Enhanced Biocontrol of Root-Knot Nematodes Through Co-Cultivation of Clonostachys rosea and Bacillus velezensis: Proline-Driven Bacterial Fitness and Synergistic Metabolite Production

Abstract

The ascomycete fungus Clonostachys rosea is a promising biocontrol agent against root-knot nematodes. To develop a more effective and stable biocontrol strategy, we rationally constructed a co-culture system by partnering C. rosea with the plant growth-promoting bacterium Bacillus velezensis. Through systematic optimization of the medium and inoculation protocol, the co-culture demonstrated significantly enhanced performance, achieving 95.3% mortality of Meloidogyne incognita juveniles, a 78.0% increase in tomato shoot dry weight, and 69.2% disease control efficacy in pot trials. Metabolomic profiling indicated that the co-culture triggered a distinct metabolic profile compared to the respective monocultures. The enhanced efficacy was associated with the accumulation of two functional metabolite groups. First, the co-culture synergistically accumulated direct-effect compounds with reported nematicidal (e.g., daidzin, L-tryptophan) and plant-growth-promoting (e.g., isopentenyladenine, melatonin, and indole-3-propionic acid) activities. In parallel, L-proline emerged as a critical microbial interaction modulator. Targeted quantification showed a clear proline abundance gradient: highest in the C. rosea monoculture, intermediate in co-culture, and lowest in the B. velezensis monoculture. This gradient suggests that proline produced by C. rosea is likely utilized by B. velezensis, a finding further supported by the observation that proline enhanced bacterial biofilm formation and upregulated the matrix genes epsC and tasA. Accordingly, the co-culture itself formed significantly more robust biofilms. Thus, the enhanced biocontrol can be attributed to synergistic metabolite accumulation together with proline-mediated fitness gains in the bacterial partner, establishing a metabolic basis for rationally engineering microbial consortia.

1. Introduction

Root-knot nematodes (Meloidogyne spp.) are globally devastating plant-parasitic nematodes, infecting over 3000 plant species and causing substantial economic losses [1]. Meloidogyne incognita represents a particularly prevalent and damaging species, posing a severe threat to vegetable production [2,3]. Their lifecycle involves eggs, four juvenile stages, and the adult phase. The second-stage juvenile (J2), which hatches from the egg, serves as the only infective and motile stage. J2s invade host roots, induce feeding sites, and subsequently molt three times to develop into a sedentary, egg-laying adult, leading to root gall formation [4]. Therefore, the most vulnerable targets for management are the invasive J2s and the eggs [5]. Aligned with sustainable agriculture, biocontrol has emerged as a pivotal strategy to manage root-knot nematode infestations.

Various microorganisms have been explored as biocontrol agents against root-knot nematodes [2]. Among these, the ascomycete fungus Clonostachys rosea represents a highly promising candidate. It employs multiple antagonistic mechanisms, including parasitism, nutrient competition, secretion of cell wall-degrading enzyme and antibiotic production [6]. Thus, C. rosea exhibits direct nematicidal activity by parasitizing eggs, inhibiting hatching, and causing mortality in second-stage juveniles (J2s) of M. incognita [7,8]. Furthermore, it produces toxic secondary metabolites such as verticillin and epipolysulfanyldioxopiperazines, which are effective against various nematodes [9]. However, the practical application of C. rosea is often limited by its variable efficacy and moderate colonization rate in field soils, due to environmental pressures like nutrient competition and variable soil conditions.

In contrast, Bacillus species are well-characterized plant growth-promoting rhizobacteria with proven biocontrol capabilities [10,11]. It has been demonstrated that they suppress nematodes by producing nematicidal metabolites, inducing plant systemic resistance, and competing for resources [12]. For example, B. subtilis produces fengycin and surfactin lipopeptides, which disrupt root-knot nematode cuticles, while B. amyloliquefaciens induces systemic resistance in plants, enhancing defenses against cyst nematodes [13,14]. Critically, the efficacy of these antagonistic strategies is often dependent on a key colonization trait: the formation of robust biofilms on root surfaces. These biofilms enhance environmental stress tolerance, facilitate microbial communication, and enable the sustained release of metabolites, thereby ensuring stable root colonization and consistent biocontrol activity [15].

The complementary modes of action of C. rosea and Bacillus spp. suggest that their combined use could yield synergistic effects against nematodes. However, their direct combination into a stable formulation faces practical barriers due to physiological incompatibilities in growth rate, morphology, and nutrient requirements. Microbial co-cultivation offers an alternative strategy to bypass these constraints, as growing the strains together can stimulate synergistic interactions and profound metabolic reprogramming not observed in axenic monocultures. For instance, co-culture of B. amyloliquefaciens with Trichoderma asperellum enhances antifungal activity and diversifies secondary metabolites [16], with a follow-up study confirming that it also upregulated fungal signaling pathways, increasing expression of genes for sporulation, secondary metabolism, and plant growth promotion [17]. Similarly, interactions between Aspergillus nidulans and Streptomyces rapamycinicus can activate silent biosynthetic clusters, leading to novel compounds [18]. Most pertinently, direct evidence comes from the co-culture of C. rosea with B. subtilis, which yields a filtrate with superior biocontrol activity against pathogenic fungi and nematodes, alongside plant growth promotion, compared to monocultures [19]. Thus, to realize this synergy, the co-culture requires systematic, mechanism-based optimization beyond simple mixing.

The development of effective multispecies formulations from in vitro synergy relies on optimizing key parameters: strain compatibility, complementary modes of action, and niche co-colonization [20]. We therefore hypothesized that systematic co-cultivation of C. rosea NF-06 and B. velezensis YB-1652 would induce synergy, enhancing bioactive metabolite production and, ultimately, the efficacy against M. incognita and plant growth, relative to individual cultures. Therefore, the objectives of this study were to establish and optimize their liquid co-culture system, to determine the optimized system’s biocontrol and plant-growth-promoting effects, and to investigate the underlying synergy through metabolomic identification of key differential metabolites.

2. Materials and Methods

2.1. Microbial Strains, Plant Materials and Growth Conditions

The fungal strain Clonostachys rosea NF-06, isolated from tomato (Solanum lycopersicum L.) roots infected with root-knot nematodes in Kaifeng, Henan Province, China, was selected based on its strong nematicidal activity against Meloidogyne incognita [21]. The strain has been deposited in the China General Microbiological Culture Collection Center under the accession number CGMCC No. 16262. The plant growth-promoting bacterial strain Bacillus velezensis YB-1652 was isolated from peanut rhizosphere soil in Zhumadian, Henan Province, China, which exhibits multiple plant growth-promoting properties, including the production of indole-3-acetic acid, phosphate solubilization, and siderophore secretion. It was identified by sequencing its 16S rRNA and gyrA gene, which showed >99% similarity to B. velezensis type strains. The strain is publicly available under accession number CGMCC No. 36152.

Tomato seeds (cv. Zhongza 9) were surface-sterilized in 75% (v/v) ethanol for 30 s, rinsed thoroughly with sterile distilled water, and sown into a sterile substrate mixture (peat:vermiculite:perlite = 3:1:1, v/v/v) within 50-cell seedling trays. Trays were placed in a growth chamber at 25 °C, 70% relative humidity (RH), under a 16 h light/8 h dark photoperiod. Seedlings were grown under these conditions for 20 days to ensure uniformity. Subsequently, seedlings at the two-true-leaf stage were transplanted into individual plastic pots (10 cm diameter × 12 cm height) filled with sterile soil. Plants were maintained in the same growth chamber under identical environmental conditions. They were watered as needed with sterile water, and no fertilizer was applied to standardize nutritional status prior to experimental treatments. This protocol provided standardized plant material for subsequent trials.

2.2. Preparation of Meloidogyne incognita Inoculum

The Meloidogyne incognita inoculum was derived from a pure culture originally isolated from infected tomato roots in Kaifeng, Henan Province, China, in 2021. This population was propagated on tomato plants in a growth chamber as described in 2.1 and subcultured onto new plants every 8–10 weeks to ensure inoculum viability. Species identity was confirmed through both morphological examination of adult female perineal patterns and molecular analysis via PCR using M. incognita-specific primers [22].

For experimental inoculum, egg masses were hand-picked from infected roots, surface-sterilized with 1% (v/v) H₂O₂ for 3 min, and rinsed thoroughly with sterile distilled water. The sterilized egg masses were then incubated in sterile water at 25 °C to allow hatching of J2s. Freshly hatched J2s were collected using a Baermann funnel [23], and their viability was confirmed under a light microscope. The juveniles’ suspension was adjusted to a density of 2000 J2s/mL with sterile tap water for subsequent bioassays.

2.3. Establishment of the Clonostachys rosea and Bacillus velezensis Co-Culture System

2.3.1. Seed Culture Preparation

Seed cultures were prepared by separate fermentation. A single colony of C. rosea NF-06 from a potato dextrose agar (PDA; containing 200 g/L potato extract, 20 g/L glucose and 20 g/L agar) was inoculated into 100 mL of potato dextrose broth (PDB; 200 g/L potato extract, 20 g/L glucose) and incubated at 25 °C with shaking at 150 rpm for 48 h. Similarly, a single colony of B. velezensis YB-1652 from a Luria-Bertani agar (LA; 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, 20 g/L agar) plate was inoculated into 100 mL of Luria-Bertani broth (LB; 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl) and incubated at 30 °C with shaking at 180 rpm for 48 h. The fungal conidial concentration was standardized to 1.0 × 10⁷ conidia/mL using a hemocytometer (Brandel, Wertheim, Germany), while the bacterial cell density was standardized to 1.0 × 10⁸ CFU/mL by serial dilution plating. Thus, a 2% (v/v) inoculation of these standardized cultures resulted in initial densities of approximately 2.0 × 10⁵ conidia/mL for C. rosea and 2.0 × 10⁶ CFU/mL for B. velezensis in the fresh medium. These standardized cultures served as inocula for all subsequent experiments.

2.3.2. Co-Culture Medium Screening

Four semi-synthetic media were evaluated for co-culture performance. Each contained a basal composition of 20 g/L maize meal, 0.5 g/L MgSO₄, 0.05 g/L FeSO₄·7H₂O, and 0.05 g/L ZnSO₄·7H₂O, and differed only in the nitrogen source: Medium 1 (5 g/L wheat bran), Medium 2 (10 g/L wheat bran), Medium 3 (5 g/L soybean meal, a defatted soybean residue), and Medium 4 (10 g/L soybean meal). The total nitrogen content of the wheat bran and soybean meal batches used, determined by the Kjeldahl method [24], was 2.2% and 6.1% (w/w), respectively. PDB was used as a conventional medium control.

For each medium, four treatments were prepared in parallel: (1) simultaneous co-culture (2% v/v C. rosea + 2% v/v B. velezensis), (2) C. rosea monoculture (2%, v/v), (3) B. velezensis monoculture (2%, v/v), and (4) a 1:1 (v/v) physical mixture of the two monoculture broths, each at the same 2% (v/v) inoculum level as used in the co-culture. The protocol for preparing the physical mixture control, as detailed above, was employed consistently in all relevant experiments throughout this study, which served to differentiate the effects of active microbial interaction from those of simply doubling the total microbial biomass or metabolites. All cultures were incubated in 250-mL Erlenmeyer flasks containing 100 mL of the respective medium, autoclaved at 121 °C for 20 min prior to inoculation. Flasks were incubated at 28 °C with shaking at 180 rpm for 48 h. All treatments were performed with three biological replicates. The experiment was repeated twice independently, and data were combined for analysis, yielding six replicates (n = 6) per treatment.

After incubation, the broths were centrifuged (10,000× g, 5 min, 4 °C). The supernatants were filter-sterilized through a 0.22-μm membrane to obtain cell-free fermentation filtrates. The nematicidal activity of these filtrates was assessed against the J2s of M. incognita. Specifically, 100 μL of filtrate was mixed with an equal volume of M. incognita J2 suspension (2000 J2s/mL) in a 96-well plate. Uninoculated medium served as the negative control. After 48 h of exposure at 25 °C, immobile J2s were probed with 4% NaOH (w/v) to confirm mortality [25]. The relative mortality, which corrects for natural mortality in the control, was calculated using the following formula:

Relative mortality (%) = [(T − C)/(100 − C)] × 100

(1)

where T is the percentage mortality in the treatment, and C is the percentage mortality in the negative control.

2.3.3. Evaluation of Inoculation Protocols and Comprehensive Efficacy Assessment

Based on initial screening, Medium 4 was selected for further optimization. Two inoculation protocols were compared: (i) Simultaneous co-culture: simultaneous inoculation of C. rosea and B. velezensis (each at 2%, v/v), incubated for 48 h; (ii) Sequential co-culture: inoculation with C. rosea (2%, v/v) for 24 h, followed by addition of B. velezensis (2%, v/v) and a further 24 h incubation. Controls groups consisted of each strain grown in monoculture (2%, v/v) and a 1:1 (v/v) physical mixture of monoculture broths. Microbial growth was quantified by counting fungal conidia (hemocytometer) and bacterial CFUs (serial dilution plating on LA). Nematicidal activity of cell-free fermentation filtrates was assessed as described in Section 2.3.2.

For the plant growth-promotion assay, tomato seeds were surface-sterilized and rinsed as described in Section 2.1. Instead of direct sowing, the seeds were soaked in the respective fermentation broth for 4 h at room temperature with gentle agitation. Seeds soaked in sterile, uninoculated medium served as the negative control. Following treatment, seeds were sown into the sterile substrate mixture and placed in the growth chamber under the standardized conditions detailed in Section 2.1. After 20 days of growth, seedlings were carefully harvested for measurement. Shoot height was determined from the cotyledon node to the apical meristem, root length was the longest primary root, and whole-seedling fresh weight was measured after gentle blotting to remove surface moisture.

The experiment included two independent runs. In each run, treatments were prepared in three biological replicate flasks, and data from both runs were pooled for analysis. Final sample sizes were as follows: for microbial growth and nematicidal activity, n = 6 (2 runs × 3 biological replicates); for the plant growth-promotion assay, n = 12 seedlings per treatment (2 runs × 6 seedlings per run).

2.3.4. Pot Experiment for In Vivo Biocontrol Efficacy Assessment

The in vivo biocontrol efficacy of the various fermentation products against M. incognita was assessed in a pot experiment. The experiment included six treatments: (1) C. rosea NF-06 monoculture fermentation broth; (2) B. velezensis YB-1652 monoculture fermentation broth; (3) broth from their simultaneous co-culture; (4) a 1:1 (v/v) physical mixture of the separately prepared monoculture broths; (5) a chemical control using a commercial 0.5% abamectin granule formulation (a nematicide commonly used in agricultural production, Noposion, Shenzhen, China); and (6) an untreated control (sterile water). All liquid treatments were applied as a soil drench at 10 mL per seedling immediately after transplanting, while the chemical control was applied by placing 2 g of granules into the planting hole. Plants were maintained in a growth chamber as described in Section 2.1.

Two days after transplanting, each tomato seedling was inoculated with M. incognita by depositing approximately 1000 J2s (in 0.5 mL suspension) into each of two 1-cm-deep holes made 2 cm from the stem base, resulting in a total inoculum of about 2000 J2s per plant. Plants were harvested 40 days after nematode inoculation. Root systems were gently washed free of soil. Disease severity was rated on a standard 0–5 scale according to the percentage of galled roots [26]. The root-knot index and control efficacy were then calculated as described previously [27]. In addition, plant growth was evaluated by recording shoot height, root length, and fresh weight. The experiment followed a completely randomized design with 6 replicates (plants) per treatment and was repeated independently once, resulting in a total of 12 plants per treatment (n = 12).

2.4. Non-Targeted Metabolite Profiling

2.4.1. Sample Preparation for Non-Targeted Metabolite Profiling

Cell-free fermentation filtrates were obtained from six biologically independent replicates (each initiated from a unique starter culture and processed in a separate batch) of each cultivation condition: C. rosea and B. velezensis monocultures, and their simultaneous co-culture, as described in Section 2.3.3. For metabolite extraction, 50 µL of each thawed fermentation filtrate was mixed with 150 µL of a pre-cooled extraction solvent (methanol:acetonitrile = 4:1, v/v) containing a mixture of internal standards. The mixture was vortexed vigorously for 3 min and then centrifuged at 12,000× g for 10 min at 4 °C. A 150 µL aliquot of the supernatant was transferred to a new vial, placed at −20 °C for 30 min to precipitate residual proteins, and centrifuged again under the same conditions for 3 min. Finally, 120 µL of the supernatant was collected and transferred to a glass insert for LC-MS analysis.

2.4.2. LC-MS Analysis

Metabolomic profiling was conducted using a Vanquish UHPLC system coupled with a Q Exactive HF-X hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Chromatographic separation was performed on a Waters ACQUITY Premier HSS T3 column (1.8 µm, 2.1 × 100 mm) maintained at 40 °C. Mass spectrometry detection was operated in both positive and negative electrospray ionization (ESI) modes. A pooled quality control (QC) sample, generated by combining equal volumes of all experimental samples, was injected at regular intervals throughout the analytical sequence to monitor system stability and ensure data quality.

2.4.3. Data Processing

The raw data files were converted to mzML format and processed using the XCMS package (v3.18.0) in R (version 4.2.0) for feature detection, retention time alignment, and peak integration [28]. Metabolic features with a relative standard deviation (RSD) > 30% in the QC samples were removed to ensure data robustness. The resulting cleaned dataset contained 6159 metabolic features defined by their accurate mass (m/z) and retention time (RT).

Principal component analysis (PCA) was performed using the ropls package (v1.30.0) in R to visualize global metabolic profiles and assess group separation [29]. Differentially abundant metabolites were identified using a combined threshold of a variable importance in projection (VIP) score > 1.0 from the OPLS-DA model and a p-value < 0.05 from Student’s t-test, with adjustment for multiple comparisons via the Benjamini-Hochberg false discovery rate (FDR) procedure. This approach aligns with established practices in the field [30,31,32]. The results were visualized in volcano plots [33]. A Venn diagram was constructed to illustrate the overlap of differential features between the co-culture versus each monoculture comparison [34].

2.4.4. Metabolite Annotation and Pathway Analysis

A tiered strategy was used to annotate significantly altered metabolic features. All differential features were queried against (1) an in-house database of authentic chemical standards, (2) public metabolite repositories including HMDB [35] and KEGG [36], and (3) the in silico prediction and spectral library within the GNPS platform [37]. High-confidence identifications were assigned to features that matched an authentic standard in our in-house database and were further supported by either (i) an MS/MS spectral similarity score > 0.8, or (ii) corroborating evidence from MetDNA [38]. This curated list of high-confidence metabolites was then subjected to KEGG pathway enrichment analysis using MetaboAnalyst 5.0 [39] to identify biological pathways significantly altered in the co-culture system.

2.5. Targeted LC-MS/MS Quantification of Proline

2.5.1. Sample Preparation for Targeted LC-MS/MS Analysis

For targeted LC-MS/MS quantification of proline, fermentation filtrates were obtained from three independent biological replicate cultures of C. rosea and B. velezensis monocultures, as well as from their simultaneous co-culture, as described in Section 2.3.3. Frozen aliquots were thawed at room temperature and vortexed to homogenize. For extraction, 200 µL of the sample was mixed with 800 µL of ice-cold methanol, vortexed for 5 min, and centrifuged at 12,000× g for 5 min at 4 °C. An 80 µL aliquot of the supernatant was filtered through a 0.22 µm PTFE syringe filter into a glass autosampler vial for subsequent LC-MS/MS analysis.

2.5.2. Chromatographic and Mass Spectrometric Conditions

Analysis was performed using an ultra-high-performance liquid chromatography system coupled to an AB Sciex Triple QuadTM 4500 mass spectrometer (SCIEX, Framingham, MA, USA). Chromatographic conditions: Separation was achieved on a Thermo ScientificTM HYPERSIL GOLD C18 column (3 µm, 2.1 × 100 mm) maintained at 35 °C. The mobile phases were (A) 0.1% (v/v) formic acid in water and (B) acetonitrile, delivered at a flow rate of 0.3 mL/min. The injection volume was 3 µL. The gradient program was: 0–3 min, 10% B; 3–6 min, 10–90% B; 6–6.5 min, 90% B; 6.5–6.6 min, 90–10% B; 6.6–10 min, 10% B. Detection was performed in positive electrospray ionization mode using a Turbo Spray^® ion source (SCIEX, Framingham, MA, USA). The relevant parameters were set as follows: ion spray voltage, +5500 V; source temperature, 550 °C; curtain gas, 30 psi; collision gas, 9 psi. Proline was quantified in multiple reaction monitoring mode by monitoring the transition at m/z 116.1 → 70.1.

2.5.3. Quantification and Quality Control

A calibration curve was constructed using a dilution series of an authentic L-proline standard (Sigma-Aldrich, St. Louis, MO, USA), with each concentration analyzed in triplicate. Linear regression of mean peak area versus standard concentration (µg/mL) yielded the calibration equation. Proline concentration in each sample extract was determined by interpolating its peak area against this curve. The final concentration in the original fermentation filtrate was calculated as: C_final (µg/mL) = c × V_total/v_sample, where c is the interpolated concentration, V_total is the total volume of the extraction mixture, and v_sample is the volume of the original fermentation filtrate used for extraction. Method reliability was monitored by analyzing a pooled QC sample after every 10 experimental injections and by interspersing calibration standards at low, medium, and high concentrations throughout the analytical sequence [40].

2.6. Functional Validation of L-Proline in the Co-Culture System

To access the role of L-proline in bacterial biofilm formation within the co-culture system, an in vitro assay was performed. Seed cultures of C. rosea and B. velezensis were prepared as described in Section 2.3.1. Subsequently, 2 mL of Medium 4 was dispensed into each well of a 12-well cell culture plate. Four treatment groups were set up in triplicate: (1) B. velezensis monoculture (2.0%, v/v); (2) Simultaneous co-culture (each partner at 2.0%, v/v); (3) B. velezensis (2.0%, v/v) supplemented with 0.1% (v/v) filter-sterilized C. rosea fermentation filtrate; (4) B. velezensis (2.0%, v/v) supplemented with 15 mM exogenous L-proline. A well containing only C. rosea NF-06 (2.0%, v/v) served as an additional control. All plates were incubated statically at 37 °C for 48 h.

2.6.1. Quantification of Biofilm Biomass by Crystal Violet Staining

After incubation, the supernatant containing planktonic cells was removed from each well. The adherent biofilms were then gently washed twice with sterile distilled water to remove any remaining non-adherent cells. The plates were subsequently inverted and dried at room temperature for 30 min prior to staining. Biofilms were stained with 2 mL of 0.1% (w/v) crystal violet for 10 min at room temperature, followed by thorough rinsing with distilled water to remove unbound dye [41]. The bound dye was eluted with 1 mL of 33% acetic acid for 30 min with gentle shaking. A 200 µL aliquot of the eluate was transferred to a 96-well plate, and its absorbance was measured at 595 nm using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA), with 33% acetic acid as the blank [42]. Relative biofilm biomass was quantified with three technical replicate wells per treatment in each of three independent biological experiments.

2.6.2. Biofilm Morphology Observation

For each treatment condition, scanning electron microscopy (SEM) was performed on three independent biological replicate biofilm samples. Biofilm samples were fixed with 1% (v/v) osmium tetroxide for 2 h at 25 °C, followed by stepwise dehydration in an ethanol gradient (30%, 50%, 70%, 80%, 90%, and 100%). Samples were critical-point dried using a standard protocol for microbial biofilms [43]. Dried samples were sputter-coated with gold and imaged using a Hitachi SU8100 scanning electron microscope (Hitachi, Tokyo, Japan) at an acceleration voltage of 3.0 kV.

2.6.3. Gene Expression Analysis

Total RNA was extracted from biofilm samples using RNAiso Plus (Takara, Dalian, China). For each treatment, RNA was extracted from three independent biofilm cultures. Genomic DNA was eliminated using the PrimeScript RT Reagent Kit with gDNA Eraser (Takara). First-strand cDNA was synthesized from 1 µg of total RNA per sample, following the manufacturer’s protocol. Quantitative PCR was performed with TB Green Premix Ex Taq II (Tli RNaseH Plus, Takara) on a QuantStudio 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The expression levels of biofilm-matrix genes (epsC, tasA) were analyzed using the 2^−∆∆Ct method [44]. The 16S rRNA gene served as the endogenous reference for normalization. Primer sequences are listed in Table S1. Each qPCR reaction was run in triplicate. The entire experiment was repeated three times independently.

2.7. Statistical Analysis

Statistical analysis was conducted for all quantitative data derived from the bioassays (nematicidal activity, plant growth parameters), pot experiments (disease index, plant biomass), and targeted metabolite quantification. The Shapiro–Wilk and Levene’s tests were used to assess normality and homogeneity of variances, respectively. Single-factor comparisons (e.g., among media or treatments at a given time point) were performed by one-way ANOVA followed by Tukey’s HSD test. Where variances were heterogeneous, data were transformed (log10); if assumptions remained unmet, the non-parametric Kruskal–Wallis test with Dunn’s post hoc correction was applied. In non-targeted metabolomics, differential features were identified using a threshold of VIP > 1.0 and p < 0.05 (Student’s t-test, with Benjamini-Hochberg FDR adjustment). All quantitative data were analyzed using GraphPad Prism (version 8.4.2, San Diego, CA, USA). Statistical significance was defined as p < 0.05.

3. Results

3.1. Screening of Co-Culture Media

The nematicidal activity against Meloidogyne incognita second-stage juveniles (J2s) was evaluated for filtrates from the Clonostachys rosea NF-06 monoculture, the Bacillus velezensis YB-1652 monoculture, their 1:1 (v/v) physical mixture, and their simultaneous co-culture across five different media (Media 1–4 and PDB). After 48 h of exposure, the cell-free fermentation filtrate from the co-culture grown in Medium 4 exhibited the highest efficacy, achieving 95.3% J2 mortality (Figure 1). This result was higher than that of the PDB control (72.0%), the individual monocultures (C. rosea: 89.0%; B. velezensis: 73.3%), and the physical mixture of the two monocultures (85.8%). In contrast, co-cultures grown in Media 1–3 resulted in mortality rates at or below 90%, indicating that the synergistic interaction between the two strains was strongly dependent on medium composition. Due to its superior ability to potentiate biocontrol activity, Medium 4 was selected for all subsequent experiments.

3.2. Evaluation of Inoculation Protocols and Comprehensive Efficacy Assessment

The performance of the C. rosea and B. velezensis co-culture system was strongly influenced by the inoculation protocols, as assessed by microbial growth, nematicidal activity, and plant growth promotion (Figure 2). Under the optimized Medium 4 (Figure 2A), simultaneous co-culture (CoSim) yielded the highest fungal conidial production (7.1 × 10⁸ conidia/mL), significantly exceeding both the C. rosea monoculture (5.9 × 10⁷ conidia/mL) and the sequential co-culture (CoSeq; 3.9 × 10⁷ conidia/mL). Conversely, the bacterial population was highest in B. velezensis monoculture (5.1 × 10¹¹ CFU/mL), with both CoSim (4.0 × 10¹¹ CFU/mL) and CoSeq (1.1 × 10¹¹ CFU/mL) showing significantly lower counts (Figure 2B). These results indicate that simultaneous inoculation favors fungal biomass accumulation while modulating bacterial growth in the shared environment.

CoSim also exhibited the strongest nematicidal effect against M. incognita J2s, causing 84.8% mortality at 24 h and 95.3% at 48 h, significantly exceeding all other treatments (Figure 2C). CoSeq resulted in lower mortality (69.1% at 24 h and 88.2% at 48 h), which was statistically similar to that of the C. rosea monoculture (71.0% and 89.0%, respectively). This suggests that simultaneous inoculation promotes a synergistic interaction that enhances the production or efficacy of nematicidal metabolites.

Furthermore, the CoSim treatment significantly promoted the growth of tomato seedlings compared to the control and the fungal monoculture, increasing shoot height, root length, and fresh weight by 34.1%, 24.3%, and 105.1%, respectively (Figure 2D–F). Notably, the growth-promoting effects of CoSim on shoot height and fresh weight were statistically comparable to those of the bacterial monoculture, suggesting that the observed benefits at this early developmental stage were primarily driven by metabolites derived from B. velezensis. These quantitative improvements corresponded to visible phenotypic advantages, including stronger apical dominance and more extensive root branching (Figure 2G).

3.3. In Vivo Biocontrol Efficacy of the Co-Culture Against Root-Knot Nematodes

The pot experiment confirmed the superior biocontrol performance of the CoSim treatment against M. incognita (

Table 1. In vivo biocontrol efficacy against Meloidogyne incognita and plant growth promotion by different microbial treatments in tomato.

Treatment	Root-Knot Index	Control Efficiency (%)	Shoot Height (cm)	Root Length (cm)	Shoot Dry Weight (g)
Cl	25.93 ± 0.74 c	55.1	45.40 ± 0.44 d	13.09 ± 0.34 d	2.07 ± 0.06 d
Ba	37.78 ± 1.28 b	34.6	50.86 ± 0.65 b	15.54 ± 0.32 b	2.22 ± 0.04 ab
CoSim	17.78 ± 1.28 d	69.2	53.09 ± 0.37 a	17.26 ± 0.45 a	2.35 ± 0.02 a
CoSeq	25.19 ± 0.74 c	56.4	46.33 ± 0.39 cd	13.27 ± 0.42 d	2.04 ± 0.05 bc
Cl+Ba	26.67 ± 1.28 c	53.8	47.41 ± 0.51 c	14.43 ± 0.36 c	1.93 ± 0.06 cd
Avi	18.52 ± 0.74 d	67.9	38.86 ± 0.38 e	12.87 ± 0.33 de	1.59 ± 0.03 e
Control	57.78 ± 1.28 a	/	33.89 ± 0.37 f	12.00 ± 0.34 e	1.32 ± 0.04 f

Cl, Clonostachys rosea NF-06 monoculture; Ba, Bacillus velezensis YB-1652 monoculture; CoSim, simultaneous co-culture; CoSeq, sequential co-culture; Cl+Ba, 1:1 (v/v) physical mixture of monoculture broths; Avi, chemical control (0.5% abamectin granules); Control, untreated plants. Data are presented as mean ± SE (n = 12). Within each column, different lowercase letters indicate significant differences among treatments (p < 0.05, one-way ANOVA with Tukey’s HSD test). “/” indicates not applicable (control group used as baseline for efficiency calculation).

). The root-knot index in the CoSim group (17.78) was significantly lower than that of the untreated control (57.78) and all other microbial treatments, including the C. rosea monoculture (25.93), the B. velezensis monoculture (37.78), the CoSeq treatment (25.19), and the physical mixture of the two monocultures (26.67). Consequently, CoSim achieved the highest control efficacy (69.2%), which was statistically comparable to that of the chemical control abamectin (67.9%).

In addition to nematode suppression, the CoSim treatment significantly enhanced tomato plant growth in the pot experiment under nematode stress (Table 1). CoSim treatment resulted in the greatest shoot height (53.09 cm), root length (17.26 cm), and shoot dry weight (2.35 g), representing increases of 56.7%, 43.8%, and 78.0%, respectively, compared to the control values (33.89 cm, 12.00 cm, and 1.32 g). Notably, CoSim also outperformed both individual monocultures and their physical mixture, indicating that co-cultivation induces a synergistic interaction that promotes plant growth and biomass accumulation. These results from the nematode challenge experiment are consistent with the plant growth-promoting effects initially observed in the seedling assay without nematodes (Figure 2).

3.4. Metabolomic Reprogramming in the Co-Culture

Metabolomic profiling of the monocultures and their co-culture was conducted using liquid chromatography–mass spectrometry (LC-MS) in both positive and negative ion modes. A total of 6159 metabolic features were detected across all samples. Analysis of the total ion chromatograms (TICs) revealed distinct and reproducible metabolic profiles for each condition, indicating significant differences in metabolite accumulation (Figure S1). Notably, the metabolic profile of the co-culture substantially diverged from that of the C. rosea monoculture and showed closer alignment with the profile of B. velezensis.

Principal component analysis (PCA) demonstrated clear separation among the groups, with the co-culture cluster positioned notably closer to B. velezensis than to C. rosea (Figure 3A). Comparative analysis identified 3192 features significantly altered in the co-culture relative to the C. rosea monoculture, and 1713 features altered relative to the B. velezensis monoculture (Figure 3C,D). Among these, 1124 features were altered in common, defining the core metabolic responses specifically triggered by co-cultivation (Figure 3B, Table S2). These results indicate that co-culture exerts a more profound reprogramming effect on the metabolome of C. rosea than on that of B. velezensis.

Based on stringent criteria (match to an authentic standard plus spectral similarity > 0.8 or MetDNA support), 1204 differential metabolites were confidently annotated (Table S3). The curated set of high-confidence differential metabolites was subjected to KEGG pathway enrichment analysis. Distinct reprogramming patterns were revealed between the co-culture and each monoculture. Compared to the C. rosea monoculture, the co-culture exhibited significant enrichment in pathways primarily associated with amino acid metabolism and biosynthesis (Figure 3E). Key enriched pathways included arginine and proline metabolism (ko00330), tryptophan metabolism (ko00380), phenylalanine metabolism (ko00360), and the biosynthesis of amino acids (ko01230). Furthermore, pathways involved in the biosynthesis of secondary metabolites (ko01110) and 2-oxocarboxylic acid metabolism (ko01210) were also notably altered. In contrast, a distinct metabolic reprogramming pattern was observed when the co-culture was compared to the B. velezensis monoculture, with a marked emphasis on lipid metabolism (Figure 3F). Significantly enriched pathways included glycerophospholipid metabolism (ko00564), alpha-linolenic acid metabolism (ko00592), linoleic acid metabolism (ko00591), and arachidonic acid metabolism (ko00590). Furthermore, enrichment was also noted in several amino acid metabolism pathways, including alanine, aspartate and glutamate metabolism (ko00250), arginine and proline metabolism (ko00330) and phenylalanine metabolism (ko00360).

Notably, arginine and proline metabolism (ko00330) was commonly enriched in both comparisons. Within this pathway, L-proline was identified as a key differentially regulated metabolite. Consistently, quantitative analysis confirmed a substantially higher proline content in the C. rosea monoculture than in the B. velezensis monoculture. Although proline levels in the co-culture decreased compared to the C. rosea monoculture, they remained significantly higher than those in the Bacillus monoculture (Table S3).

Beyond L-proline, the co-culture system accumulated a suite of additional metabolites with putative functional significance, as detailed in

Table 2. Key differentially abundant metabolites identified in the co-culture system of Clonostachys rosea and Bacillus velezensis.

Compound	Molecular Formula	Exact Mass	Observed Mass	Adduct	Retention Time	Function	Reference
		(Da)	(Da)		(min)
L-Tryptophan	C11H12N2O2	204.0899	205.0972	[M+H]+	2.2126	Plant growth promotion, nematocidal	[45,46]
Daidzin	C21H20O9	416.1107	417.1195	[M+H]+	3.2258	Egg hatching inhibation, nematocidal	[47]
Melatonin	C13H16N2O2	232.1212	231.1061	[M-H]-	3.8312	Plant growth and yield promotion	[48]
Indole-3-propionic acid	C11H11NO2	189.079	234.0764	[M+HCOO]-	3.3194	Root growth regulation	[49]
Isopentenyladenine	C10H13N5	203.1171	204.1245	[M+H]+	3.6888	Key regulators for plant development and stress adaptation	[50]
L-Proline	C5H9NO2	115.0633	116.0707	[M+H]+	0.7841	Promote the formation of bacterial biofilms	[51]

and Figure 4. These can be categorized into two major groups based on their reported biological activities. First, key plant growth-regulating compounds, including isopentenyladenine, melatonin, and indole-3-propionic acid, were significantly upregulated in the co-culture compared to both monocultures (Table S3). Second, metabolites with direct nematicidal activity were markedly increased. Notably, this group included L-tryptophan, which is known to possess both plant growth-promoting and nematicidal properties, and daidzin, an isoflavone glycoside reported to inhibit nematode egg hatching and nematicidal effects. The co-accumulation of these compounds suggests that the co-culture employs a multi-pronged strategy, concurrently enhancing plant health and directly targeting nematodes.

3.5. Targeted LC-MS/MS Validation and Quantification of Proline

Targeted LC-MS/MS analysis based on an authentic standard was carried out to validate and quantify proline, a key metabolite from the non-targeted screen. The protonated ion [M+H]⁺ of proline (theoretical m/z 116.0707) was detected at m/z 115.9 for the standard and m/z 116.0 in the samples (Figure 5A). These measured values are within the accepted mass accuracy tolerance (±0.5 Da) specified for targeted quantification on the unit-resolution AB Sciex 4500 triple quadrupole system. More critically, the tandem mass spectrometry (MS/MS) fragmentation pattern of the analyte (precursor m/z 116.0) aligned perfectly with that of the authentic standard (precursor m/z 115.9), conclusively confirming the presence of proline (Figure 5B,C). The extracted ion chromatogram (XIC) at m/z 116.0 further verified proline detection and demonstrated its effective chromatographic separation from other matrix components (Figure 5D).

Quantification was performed using an external calibration curve, which exhibited excellent linearity (R² > 0.999) across the tested concentration range, ensuring accurate and precise measurements (Figure 5E). Consistent with the trend identified by non-targeted metabolomics, absolute quantification confirmed a markedly elevated concentration of proline in the C. rosea monoculture. The proline level in the co-culture was significantly lower than that in the C. rosea monoculture but remained substantially higher than the concentration detected in the B. velezensis monoculture (Figure 5F). This pattern suggests that proline produced by C. rosea is likely utilized by B. velezensis.

3.6. Fungal-Derived Proline Elicits Robust Biofilm Formation in Bacillus velezensis

L-Proline has been established in prior studies as a key metabolite capable of modulating bacterial biofilm architecture and stability. To investigate whether fungal-derived L-proline triggers biofilm formation in B. velezensis, we examined biofilm phenotypes and associated molecular responses. Macroscopic and quantitative crystal violet assays revealed that biofilm biomass was significantly enhanced in B. velezensis under three conditions: co-culture with C. rosea, supplementation with filter-sterilized C. rosea fermentation filtrate, or supplementation with pure L-proline, compared to the bacterial monoculture alone (Figure 6A,B). No biofilm was detected in the fungal monoculture (Figure S2).

At the molecular level, the expression of key biofilm matrix genes, epsC (exopolysaccharide biosynthesis) and tasA (major matrix protein), was significantly upregulated in B. velezensis under the same three inducing conditions (Figure 6C). Scanning electron microscopy visually confirmed these findings, showing dense, matrix-encased bacterial aggregates in co-culture and in the presence of fungal fermentation filtrate or L-proline, in contrast to the dispersed cells in the monoculture (Figure 6D).

4. Discussion

In natural environments, bacteria and fungi establish close physical and metabolic associations that critically shape their physiology and ecological roles in shared niches such as the soil and rhizosphere [52]. Laboratory co-culture systems have become essential tools for investigating these complex interactions under controlled conditions. Such systems frequently activate biosynthetic pathways that remain silent in axenic monocultures, leading to the production of novel bioactive metabolites [18]. For instance, co-culture of the marine fungus Pestalotia sp. with a Gram-negative bacterium induced synthesis of the antibiotic pestalone, which was absent in monocultures [53]. Similarly, Bacillus subtilis has been shown to trigger extensive metabolic remodeling in Fusarium tricinctum, boosting the yield of certain secondary metabolites by up to 78-fold, such as lateropyrone and the lipopeptide fusaristatin A [54]. These examples highlight the potential of microbial co-cultures to reprogram metabolic output, offering promising routes for developing novel biocontrol agents.

However, translating this potential into effective field consortia remains challenging [55]. In practice, deliberate combinations of microbial strains often lead to antagonistic interactions that suppress the growth or activity of one or both partners, ultimately compromising consortium performance [56]. This reveals a critical difference between the demonstrated metabolic potential of mixed strains under controlled conditions and their successful deployment as robust biocontrol agents in complex environments. To bridge this gap, it is essential to shift from simply mixing strains toward the rational design of consortia based on a mechanistic understanding of microbial interactions. Although phenotypic enhancement of biocontrol has been observed in co-cultures of C. rosea with Bacillus species, the specific metabolic foundations and optimal cultivation conditions that drive such synergy are still poorly understood. Therefore, this study aimed to systematically optimize the partnership between C. rosea NF-06 and B. velezensis YB-1652, with the goal of elucidating its synergistic mechanism and evaluating its potential for enhanced biocontrol.

The optimization revealed that the synergistic effect depended critically on using a maize meal-based medium supplemented with soybean meal (10 g/L) as the nitrogen source (medium 4), together with simultaneous inoculation. Under these conditions, the consortium suppressed M. incognita and enhanced tomato growth significantly more than individual monocultures or a physical mixture, confirming that improvement arose from active biological interaction, not an additive effect. The choice of nitrogen source (soybean meal versus wheat bran) is a primary determinant of synergy, as evidenced by prior work showing that the production of antifungal lipopeptides and peptaibols in a Bacillus-Trichoderma co-culture is highly dependent on specific nitrogen availability [16]. In our system, soybean meal likely provided a balanced nutritional background contribute to initiating synergistic metabolism. Furthermore, the inoculation protocol was decisive. Simultaneous inoculation promoted fungal biomass accumulation while modulating bacterial growth, suggesting C. rosea rapidly establishes a dominant yet non-suppressive presence. This may involve preferential resource access coupled with the early secretion of metabolites that fine-tune bacterial activity, thereby fostering a synergistic balance rather than antagonism. Similarly, Ola et al. [54] showed that pre-culturing Bacillus subtilis on solid medium optimally promoted fungal metabolite accumulation in a subsequent co-culture with Fusarium tricinctum. Thus, systematic optimization of both medium and inoculation protocol is essential to translate a co-culture into a stable, effective, and scalable biocontrol product.

Beyond biocontrol enhancement, optimization of the co-culture system induced global metabolic reshaping. Metabolomic profiling revealed a distinct metabolic state in co-culture, characterized by significant shifts in major pathway activities, resulting in specific accumulation of key bioactive metabolites. These metabolites were classified into two functional groups: direct effectors, which act on the nematode or plant, and microbial interaction modulators, which stabilize and enhance the partnership itself.

Direct effectors included compounds with explicit nematicidal or plant-growth-promoting functions. For instance, the direct antagonism against M. incognita was associated with a marked up-regulation of L-tryptophan, a metabolite with established dual nematicidal and phytostimulatory activities [45,46]. Notably, the co-culture not only accumulated tryptophan but also concurrently elevated the level of indole-3-propionic acid, for which tryptophan serves as the direct biosynthetic precursor. This co-accumulation is mechanistically significant because, among known microbial biosynthetic routes, indole-3-propionic acid is produced predominantly via a specific enzyme-mediated reductive pathway from tryptophan [57,58]. The coordinated increase in both metabolites indicates that the interaction between C. rosea and B. velezensis activates or enhances this complete metabolic route from tryptophan to indole-3-propionic acid. As an auxin-related metabolite involved in root-architecture regulation [49], the microbially derived indole-3-propionic acid likely contributed to the improved root phenotypes observed, directly linking metabolic interaction to a key phenotypic outcome. The co-culture also specifically elevated the level of daidzin, an isoflavone glycoside reported to inhibit nematode egg hatching, thereby contributing to direct nematicidal activity [47]. Furthermore, synergistic enhancement was observed for two key phytoeffectors: the cytokinin isopentenyladenine, a master regulator of plant development and stress adaptation [50], and the pleiotropic plant growth regulator melatonin, which promotes plant growth and yield [48]. Therefore, the superior plant-growth-promoting and nematicidal efficacy of the co-culture system can be attributed to the concerted accumulation of these functionally diverse metabolites.

A key microbial interaction modulator was identified as L-proline. Enrichment analysis highlighted significant alterations in amino acid metabolism, particularly within the arginine and proline pathways. Non-targeted metabolomics revealed a distinct concentration gradient: L-proline was most abundant in the C. rosea monoculture, intermediate in the co-culture, and lowest in the B. velezensis monoculture. Targeted LC-MS/MS quantification robustly confirmed this gradient. This observed gradient is consistent with the biosynthetic and secretory capacity of C. rosea, and likely reflects its role as the primary producer. The fungus synthesizes proline primarily via the conserved glutamate pathway, driven by the key enzymes Pro1 (glutamate kinase), Pro2 (γ-glutamyl phosphate reductase), and Pro3 (pyrroline-5-carboxylate reductase), with potential supplementary flux from the arginine-ornithine pathway [59]. Subsequently, secretion is facilitated by plasma-membrane-localized AAP/ABC amino acid transporters [60], establishing C. rosea as the dominant producer.

Functionally, while L-proline is known to participate in osmoregulation, nitrogen supply, and ROS homeostasis, its role as a key regulator of bacterial biofilm formation is particularly relevant to this study [51,61]. We confirmed that L-proline, supplied directly or via C. rosea culture filtrate, significantly enhanced biofilm formation in B. velezensis. This was evidenced by increased biomass, structural maturation visualized by scanning electron microscopy, and upregulation of the matrix genes epsC and tasA. Accordingly, the co-culture itself formed more robust biofilms. Previous reports suggest that proline metabolized within biofilms can be converted into products such as short-chain fatty acids, which help maintain a neutral local pH and enhance biofilm structural integrity [51]. Therefore, we propose that proline derived from C. rosea is likely utilized by B. velezensis not only as a metabolic substrate but also as a signaling molecule that promotes biofilm formation, representing a plausible core mechanism for the observed functional synergy.

The proline-driven enhancement of biofilm formation may play an important role in improving biocontrol efficacy against M. incognita. In Bacillus species, biofilm development is essential not only to rhizosphere colonization but also to the regulated synthesis of antagonistic lipopeptides such as fengycins and surfactins, processes that are often coordinated through shared regulatory mechanisms such as quorum sensing [62]. First, a more robust biofilm could improve the colonization competitiveness of the biocontrol bacterium. A stable biofilm matrix may enable B. velezensis to adhere more effectively to root surfaces, occupy a favorable niche, and better withstand environmental stress and competition from indigenous microorganisms, thereby supporting sustained biocontrol activity [63]. Second, the biofilm microenvironment can function as a localized metabolic site. It not only offers some protection to bacterial cells but may also help maintain a higher local concentration of bioactive metabolites, such as lipopeptides, at the root-soil interface [64]. These lipopeptides are well-established nematicidal agents capable of disrupting egg hatching, damaging juvenile cuticles, and interfering with nematode chemotaxis [12].

Thus, proline-enhanced biofilm formation likely strengthens biocontrol through two interconnected pathways: improving bacterial colonization and promoting the production of nematicidal metabolites. This suggests a potential cooperative cycle in which C. rosea supplies proline to stimulate biofilm development in B. velezensis; the enhanced biofilm, in turn, could help stabilize the consortium in the rhizosphere while also acting as a protected site for the sustained release of compounds such as lipopeptides. By linking improved root colonization with enhanced metabolite delivery, this integrated mechanism provides a plausible explanation for the more effective and consistent suppression of M. incognita achieved by the optimized co-culture compared to individual strains or a simple mixture.

5. Conclusions

Systematic optimization of the Clonostachys rosea and Bacillus velezensis partnership unlocked a synergistic interaction that surpassed the effects of individual strains or their mixture in both suppressing Meloidogyne incognita and promoting tomato growth. Metabolomic reprogramming underlies this synergy, characterized by the accumulation of complementary functional metabolites. The co-culture specifically accumulated direct-effect metabolites, including the nematicidal precursor L-tryptophan and its plant-growth-promoting derivative indole-3-propionic acid. Concurrently, L-proline, predominantly synthesized and secreted by C. rosea, was identified as a key inter-partner signal that primed robust biofilm formation in B. velezensis. This proline-enhanced biofilm is likely to consolidate bacterial root colonization and may amplify the localized production of antagonistic agents such as lipopeptides, thereby contributing to the consortium’s sustained efficacy. Collectively, our work elucidates the metabolic basis of this cross-kingdom synergy and offers a framework for designing consortia through targeted metabolite exchange (Figure 7). Future efforts should focus on field validation and clarifying how proline sensing regulates bacterial antagonism.