematode predation generally influences the composition and function of root-associated microbiomes. Recent work shows that protist predation reshapes bacterial communities to suppress plant disease15, but the microbial functions or traits underlying this effect remain unclear. Since predation is known to promote microbial investment in traits such as antibiotic production and competitive resource uptake16, we hypothesize that nematode predation activates similar microbiome functions that enhance the community’s resistance to the pathogen R. solanacearum. We next explore how nematodes interact with specific microbial taxa that mediate pathogen suppression. In soils, nematodes interact with microbial communities through selective predation, which is shaped by microbial traits such as palatability, defense, and metabolic capacity17. Our previous work shows that R. solanacearum is toxic to nematodes and is actively avoided by them18, whereas many Proteobacteria are preferentially consumed by nematodes17,19. In contrast, Firmicutes and Actinobacteria, often protected by thick cell walls or chemical defenses, are generally not preferred17,19,20. This preferential feeding on fast-growing Proteobacteria can stimulate their metabolic activity and initiate positive feedback that reinforce their dominance within the community13,21-23. We therefore hypothesize that nematode predation promotes the dominance of certain Proteobacteria, which in turn enhances microbial network cohesion and stabilizes microbiome structure under pathogen stress.

To test these hypotheses, we construct a controlled micro-food web system consisting of nematodes, a defined synthetic rhizosphere microbiome, R. solanacearum, and tomato plants. The constructed SynCom comprises 122 genome-sequenced bacterial strains spanning dominant cultivable taxa in the tomato rhizosphere, including Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria17. These groups include key lineages known to promote or inhibit nematode growth and to participate in microbial cooperation or competition17,24. Whole-genome sequencing of all strains enables functional metatranscriptomic analysis and precise tracking of bacteria-nematode interactions. This experimental system enables us to investigate how faunal predation influences microbiome structure and function and how these changes suppress plant pathogens.

Results
Nematode presence reduces plant pathogen invasion in the greenhouse
We surveyed 124 agricultural field sites to gain an overview of how the diversity of soil nematodes and bacteria covaries under healthy and diseased plant conditions (Fig. 1a). We sampled rhizosphere soils from healthy plants (n=63) and from diseased plants infected with R. solanacearum (n=61) and constructed co-occurrence networks from the relative abundance of bacterial ASVs and nematode species to infer cross-trophic species interactions. Comparison of network topologies revealed a higher proportion of negative interactions within bacterial-faunal networks in healthy soils (343 links, 48%) than in diseased soils (134 links, 31%) (Fig. 1b, Supplementary Fig. 1). These were primarily driven by bacterivores, followed by omnivore-predators, but not by fungivores or herbivores (Fig. 1b).

To validate the cross-trophic species correlations observed in the field, we next conducted a tomato greenhouse experiment using a 122-member bacterial SynCom with four trophic treatments including two different bacterivorous nematodes, an omnivorous nematode consuming microbes and other nematodes, and a mixture of bacterivores and the omnivore (Fig. 1c). Tomato plants inoculated with SynCom and R. solanacearum were diseased by day 45, while those co-inoculated with nematodes remained healthy up to day 60, except for one treatment with low nematode abundance (Fig. 1d). Fluorescent imaging of stem sections confirmed weak or no R. solanacearum colonization in nematode-treated plants (Fig. 1d). Nematode addition reduced rhizosphere pathogen abundance by 63% (Kruskal-Wallis chi-squared = 48.2, df = 6, P = 1e-8), and wilt incidence by 74% compared to the R. solanacearum-only control (Kruskal-Wallis chi-squared = 40.3, df = 6, P = 4e-7) (Fig. 1d). Specifically, the two bacterivorous nematodes and the mixed bacterivore-omnivore nematode community showed the strongest pathogen suppression and lowest disease incidence, while the omnivorous nematode was less effective in disease suppression (Fig. 1d). These results confirm that nematodes contribute to pathogen suppression in controlled environments.

Nematode predation reshapes bacterial communities, promoting species evenness
To explore how nematodes affect SynCom dynamics, we conducted a 24-day petri dish experiment with four sequential transfers (on days 6, 12, 18, and 24), comparing SynCom alone versus SynCom with three nematode species (Fig. 2a). Nematode inoculation initially reduced bacterial biomass, but both populations later stabilized (Supplementary Fig. 2). Nematode presence significantly changed bacterial community composition (Bray-Curtis distance, F = 5.4, R2 = 0.15, p < 0.001, Fig. 2b). Although species richness declined across all treatments (Supplementary Fig. 3a), nematodes increased community evenness (Fig. 2c).

Selective grazing by nematodes enriched Proteobacteria in their gut microbiota, promoting their dominance in the surrounding environment, especially at days 12 and 18 (Kruskal-Wallis chi-squared = 17.6-19.8, df = 3, P < 0.001, Supplementary Fig. 4). Nematode predation reshaped the bacterial community by enriching seven species, including four abundant taxa (>1% average relative abundance), all of which belonged to the phylum Proteobacteria (Fig. 2d, Supplementary Fig. 3b, Supplementary Table 1). Specifically, nematodes reduced the most dominant species Paraburkholderia oxyphila (PAR1, Betaproteobacteria) by 38% (Kruskal-Wallis chi-squared = 13.3, df = 3, P = 0.004), while boosting the less common species Raoultella ornithinolytica (RAO1, Gammaproteobacteria) and Klebsiella michiganensis (KLE1, Gammaproteobacteria) by 390-420% (Kruskal-Wallis chi-squared = 25.5, df = 3, P = 1e-5, Figs. 2e-g, Supplementary Fig. 3c). These results suggest that top-down trophic control from nematodes promotes a more even microbiome by suppressing dominant species and enriching less common taxa.

Functional changes underlying nematode-driven suppression of plant pathogens
To test whether nematode predation enhance s microbiome functions related to pathogen resistance, we assessed two key indicators at the final sampling time (day 24): suppression of R. solanacearum and carbon substrate utilization as a proxy for resource competition (Fig. 3a). Pathogen abundance was reduced by 82% compared to the SynCom control (Kruskal-Wallis chi-squared = 14.2, df = 3, P = 0.003, Fig. 3b). Microbial communities also showed a 142% increase in carbon utilization ability and substrate diversity relative to the SynCom control (Kruskal-Wallis chi-squared = 27.3, df = 3, P = 5e-6, Fig. 3c), including broader use of carbohydrates, carboxylic acids, amines, and polymers (Supplementary Fig. 5). These findings indicate that nematodes enhance microbial functional capacity.

To determine whether these functional shifts were reflected at the molecular level, we next conducted metatranscriptomic profiling of the bacterial community at the final sampling time (day 24, Fig. 3a). Nematode predation significantly altered the SynCom’s transcriptional profiles at the community level, compared to the SynCom control (Bray-Curtis distance, F = 17.6, R2 = 0.87, P < 0.001, Fig. 3d, Supplementary Fig. 6a). A total of 19,061 genes showed consistent changes across three nematode treatments, with 60% (11,487 genes) exhibiting increased activity, primarily contributed by KLE1 and RAO1, while most genes with reduced activity were associated with PAR1 (Figs. 3e-f, Supplementary Fig. 6b). Over 80% of the genes with increased activity were involved in metabolic functions, including the biosynthesis of secondary metabolites, antibiotics, amino acids, as well as carbon metabolism (Figs. 3f-g, Supplementary Fig. 7). These molecular signatures, together with enhanced pathogen suppression and carbon utilization capacity, point to a key role of nematode-driven bacterial metabolism in plant pathogen suppression.

Predation triggers positive feedback loops that enhance plant health
The high expression of antibiotic biosynthesis and carbon metabolism genes in KLE1 and RAO1 led us to investigate whether predator-induced interactions between these taxa contribute to pathogen suppression. We conducted fully factorial laboratory microcosms with KLE1, RAO1, and nematodes in the presence of R. solanacearum. Nematodes alone did not reduce pathogen abundance (ANOVA: F = 1.48, P = 0.26, Fig. 4a), likely due to impaired growth under pathogen stress (ANOVA: F = 24.8, P < 0.001, Fig. 4b). However, KLE1 and RAO1 individually suppressed pathogens by 70% and 32%, respectively (ANOVA: F = 95-281, P < 0.001, Fig. 4a, Supplementary Fig. 8a). Their co-culture suppressed the pathogen by 79% (Wilcoxon rank-sum test, W = 25, P = 0.008, Fig. 4a), exceeding either monoculture, indicating functional complementarity. This synergy was likely driven by metabolic cross-feeding, as KLE1 grew better in RAO1-conditioned medium than in fresh medium (ANOVA: F = 9.0, P = 0.02, Figs. 4c-d, Supplementary Fig. 8b).

Although nematodes alone did not suppress pathogen density, they strongly modulated the suppressive effects of KLE1 and RAO1. KLE1, the preferred prey, supported higher nematode growth (ANOVA: F = 49.1, P < 0.001, Figs. 4b-e, Supplementary Fig. 8c), although its combination with nematodes did not enhance suppression beyond KLE1 alone (Fig. 4a). In contrast, RAO1, a suboptimal food source (Figs. 4b-c, Supplementary Fig. 8c), showed stronger pathogen suppression when combined with nematodes than when alone (ANOVA: F = 15.2, P = 0.004, Fig. 4a). The RAO1-KLE1-nematode combination suppressed the pathogen more strongly than the RAO1-KLE1 pair (86% vs 79%, ANOVA: F = 8.8, P = 0.02, Fig. 4a). These results reveal a positive feedback loop across trophic levels (Fig. 4f): RAO1 facilitates KLE1 growth, amplifying KLE1-R. solanacearum antagonism and providing optimal food for the nematode. Meanwhile, RAO1-nematode interactions enhance RAO1’s suppressive effect on the pathogen. Together, the tripartite combination of KLE1, RAO1, and nematode predators achieves stronger suppression than any species or species pair alone.

Importantly, the positive feedback loop observed in petri dishes was validated in greenhouse experiments, where it enhanced both microbial function and plant performance (Fig. 5a). The plant disease index and pathogen density were lowest when all three species were present (Figs. 5b-c). Meanwhile, plant growth (ANOVA: F = 8.5-24.8, Fig. 5d), soil dissolved organic carbon (DOC) (ANOVA: F = 16.1, P < 0.001, Fig. 5e), and bacterial and nematode abundance (ANOVA: F = 4.0, P = 0.03, Fig. 5f, Supplementary Fig. 9) were highest. Consistent with our in vitro conceptual framework (Fig. 4f), a structural equation model fitted to these data suggests that nematodes enhance plant growth through a trophic cascade, where predation increases bacterial abundance, which in turn suppresses pathogen density and alleviates disease burden on plants (Fig. 5g). Nematodes also increased DOC concentration, which correlated positively with plant growth. Overall, these findings illustrate how greater interaction diversity in soil microbial trophic networks enhances plant health.

Methods
Farmland investigation sampling
The regional-scale farmland investigation was conducted in Jiangxi Province, southeastern China, spanning between 24°29′14″N-30°04′43″N and 113°34′18″E-118°28′56″E. This region experiences a subtropical warm and humid monsoon climate, characterized by an average annual temperature ranging from 16 to 25℃ and an annual precipitation of 1341 to 1943 mm. In summer 2022, we collected 124 tobacco rhizosphere soil samples across sites where tobacco is typically rotated with late rice and often planted on ridged paddy land. This rotation system has long been practiced in the region. Sampling locations were on the province’s dominant red soils (FAO: Acrisols) and paddy soils, and tobacco bacterial wilt caused by the Ralstonia solanacearum species complex is widespread in this region. Each location was sampled with 8 to 10 plants to collect rhizosphere soils, which were then combined to create a single composite plot sample. The relative abundance of R. solanacearum was obtained through the analysis of 16S ribosomal DNA gene sequences using Illumina sequencing. Soil nematodes were extracted from 100 g of fresh rhizosphere soil using a combination of modified Baermann trays and centrifugal flotation methods39.

Bacterial and nematode species
The synthetic bacterial community, consisting of 122 species, was isolated from the rhizosphere of tomato plants. Each bacterium was derived from a single clone and identified through 16S rDNA sequencing (Supplementary Table 1). Every bacterium was cultured using TSB medium (15 g L-1 Tryptone, 0.5 g L-1 Soytone, 0.5 g L-1 NaCl) at 30 °C.

The model nematode species Caenorhabditis elegans N2 was cultured in the laboratory as standard practice. The other three soil-dwelling nematode species (Pristionchus quartusdecimus, Distolabrellus veechi and Panagrolaimus sp.) were isolated from the rhizosphere of tomato plants and identified based on morphology and 18S rDNA (NCBI accession number: OQ832773, PX736993, PX736994). We selected free-living nematodes that (i) were dominant in the field survey of rhizosphere communities, (ii) were easy to culture and manipulate, and (iii) exhibited similar developmental rates (L1 to egg-laying in 4–6 days), enabling synchronized and well-controlled microcosm and greenhouse assays. All nematodes were cultivated on NGM agar plates with Escherichia coli OP50 at 20 °C. The NGM medium consisted of 2.5 g L-1 Peptone, 3 g L-1 NaCl and 17 g L-1 agar. Before use, 25 mL 1 M KH PO4-K HPO Buffer (pH 6.0), 1 mL 1 M CaCl , 1 mL 1 M MgSO and 1 mL 5 mg/mL cholesterol were added sequentially to 1 L NGM medium. Tryptone, Soytone, Peptone, and chemical reagents were purchased from OXOID (UK) and Sangon Biotech (China). Soil-dwelling nematodes were synchronized at least five times to eliminate any potentially carried microbes from the natural environment.

Experimental setup and sampling
To explore the impact of nematode consumers on bacterial community coexistence and functions, we designed four treatments: control (synthetic bacterial community only), C. elegans, D. veechi, and Panagrolaimus sp. (each with synthetic bacterial community). Each treatment had six replicates and four sampling points, leading to a total of 96 microcosms (Fig. 2a). The microcosm experiment was conducted in 60 mm diameter culture plates using a fresh soil suspension to mimic the soil environment. Specifically, 60 g of fresh tomato-cultivated soil was combined with 120 mL PBS (pH 7.0) in a shaker for 4 h to create a soil suspension. After centrifugation at 5000 g for 5 min, a sterile soytone solution was added to the soil suspension, resulting in a final concentration of 0.05 % (W/V). This soil suspension mixture was then filtered twice through a 0.22 μm filter for future use.

Each bacterium was cultured overnight and adjusted to a density of 0.5 (OD600 nm). The 122 bacterial species were pooled in equal proportions (1:1…:1), washed three times in M9 buffer to remove residual culture medium, and inoculated into soil-suspension microcosms to create the synthetic bacterial community. A 1 mL volume of synthetic bacteria was added to 10 mL of soil suspension and adjusted to a density of 0.1 (OD600 nm), followed by transferring 10 mL of the resulting suspension into the culture plate. Approximately 200 synchronized nematode individuals at the L4 stage (the fourth larval stage immediately prior to adulthood) were added, with six biological replicates per treatment. Microcosms were maintained at 20 °C with gentle shaking for 6 days before transferring 5 mL to new culture dishes containing 5 mL of fresh soil suspension. This process continued for a total of 4 transfers over 24 days. Bacterial density, nematode numbers (excluding eggs), and 16S rDNA amplicon sequencing for bacterial community in both the soil suspension and nematode gut were measured before each transfer. Antagonistic ability against R. solanacearum, BioLog system analysis, and Metatranscriptome sequencing for bacterial functional profiles were conducted at the final sampling point.

DNA extraction
To extract microbial DNA from soil suspension, 2 mL of the culture suspension was transferred into an injector equipped with a 300-mesh filter membrane to remove nematodes. To isolate bacterial cells, the filtered culture suspension was centrifuged at 15000 × g for 1 min. The resulting bacterial pellet was then used for total DNA extraction using the DNeasy 96 Blood & Tissue Kit (Qiagen, Germany), following the manufacturer’s guidelines.

To extract microbial DNA from the nematode gut, 200 nematode individuals were selected and transferred to 1.5 mL tubes and washed with M9 buffer five times. Then, M9 buffer containing 10 mM levamisole was added to immobilize nematodes for 10 min, followed by the addition of a 2-fold volume of disinfectant M9 buffer (1% NaClO and 0.25 M NaOH) to kill bacteria for 3 min. The mixture was then centrifuged at 600 g for 1 min to remove the supernatant, and the nematodes were washed with M9 buffer three times. The final wash buffer was spread onto a TSB plate to confirm successful sterilization. The remaining nematodes were lysed using an electric grinder (JingXin, China) equipped with a sterile broach to release bacteria into the solution. Total DNA was extracted using the DNeasy 96 Blood & Tissue Kit (Qiagen, Germany), following the manufacturer’s guidelines.

16S rDNA amplicon sequencing and analysis
The V3-V4 region of the bacterial 16S rDNA gene was amplified using the primer pair 341F (5’-barcode-CCT AYG GGR BGC ASC AG-3’) and 806R (5’-GGA CTAC NNG GGT ATC TAA T-3’), where each sample had a unique eight-base barcode. Amplicons were then extracted from 2 % agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen, USA), following the manufacturer’s instructions. Purified PCR products were quantified using the Qubit® 3.0 (Life, USA), and twenty-four amplicons with distinct barcodes were equally combined. The pooled DNA product was used to construct Illumina Pair-End library following Illumina’s genomic DNA library preparation procedure. Subsequently, the amplicon library underwent paired-end sequenced (2 × 250) on an Illumina platform (BIOZERON Biotech., China) according to the standard protocols.

Raw fastq files were first demultiplexed using in-house perl scripts based on barcode sequence information for each sample. The following criteria were applied: (i) The 250 bp reads were truncated at any position with an average quality score < 20 over a 10 bp sliding window. Truncated reads shorter than 50 bp were discarded. (ii) Exact barcode matching was employed, with a tolerance of up to two nucleotide mismatches allowed in primer matching. Reads containing ambiguous characters were removed. (iii) Sequences that overlapped by at least 10 bp were assembled based on their overlapping sequence. Unassembled reads were excluded. Operational taxonomic units (OTUs) were clustered at a 97% similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/), and potential chimeric sequences were identified and removed using UCHIME. The phylogenetic affiliation of each 16S rDNA gene sequence was determined using the uclust algorithm (http://www.drive5.com/usearch/manual/uclust_algo.html), with a confidence threshold of 80%. The database of 16S rRNA gene fragments was constructed using the synthetic bacterial community composed of 122 species. All 16S rDNA gene sequences were compared to the established database using BLASTN, with a 97% similarity cutoff.

Nematode-mediated SynCom antagonism capacity: R. solanacearum suppression assay
To assess the impact of nematode predation on the SynCom’s ability to suppress R. solanacearum QL-Rs1115, at the final sampling time (day 24) of the microcosm experiment, culture suspensions from each treatment were transferred into 50 mL tubes and centrifuged at 10000 × g for 3 min to pellet the bacterial cells and nematodes. The supernatant was collected and filtered through a 0.22 μm filter to obtain cell-free filtrates, and mixed with NA medium (glucose 10 g L-1, tryptone 5 g L-1, beef extract 3 g L-1, yeast extract 0.5 g L-1) at ratio of 1:1 (V/V). R. solanacearum QL-Rs1115 was cultured overnight and inoculated into the mixed medium at 1 %. After incubation at 30 ℃ with shaking at 220 rpm for 24 hours, the abundance of QL-Rs1115 was quantified by serial dilution and plate counting.

Nematode-mediated SynCom carbon utilization: BioLog analysis
BioLog EcoPlate, consisting 96 wells with 31 carbon sources, was used to assess the carbon metabolism of nematode-mediated SynCom. Briefly, 1 mL of the culture suspension was centrifuged at 15000 × g for 1 min, after which the supernatant was discarded. The pellet was washed with sterile water three times, and added to 9 mL of sterile 0.85 % NaCl (10-1). A vortex mixer was used to ensure uniform mixing, and serial dilutions were made to achieve 10-3 concentration. A 150 μL aliquot of the dilution was added to the EcoPlates, which was then incubated at 25 °C. Absorbance readings were taken at 590 nm and 750 nm every 24 h up to 168 h. To correct for background absorbance, the readings for individual substrates were corrected by subtracting the absorbance of the control well (water) and the absorbance of the first reading. The average metabolic activity, indicated by average well color development (AWCD), was calculated using the formula: AWCD = Σ(C590-C750)/31, where C590 and C750 represent the optical density values at 590 nm and 750 nm from each well, respectively, with correlations for blank well values (inoculated with distilled water).

Nematode-mediated SynCom functional profile: Metatranscriptome sequencing and analysis
RNA extraction and library preparation: Total RNA was extracted from rhizosphere samples using TRIzol® Reagent (Invitrogen) following the manufacturer’s instructions, and residual genomic DNA was removed using DNase I (TaKaRa). RNA quantity and integrity were assessed using a NanoDrop 2000 (Thermo Fisher Scientific) and an Agilent 2100 Bioanalyzer. Ribosomal RNA was depleted using the Ribo-Zero™ rRNA Removal Kit (Illumina), and cDNA libraries were constructed from 5 µg of total RNA using the TruSeq™ Stranded Total RNA Library Prep Kit (Illumina). Following fragmentation, cDNA synthesis, end repair, adaptor ligation, and PCR enrichment (15 cycles with Phusion DNA polymerase, NEB), target fragments of 200–300 bp were size-selected on a 2% Low Range Ultra Agarose gel. Libraries were sequenced on the Illumina NovaSeq 6000 platform (BIOZERON Biotech, China), generating paired-end reads (2 × 150 bp).

Data processing and functional annotation: Raw reads were quality-trimmed using Trimmomatic v0.36 (parameters: SLIDINGWINDOW:4:15 MINLEN:75). Reads aligning to the host genome were filtered out. Gene prediction was performed using MetaProdigal, and a non-redundant gene catalog was built using CD-HIT with 95% identity and 90% coverage. Functional annotations were assigned via BLASTp (v2.8.1+) against the NCBI NR, KEGG, and STRING databases using an E-value cutoff of 1e-5. Gene Ontology (GO) terms were annotated using BLAST2GO.

Differential expression and pathway analysis: Transcript quantification was performed using Salmon (https://github.com/COMBINE-lab/salmon), and gene abundances were further normalized using RSEM. Differentially expressed genes (DEGs) between nematode-inoculated and control treatments were identified using edgeR (v3.36.0). Genes with an absolute log₂ fold change > 2 and FDR < 0.05 were considered significantly changed in transcript signal. As transcript counts in metatranscriptomic data reflect both the abundance of gene templates (i.e., species-level DNA copy number) and mRNA transcriptional activity, all differential analyses were interpreted as aggregate transcript signal changes, representing the combined effects of population abundance shifts and transcriptional regulation. GO enrichment and KEGG pathway analysis were conducted using GOATOOLS and KOBAS, respectively. Enrichment was considered significant at Bonferroni-corrected P < 0.05.

Functional categorization and taxonomic assignment: To evaluate the impact of nematodes on bacterial functional profiles, we focused on genes with consistently increased aggregate transcript signal across all three nematode-inoculated treatments. Highly upregulated genes (|log₂FC| > 5) were extracted for downstream interpretation. DEGs were categorized into five major functional groups: metabolism, environmental information processing, genetic information processing, cellular processes, and organismal systems. Among these, we focused on the seven most enriched KEGG pathways: metabolic pathways, biosynthesis of secondary metabolites, metabolism in diverse environments, biosynthesis of antibiotics, biosynthesis of amino acids, carbon metabolism, and purine metabolism. The number of genes with increased signal in each pathway was quantified and linked to species-level taxonomic annotations, based on the mapping of transcripts to the non-redundant gene catalog.

Nematode feeding preference between Klebsiella michiganensis (KLE1) and Raoultella ornithinolytica (RAO1)
Nematode feeding preference was assessed using bacteria’s attractive index to nematodes. The attractive index was determined using the conventional method outlined in previous studies40,41. Briefly, a 90 mm culture plate was divided into sections with 200 adult nematodes placed at the center and two bacterial suspensions (each 20 μL) in marginal circles, including one of the bacteria and model bacterium E. coli OP50. Attraction was recorded at 8, 12, 24, and 36 h. The attraction index (AI) was calculated as Ni / (Ni + N_OP50), where Ni represents the number of nematodes in the dot-circle of bacteria i, and N_OP50 represents the number of nematodes in the dot-circle of another bacterium E. coli OP50.

Interaction among the nematode, Klebsiella michiganensis (KLE1), Raoultella ornithinolytica (RAO1) and Ralstonia solanacearum (QL-Rs1115)
The antagonistic abilities of nematodes, KLE1, and RAO1 against QL-Rs1115 were evaluated using eight treatments: QL-Rs1115 alone, QL-Rs1115 + nematode, QL-Rs1115 + bacteria (KLE1, RAO1, or KLE1+RAO1), and QL-Rs1115 + nematode + bacteria, with five replicates per treatment. Nematodes were synchronized and cultured to the L4 stage, washed with M9 buffer, and starved for 12 hours. KLE1 and RAO1 were diluted to an OD₆₀₀ of 0.6. A total of 1500 nematodes and 30 μL of bacterial suspension were added to 50 mL sterile tubes containing 3 mL NGM + 1% TSB medium and incubated at 22°C with shaking at 120 rpm for 24 hours. After centrifugation, supernatants were filtered to obtain sterile extracellular secretions. QL-Rs1115 was cultured in NA medium, diluted to an OD₆₀₀ of 0.4. 1.5 mL of QL-Rs1115 suspension was inoculated into an equal volume of either NGM + 1% TSB medium or the collected extracellular secretions. The mixtures were incubated at 30°C and 180 rpm for 12 hours. Colony-forming units (CFUs) were counted.

Nematode body size was used as an integrative proxy for consumer performance and prey profitability on each bacterial prey, complementing the feeding-preference assay. Body size was calculated as volume from length and width measurement: body size = (length × width²) / 1.7. Nematodes were cultured on NGM plates seeded with E. coli OP50, QL-Rs1115, KLE1, or RAO1 at 22°C until adulthood, then heat-fixed and observed under a stereomicroscope. Body dimensions were measured using Motic Images Plus 3.0 software, with 20 replicates per treatment.

The interaction between KLE1 and RAO1 was tested using extracellular secretions. KLE1 and RAO1 suspensions were diluted to OD₆₀₀ of 0.4, centrifuged, and filtered to obtain extracellular secretions. Each 1.5 mL suspension was incubated in an equal volume of either fresh TSB or the extracellular secretions of the other strain, and incubated at 30 °C with shaking at 180 rpm for 12 hours. Colony-forming units (CFUs) were counted. Interaction effects were interpreted as follows: if CK (TSB) > ES (strain), extracellular secretions inhibited the other strain’s growth; if equal, no significant effect was observed; and if less, extracellular secretions promoted growth. Here, CK (TSB) refers to bacterial count in standard TSB medium, while ES (strain) refers to bacterial count in the extracellular secretions of the other strain.

The effect of nematodes on the interaction between KLE1 and RAO1 was studied with six treatments: monoculture (KLE1, RAO1, KLE1 + nematodes, RAO1 + nematodes) and co-culture (KLE1 + RAO1 and KLE1 + RAO1 + nematodes), each replicated five times. Nematodes were synchronized and cultured to the L4 stage, washed with M9 buffer, and starved for 12 hours. KLE1 and RAO1 suspensions were diluted to OD₆₀₀ of 0.4. A total of 1500 nematodes and 300 μL of bacterial suspension were added to 50 mL sterile tubes containing 3 mL NGM + 1% TSB medium and incubated at 22°C with shaking at 120 rpm for 12 hours. Colony-forming units (CFUs) were counted. Interaction effects were interpreted as follows: if MP_KLE1 + MP_RAO1 > CP_KLE1+RAO1, the strains were competitive; if equal, the interaction was neutral; and if less, the strains were facilitative. Here, MP and CP represent bacterial counts from monoculture and co-culture treatments, respectively.

Pot experiments
The farmland soil and substrate were pre-mixed at a 1:1 ratio, then sterilized by irradiation at a dose of 90 kGy (Xiyue Technology Co. Ltd., Nanjing), and packed into sterile plastic cups, with each cup containing 300 g of the mixed soil. Tomato seedlings were grown in sterile seedling-raising plate with sterile substrate, and transplanted into the sterile plastic cups once they reached the four-leaf stage. For nematode and SynCom experiment: after a 7-day stabilization period, nematodes and SynCom were introduced, followed by root inoculation with red fluorescent protein-labeled QL-Rs1115 (tagged with the pYC12-mCherry plasmid, construction details are provided in our previous study42) 7 days later. For KLE1, RAO1 and nematodes experiment: after a 7-day stabilization period, nematodes, KLE1 and green fluorescent protein-labeled RAO1 (tagged with the pYC12-eGFP plasmid, construction details are provided in our previous study42) were introduced, followed by root inoculation with red fluorescent protein labeled QL-Rs1115 7 days later. Each treatment group consisted of 20 individual repeats. The plants were cultivated in a greenhouse with temperatures ranging from 24 °C to 30 °C, with regular watering using sterile water and rearrangement every 5 days. The severity of bacterial wilt disease was recorded as a mean of disease index of each plant using a scale ranging from 0 to 4 (0, no signs of wilting; 1, 1-25% leaf area wilted; 2, 26-50% leaf area wilted; 3, 51-75% leaf area wilted; and 4, 76-100% leaf area wilted).

The rhizosphere soil samples and plant samples were collected at 10 days and 20 days after inoculation with QL-Rs1115, with 5 replicates each sample. Nematode extraction using a modified Baermann method. Plant height was measured as the vertical distance from the base of the stem (at the root collar) to the top of the plant under natural growth conditions. The red fluorescent visualization of tomato stems was conducted using a Confocal Laser Scanning Microscope (Leica TCS sp8, Germany). The abundance of QL-Rs1115, KLE1, and RAO1 was determined using real-time quantitative PCR with fluorescent primers. The primers used are as follow, Rs-forward: 5’-AGT TCA TGT ACG GCT CCA AGG CCT AC -3’, Rs-reverse: 5’- CGC GCA GCT TCA CCT TGT AGA TGA AC-3’; KLE1-forward: 5’- CAT GGC ACT GAA ATG GAA CGG CTT TG-3’, KLE1-reverse: 5’- TAT CTG GGC GTC AAG CAG TGT ATT CC -3’; RAO1-forward: 5’- TCA AGG ACG ACG GCA ACT ACA AGA C -3’, RAO1-reverse: 5’- CCT CGA TGT TGT GGC GGA TCT TGA A -3’. All primers were synthesized by Genscript Biotech Corporation.

Ammonium nitrogen content in soil was determined using the indophenol blue colorimetric method, with absorbance measured at 625 nm using a UV-Vis spectrophotometer (UV-8000, Shanghai Yuanxi). Available phosphorus content in soil was determined using the molybdenum-antimony colorimetric method following sodium bicarbonate extraction, with absorbance measured at 625 nm using a UV-Vis spectrophotometer. Dissolved organic carbon content in soil was determined by deionized water extraction and measured using a total organic carbon analyzer (TOC-LCPH, Shimadzu Corporation, Japan).

Statistical analysis
All statistical analyses and data visualization were conducted using R (version 4.1.2). To compare variable means across multiple treatments, we first evaluated the data distribution and selected appropriate statistical methods accordingly. Data normality was assessed using the Shapiro-Wilk test. For datasets meeting the normality assumption, one-way analysis of variance (ANOVA), followed by least significant difference (LSD) post hoc tests was used to assess differences among treatments. When the normality assumption was violated, non-parametric methods were applied: the Wilcoxon rank-sum test for pairwise comparisons and the Kruskal-Wallis test for multiple group comparisons.

To explore cross-trophic interactions between bacterial taxa and nematode taxa (bacterivores, fungivores, omnivore-predators, and herbivores) at the regional-scale, we constructed co-occurrence networks from field-survey data using the iNAP platform43 (https://inap.denglab.org.cn) and visualized them with Gephi 9.2. Networks were built separately for healthy and diseased soils based on a unified ASV table integrating bacterial and nematode datasets. Only bacterial ASVs with relative abundance > 0.01% and occurrence frequency > 10% were included. Co-occurrence patterns were inferred using the SparCC method with correlation thresholds of |r| > 0.3 and P < 0.05. To focus on pathogen-relevant interactions, bacterial ASVs associated with R. solanacearum and with nematode groups were extracted for further analysis. In the resulting networks, nodes represent ASVs and edges denote significant correlations.

To evaluate the effects of nematodes on bacterial community structure and functional profiles, we conducted a series of diversity and multivariate analyses. Alpha diversity indices, including species richness and Pielou’s evenness, were calculated using the vegan package44. The ‘loess’ smoothing method was applied to create a smooth curve that captures the underlying trends in alpha diversity. Differences in bacterial community composition among treatments were visualized using non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarity and statistically tested using permutational multivariate analysis of variance (PERMANOVA, adonis function in the vegan package, 999 permutations). In addition, bacterial species were categorized as dominant (>10% relative abundance), sub-dominant (1-10%), or rare (<1%) to assess compositional shifts. We also calculated fold changes in the relative abundance of each species using the formula (X-control)/control, where X represents the average abundance across all sampling times in the nematode-inoculated treatment. To assess functional shifts in the bacterial community, principal coordinates analysis (PCoA) was performed on Euclidean distances derived from normalized metatranscriptomic data. Statistical significance of treatment effects on both taxonomic and functional profiles was tested using PERMANOVA (adonis function).

To evaluate the direct and indirect effects of nematodes on plant health, we performed piecewise structural equation modeling (SEM) using the piecewiseSEM package45. Plant growth was quantified as a composite variable combining aboveground dry biomass and plant height, with the first principal component (explaining 73.8% of the variance) used as the growth index for modeling. The SEM framework included key variables such as nematode presence, bacterial and pathogen densities, dissolved organic carbon, and plant growth. Each path was fitted as a linear model, and model fit was evaluated using Fisher’s C statistic and chi-square test. A non-significant chi-square test (P > 0.05) indicated acceptable model fit.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Li et al., investigated how predatory nematodes interactions with specific bacterial species affect the over-all bacterial community structures, composition and functional traits, and ultimately leads to the suppression of plant pathogen Ralstonia solanacearum. The study was detailed involving field survey data and controlled laboratory microcosms and greenhouse experiments using defined synthetic bacterial communities (SynComs) and representative nematode species. The study provides detailed insights into nematode-bacterial interactions and reveals compelling insight that would contribute to our understanding of multi-trophic interactions and exploring their emergent beneficial outcomes for crop disease management.\nThe manuscript is well written with detailed analysis and data presentation. However, the manuscript require improvement with respect to detailed explanations for specific methodologies and analysis.\nBelow, I have summarized other key/minor comments to consider for improving the manuscript.\nMain\nThe nematodes used are free-living. What criteria was used to select me?\nLine 81 Is it \"...the microbiome ...\" or the constructed SynCom?\nMaterials and Methods\nLines 270-2 Please provide specific details of the fields cropping history, soil type and S. solanacearum distribution.\nLines 296-99. I suggest you reference Fig 2a here to guide the reader.\nLines 308 Elaborate why you did the washing.\nLine 312 “L4 stage”- Please describe briefly this stage in parenthesis.\nLines 348-9 “….following Illumina’s genomic DNA library 348 preparation procedure.”. Please cite\nLines 441 while KLE1 and RAO1 were described later as Klebsiella michiganensis (KLE1) and Raoultella or-nithinolytica in the discussion, it will be appropriate to write the respective full names in the materials and methods section. Also for QL-Rs1115.\nLines 456-7 Why did you measure body size?\nResults\nLine 95 Extended Data Figs. 1 should be referenced together with Fig. 1b. Also in Fig. 1d, you showed only selected images at the specific time points, why?\nFig. 1b Is there significant differences in the nematode functional groups detected in healthy and diseased soils?\nLine 122 Ideally, in the natural soil system, several free-living nematodes will be interacting together. I would expect to see IRs_SC_NT as part of several experimental works. Please provide explanation for ex-cluding it in the downstream investigations for example in the nematode predation effects on bacterial community composition” (Fig. 2).\nLine 123-125 which treatment caused the most significant changes in bacterial community. Please perform pairwise adonis test to compare the different treatments.\nLines 158-156 Figures should generally be cited in the order they appear. For example fig 3d before e-f. Please edit this where appropriate in the text.\nFig. 3a is missing in the text\nFigure 3f. remove blue bar under legends symbol in stacked bar plot.\nFigure 4 What is OP50?\nLine 130 Extended Data Figs 4. The enrichment pattern here is not clear, especially for SC_N2 and SC_N3 with declining Proteobacteria RA) at 24 days. Please also perform statistics to confirm this trend.\nLines 133-7 Lines 133-137 what could be causing this changes?\nLines 145-7 “…. Pathogen abundance was reduced by 82% compared to the SynCom control (Kruskal-Wallis chi-squared = 14.2, 146 df = 3, P = 0.003, Fig. 3b). Did you test if filtered suspension from nematode-inoculated plates inhibit RS?", "reviewer_2": "This study investigated the contribution of microbivorous nematodes to reorganize the microbiome of tomato for better suppression of the soilborne pathogen Ralstonia solanacearum. The authors analysed the communities of nematodes and microbes of healthy and diseased plants in the field. More interestingly, they did controlled experiments with communities consisting of nematodes, tomato plant, the pathogen and a selection of 122 well characterized bacteria from the tomato rhizosphere (SynCom).\nNot surprisingly, communities of nematodes and microbes differed between healty and diseased plants in the field. As this could be related to the cause or, more likely, is a consequence of the disease, conclusions remain speculative.\nL. 92: Skip “in the field”.\nL. 102-103: Remove this speculative conclusion.\nL. 116: Skip “both field and”.\nIn a greenhouse pot experiment, addition of microvorous nematodes to plants inoculated with SynCom and pathogen reduced the abundance of the pathogen in the rhizosphere and disease incidence, compared to controls without nematodes.\nL. 115: Add results from inoculation of the different nematodes, especially P. quartusdecimus.\nIn a 24-days petri dish experiment, addition of microvorous nematodes to SynCom reduced the most adundant bacterial species and increased some less abundant species, compared to SynCom alone. This resulted in an increased community evenness. Addition of nematodes to SynCom increased carbon utilization ability and substrate diversity, as determined in Biolog plates. Addition of nematodes to SynCom increased its antagonistic activity against R. solanacearum, compared to SynCom alone.\nNematodes altered the metatranscriptomic profiles of the SynCom. Among the strongly upregulated transcripts, some are involved in biosynthesis of secondary metabolites and antibiotics.\nL. 152-156: Clarify whether these changes in transcriptional profiles were explained by changes in community structure, or were the profiles of single abundant bacteria (KLE1, RAO1) changed?\nFully factorial microcosms with two of the bacteria that profited from nematodes (KLE1, RAO1), the pathogen, and nematodes were done. Nematodes alone did not reduce the abundance of the pathogen. KLE1 and RAO1 individually suppressed the pathogen to different extent, but even more in combination of both strains. Best pathogen suppression was achieved with both bacteria plus nematodes, compared to any single species or pair of species. Greenhouse pot trials were consistent with this finding, as the species combination showed the lowest plant disease index and pathogen density.\nThese results highlight the importance of emergent properties of plant-biome-systems that arise from multiple interactions in the plant-associated functional network. It challenges models of disease suppression that are solely based on antagonist-pathogen interactions.\nI recommend to accept this manuscript for publication."}, "author_response": "Response to reviewers’ comments\nTitle: “Predator-driven microbial feedback loops promote plant health”\nTracking #: NCOMMS-25-86076-T\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nLi et al., investigated how predatory nematodes interactions with specific bacterial species affect the\nover-all bacterial community structures, composition and functional traits, and ultimately leads to the\nsuppression of plant pathogen Ralstonia solanacearum. The study was detailed involving field survey\ndata and controlled laboratory microcosms and greenhouse experiments using defined synthetic\nbacterial communities (SynComs) and representative nematode species. The study provides detailed\ninsights into nematode-bacterial interactions and reveals compelling insight that would contribute to our\nunderstanding of multi-trophic interactions and exploring their emergent beneficial outcomes for crop\ndisease management. The manuscript is well written with detailed analysis and data presentation.\nHowever, the manuscript require improvement with respect to detailed explanations for specific\nmethodologies and analysis.\nBelow, I have summarized other key/minor comments to consider for improving the manuscript.\nThank you very much for your thoughtful and encouraging feedback. We agree and have revised the\nMethods, figure legends, and Supplementary Information to provide detailed descriptions of the specific\nmethodologies and analyses.\nMain\nThe nematodes used are free-living. What criteria was used to select them?\nWe clarified in the Materials and Methods section “Bacterial and nematode species” that: “We selected\nfree-living nematodes that (i) were dominant in the field survey of rhizosphere communities, (ii) were\neasy to culture and manipulate, and (iii) exhibited similar developmental rates (L1 to egg-laying in ~4–\n6 days), enabling synchronized and well-controlled microcosm and greenhouse assays.” (lines 294-\n297)\nLine 81 Is it \"...the microbiome ...\" or the constructed SynCom?\nWe now clarify that the text refers to the constructed SynCom: “The constructed synthetic community\n(SynCom) comprised 122 genome-sequenced bacterial strains…”. (line 81)\nMaterials and Methods\nLines 270-2 Please provide specific details of the fields cropping history, soil type and S. solanacearum\ndistribution.\nWe have added site-level context on cropping history, soil types, and pathogen distribution to the\n“Farmland investigation sampling” section.\nThe sentence has been supplemented as follows: “In summer 2022, we collected 124 tobacco\nrhizosphere soil samples across sites where tobacco is typically rotated with late rice and often planted\non ridged paddy land. This rotation system has long been practiced in the region. Sampling locations\nwere on the province’s dominant red soils (FAO: Acrisols) and paddy soils, and tobacco bacterial wilt\n1\ncaused by the Ralstonia solanacearum species complex is widespread in this region.” (lines 272-277)\nLines 296-99. I suggest you reference Fig 2a here to guide the reader.\nWe agree and have added an explicit citation to Fig. 2a in this passage. The revised text reads: “To\nexplore the impact of nematode consumers on bacterial community coexistence and functions, we\ndesigned four treatments: control (synthetic bacterial community only), C. elegans, Panagrolaimus sp.,\nand D. veechi (each with synthetic bacterial community). Each treatment had six replicates and four\nsampling points, leading to a total of 96 microcosms (Fig. 2a).” (lines 307-311)\nLines 308 Elaborate why you did the washing.\nWe washed the pooled isolates three times in M9 to remove residual culture medium. This minimized\nnutrient carryover into the soil-suspension microcosms and prevented bias in community assembly and\nnematode behavior.\nThe sentence has been revised as: “The 122 bacterial species were pooled in equal proportions\n(1:1…:1), washed three times in M9 buffer to remove residual culture medium, and inoculated into soil-\nsuspension microcosms to create the synthetic bacterial community.” (lines 319-321)\nLine 312 “L4 stage”- Please describe briefly this stage in parenthesis.\nWe added a brief definition in parentheses as requested.\nThe sentence has been revised as: “Approximately 200 synchronized nematode individuals at the\nL4 stage (the fourth larval stage immediately prior to adulthood) were added”. (line 325)\nLines 348-9 “….following Illumina’s genomic DNA library preparation procedure.”. Please cite\nWe have now added the appropriate citation for this method in the revised manuscript, as follows:\n“……following Illumina’s genomic DNA library preparation procedure (https://support-\ndocs.illumina.com/LP/IlluminaDNAPrep/Content/LP/Illumina_DNA/DNA-Prep/Protocol_IDP.htm)”\n(lines 364-365)\nLines 441 while KLE1 and RAO1 were described later as Klebsiella michiganensis (KLE1), and\nRaoultella or-nithinolytica in the discussion, it will be appropriate to write the respective full names in\nthe materials and methods section. Also for QL-Rs1115.\nWe agree and have now spell out the full species names in the Materials and Methods section title:\nKlebsiella michiganensis (KLE1), Raoultella ornithinolytica (RAO1), and Ralstonia solanacearum (QL-\nRs1115). (lines 460 and 472)\nLines 456-7 Why did you measure body size?\nWe measured nematode body size as a proxy for prey profitability. Bacterial prey that support larger\nbody size are typically more profitable and thus more likely to be preferred, so this metric complements\nthe feeding-preference test.\nThe sentence has been supplemented as follows: “Nematode body size was used as an integrative\nproxy for consumer performance and prey profitability on each bacterial prey, complementing the\nfeeding-preference assay. Body size was calculated as volume from length and width measurement:\nbody size = (length × width²) / 1.7.” (lines 487-489)\nLine 95 Extended Data Figs. 1 should be referenced together with Fig. 1b. Also in Fig. 1d, you showed\nonly selected images at the specific time points, why?\nThank you for your reminder. We have now referenced Extended Data Fig. 1 together with Fig. 1b.\nThe sentence has been revised as: “Comparison of network topologies revealed a higher\nproportion of negative interactions within bacterial-faunal networks in healthy soils (343 links, 48%)\nthan in diseased soils (134 links, 31%) (Fig. 1b, Extended Data Fig. 1)” (line 100)\nRegarding Fig. 1d, the selected images at specific time points correspond to the appearance of\nplant disease under the respective treatments. For example, disease appeared at 25 days for Rs, at\n45 days for RS_SC, and at 60 days for Rs_SC_N1. Since no disease was observed in the other three\nnematode treatments (Rs_SC_N2, Rs_SC_N3, Rs_SC_NT) by day 60, we captured images at that\ntime along with the Rs_SC_N1 treatment.\nWe have added the following clarification to the caption of Fig. 1d: “Disease appeared at 25 days\nfor Rs, 45 days for RS_SC, and 60 days for Rs_SC_N1. No disease was observed in the other three\nnematode treatments (Rs_SC_N2, Rs_SC_N3, Rs_SC_NT) by day 60, so images for these treatments\nwere captured at the same time as for Rs_SC_N1.”\nFig. 1b Is there significant differences in the nematode functional groups detected in healthy and\ndiseased soils?\nWe compared the relative abundances of four nematode functional groups between healthy and\ndiseased soils and found no significant differences for any group The figure is shown below:\nLine 122 Ideally, in the natural soil system, several free-living nematodes will be interacting together. I\nwould expect to see Rs_SC_NT as part of several experimental works. Please provide explanation for\nex-cluding it in the downstream investigations for example in the nematode predation effects on\nbacterial community composition” (Fig. 2).\nThe exclusion of Pristionchus quartusdecimus from downstream investigations was mainly due to\nconcerns that combining predatory nematodes with bacterivorous nematodes would complicate the\ninterpretation of interactions with KLE1 and RAO1. Additionally, Pristionchus quartusdecimus was less\neffective at suppressing Ralstonia solanacearum compared to bacterivorous nematodes, with disease\nsymptoms appearing by day 60. Therefore, we decided not to include it in subsequent pathogen\nsuppression experiments.\nPlease\nperform pairwise adonis test to compare the different treatments.\nWe performed the pairwise ADONIS test to compare the different treatments. Based on the results (see\nbelow picture), we found that the most significant changes in bacterial community composition were\ncaused by the following treatments:\n• C. elegans vs Control (R2 = 0.21, p = 0.006)\n• D. veechi vs Control (R2 = 0.17, p = 0.006)\n• C. elegans vs Panagrolaimus (R2 = 0.09, p = 0.048)\nAmong these, C. elegans vs Control had the largest effect, as it exhibited the highest R2 value,\nindicating a greater proportion of variance explained by the treatment.\nIn the revised manuscript, we did not include the above results, as our focus was on observing general\npatterns rather than the effects of different nematodes on the SynCom structure. As reflected in the\nmetatranscriptome analysis, which concentrated on shared genes influenced by all three nematode\nspecies.\nLines 158-156 Figures should generally be cited in the order they appear. For example fig 3d before e-\nf. Please edit this where appropriate in the text.\nThank you for your reminder. I have reviewed and corrected the figure citations to ensure they follow\nthe correct order. Regarding Fig. 4, the order progresses from 4a to 4f. Fig. 4a is referenced multiple\ntimes as it includes results from several treatments, and different parts of Fig. 4a are mentioned in\ndifferent sections of the manuscript based on the treatments.\nFig. 3a is missing in the text\nThank you for your reminder. We have clarified the statement: “To test whether nematode predation\nenhances microbiome functions related to pathogen resistance, we assessed two key indicators at the\nfinal sampling time (day 24): suppression of R. solanacearum and carbon substrate utilization as a\nproxy for resource competition (Fig. 3a).” (lines 145-148)\nFigure 3f. remove blue bar under legends symbol in stacked bar plot.\nThe changes have been made as suggested. The revised figure is shown below.\nOP50 refers to the standard laboratory Escherichia coli strain OP50 used as a food source for\nnematodes. It was initially included as a control to assess the impact of Klebsiella michiganensis (KLE1),\nRaoultella ornithinolytica (RAO1), and Ralstonia solanacearum on nematode body size.\nHowever, after careful consideration, we have removed the OP50 data from the Fig. 4b, as\ncomparisons among KLE1, RAO1, and R. solanacearum alone were sufficient to highlight the key\ndifferences.\nLine 130 Extended Data Figs 4. The enrichment pattern here is not clear, especially for SC_N2 and\nSC_N3 with declining Proteobacteria RA) at 24 days. Please also perform statistics to confirm this trend.\nWe performed the relevant statistical analyses and found significant differences in Proteobacteria\nenrichment at 12 and 18 days, but no significant difference at 24 days. Therefore, we have corrected\nour results in the manuscript and included the relevant statistical analysis.\nThe sentence has been supplemented as follows: “Selective grazing by nematodes enriched\nProteobacteria in their gut microbiota, promoting their dominance in the surrounding environment,\nespecially at 12 and 18 days (Kruskal-Wallis chi-squared = 17.6-19.8, df = 3, P < 0.001, Extended Data\nFig. 4).” (lines 129-131)\nLines 133-137 what could be causing this changes?\noxyphila (PAR1) and an increase in Raoultella ornithinolytica (RAO1) and Klebsiella michiganensis\n(KLE1), are caused by nematode predation. Although nematodes showed a similar feeding preference\nfor KLE1 and PAR1 in the two-choice assays, PAR1 is the most abundant species in the synthetic\ncommunity (SynCom), making it more likely to be encountered and preyed upon by nematodes. This\npredation on PAR1 likely released ecological space, allowing KLE1 and RAO1 to thrive and increase\nin relative abundance.\nLines 145-7 “…. Pathogen abundance was reduced by 82% compared to the SynCom control (Kruskal-\nWallis chi-squared = 14.2, 146 df = 3, P = 0.003, Fig. 3b). Did you test if filtered suspension from\nnematode-inoculated plates inhibit RS?\nYes, the results shown in Fig. 3b represent the effect of the filtered suspension from nematode-\ninoculated plates on Ralstonia solanacearum (RS) inhibition. Specifically, we used the suspension\nobtained by centrifuging and filtering (with a 0.22 µm filter) the nematodes and SynCom bacteria from\nthe nematode + SynCom treatment and the SynCom-only treatment to test its effect on RS inhibition.\nThe description of this assay has been added to the Materials and Methods section as follows:\n“Nematode-mediated SynCom antagonism capacity: R. solanacearum suppression assay\nTo assess the impact of nematode predation on the SynCom’s ability to suppress R. solanacearum\nQL-Rs1115, at the final sampling time (day 24) of the microcosm experiment, culture suspensions from\neach treatment were transferred into 50 mL tubes and centrifuged at 10000 × g for 3 min to pellet the\nbacterial cells and nematodes. The supernatant was collected and filtered through a 0.22 μm filter to\nobtain cell-free filtrates, and mixed with NA medium (glucose 10 g L-1, tryptone 5 g L-1, beef extract 3 g\nL-1, yeast extract 0.5 g L-1) at ratio of 1:1 (V/V). R. solanacearum QL-Rs1115 was cultured overnight\nand inoculated into the mixed medium at 1 %. After incubation at 30 ℃ with shaking at 220 rpm for 24\nhours, the abundance of QL-Rs1115 was quantified by serial dilution and plate counting.” (lines 385-\n396)\nReviewer #2 (Remarks to the Author):\nThis study investigated the contribution of microbivorous nematodes to reorganize the microbiome of\ntomato for better suppression of the soilborne pathogen Ralstonia solanacearum. The authors analysed\nthe\ncommunities of nematodes and microbes of healthy and diseased plants in the field. More\ninterestingly, they did controlled experiments with communities consisting of nematodes, tomato plant,\nthe pathogen and a selection of 122 well characterized bacteria from the tomato rhizosphere (SynCom).\nNot surprisingly, communities of nematodes and microbes differed between healthy and diseased\nplants in the field. As this could be related to the cause or, more likely, is a consequence of the disease,\nconclusions remain speculative.\nWe agree that the differences observed between healthy and diseased plant microbiomes in the field\ncould be either a cause or a consequence of the disease, which makes conclusions drawn from field\ndata speculative. To address this, we conducted controlled microcosm experiments to provide more\ndirect evidence of the potential role of nematodes in microbiome restructuring and pathogen\nsuppression.\nWe have revised the sentence to skip \"in the field,\" as recommended. The updated sentence now reads:\n\"Nematode presence reduces plant pathogen invasion in the greenhouse.\" (line 92)\nL. 102-103: Remove this speculative conclusion.\nWe have removed the speculative conclusion: “suggesting that bacterivorous nematodes structure\nbacterial communities to better resist pathogens” as recommended.\nL. 116: Skip “both field and”.\nThe changes have been made as suggested. The sentence has been revised as: “These results\nconfirm that nematodes contribute to pathogen suppression in controlled environments.” (line 118)\nIn a greenhouse pot experiment, addition of microbivorous nematodes to plants inoculated with\nSynCom and pathogen reduced the abundance of the pathogen in the rhizosphere and disease\nincidence, compared to controls without nematodes.\nL. 115: Add results from inoculation of the different nematodes, especially P. quartusdecimus.\nThe changes have been made as suggested. The supplemented sentence is as follows: “Specifically,\nthe two bacterivorous nematodes and the mixed bacterivore-omnivore nematode community showed\nthe strongest pathogen suppression and lowest disease incidence, while the omnivorous nematode\nwas less effective in disease suppression (Fig. 1d).” (lines 114-117)\nIn a 24-days petri dish experiment, addition of microbivorous nematodes to SynCom reduced the most\nabundant bacterial species and increased some less abundant species, compared to SynCom alone.\nThis resulted in an increased community evenness. Addition of nematodes to SynCom increased\ncarbon utilization ability and substrate diversity, as determined in Biolog plates. Addition of nematodes\nto SynCom increased its antagonistic activity against R. solanacearum, compared to SynCom alone.\nNematodes altered the metatranscriptomic profiles of the SynCom. Among the strongly upregulated\ntranscripts, some are involved in biosynthesis of secondary metabolites and antibiotics.\nL. 152-156: Clarify whether these changes in transcriptional profiles were explained by changes in\ncommunity structure, or were the profiles of single abundant bacteria (KLE1, RAO1) changed?\nThe changes in transcriptional profiles were primarily explained by changes in the overall bacterial\ncommunity structure, rather than by changes in the profiles of individual abundant bacteria, such as\nKLE1 and RAO1.\nWe have revised the sentence for clarification: “Nematode predation significantly altered the\nSynCom’s transcriptional profiles at the community level, compared to the SynCom control (Bray-Curtis\ndistance, F = 17.6, R2 = 0.87, P < 0.001, Fig. 3d, Extended Data Fig. 6a).” (line 157-159)\n3,8\nFully factorial microcosms with two of the bacteria that profited from nematodes (KLE1, RAO1), the\npathogen, and nematodes were done. Nematodes alone did not reduce the abundance of the pathogen.\nKLE1 and RAO1 individually suppressed the pathogen to different extent, but even more in combination\nof both strains. Best pathogen suppression was achieved with both bacteria plus nematodes, compared\nto any single species or pair of species. Greenhouse pot trials were consistent with this finding, as the\nspecies combination showed the lowest plant disease index and pathogen density.\nmultiple interactions in the plant-associated functional network. It challenges models of disease\nsuppression that are solely based on antagonist-pathogen interactions.\nI recommend to accept this manuscript for publication.\nThank you for your positive feedback and recommendation. We appreciate your recognition of our work.\n8"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily revised the manuscript and I therefore suggest it should be considered for publication.", "reviewer_2": "The authors have satisfactorily addressed all suggested changes to the manuscript. I recommend accepting the manuscript for publication."}, "author_response": ""}]

Decision letter