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Article

Functional Characterization of a Synthetic Bacterial Community (SynCom) and Its Impact on Gene Expression and Growth Promotion in Tomato

by
Mónica Montoya
1,2,*,
David Durán-Wendt
1,
Daniel Garrido-Sanz
1,
Laura Carrera-Ruiz
1,
David Vázquez-Arias
1,
Miguel Redondo-Nieto
1,
Marta Martín
1 and
Rafael Rivilla
1
1
Departamento de Biología, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain
2
Departamento de Química y Tecnología de Alimentos, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1794; https://doi.org/10.3390/agronomy15081794
Submission received: 9 June 2025 / Revised: 18 July 2025 / Accepted: 23 July 2025 / Published: 25 July 2025
(This article belongs to the Section Farming Sustainability)

Abstract

Sustainable agriculture requires replacing agrochemicals with environmentally friendly products. One alternative is bacterial inoculants with plant-growth-promoting (PGP) activity. Bacterial consortia offer advantages over single-strain inoculants, as they possess more PGP traits and allow the exploitation of bacterial synergies. Synthetic bacterial communities (SynComs) can be used as inoculants that are thoroughly characterized and assessed for efficiency and safety. Here, we describe the construction of a SynCom composed of seven bacterial strains isolated from the rhizosphere of tomato plants and other orchard vegetables. The strains were identified by 16S rDNA sequencing as Pseudomonas spp. (two isolates), Rhizobium sp., Ensifer sp., Microbacterium sp., Agromyces sp., and Chryseobacterium sp. The metagenome of the combined strains was sequenced, allowing the identification of PGP traits and the assembly of their individual genomes. These traits included nutrient mobilization, phytostimulation, and biocontrol. When inoculated into tomato plants in an agricultural soil, the SynCom caused minor effects in soil and rhizosphere bacterial communities. However, it had a high impact on the gene expression pattern of tomato plants. These effects were more significant at the systemic than at the local level, indicating a priming effect in the plant, as signaling through jasmonic acid and ethylene appeared to be altered.

1. Introduction

The use of bioinoculants for plant growth promotion (PGP) is an environmentally friendly fertilization strategy [1,2]. Bioinoculants, which can be composed of one or more plant growth promotion rhizobacteria (PGPRs), are characterized by an increase in the availability of nutrients in the rhizosphere and/or the promotion of other beneficial mechanisms for plant growth [3] such as phytostimulation and plant immune system induction. Under greenhouse and field conditions experiments, the synergistic effect between several plant-growth-promoting rhizobacteria on plant growth has been reported [4,5]. Rosa et al. [6] and Ríos-Ruiz et al. [7] demonstrated that inoculation with a bacterial consortium of sugarcane and maize, respectively, increased yield and allowed a decrease in chemical fertilization. Therefore, the use of bioinoculants formulated as a bacterial consortium with a combination of beneficial properties can result in a sustainable strategy to improve plant quality and yield [8] decreasing the use of chemical fertilizers. PGPRs used in biofertilization belong to a wide variety of genera, Agrobacterium, Azotobacter, Azospirillum, Bacillus, Burkholderia, Delfitia, Paenobacillus, Pantoea, Pseudomonas, Serratia, Streptomyces, and Rhizobium being among the most important [3].
The use of traditional consortia, i.e., consortia isolated from the rhizosphere and obtained by enrichment cultures, has several drawbacks. These include the presence of bacteria that do not play a role in plant growth promotion and the presence of undesired or opportunistic strains that might pose an environmental or sanitary risk [9]. Moreover, traditional consortia often lack reproducibility and mechanistic understanding, since their composition is shaped by ecological selection rather than functional design. In contrast, the use of synthetic communities (SynComs) appears to be a suitable alternative for the design of agricultural bioinoculants [10]. In particular, SynComs enable the intentional exclusion of undesirable strains, as well as nonfunctional members that might otherwise persist in enrichment-based consortia. Furthermore, the combination of culturomics with metagenomic technologies has been employed [11,12] to study the taxonomic diversity and metabolic potential of microbial communities [13], including synthetic communities. Technical advances in next-generation sequencing (NGS) have made it possible to provide more accurate assignment of the metagenomes, as is the case of the nanopore-based sequencing platform, MinION™, which produces long reads’ lengths [14]. Nevertheless, DNA-based metabolic and phylogenetic diversity analysis do not provide information on the expression state of the genes. To overcome this limitation, NGS technologies have also been applied to the study of transcriptomics employing RNA sequencing, offering genome-wide insight into gene expression profiles [15]. Therefore, the integration of these technologies in the study of the application of bacterial consortia and the response of agricultural crops represents a challenge to be addressed.
In this context, this study aimed to design and construct a SynCom to characterize its functional potential and evaluate its impact on the plant and soil microbiomes using combined metagenomic and transcriptomic analyses, in order to determine its possible use as an agricultural bioinoculant. It was hypothesized that (i) the constructed SynCom would exhibit many plant-growth promotion activities, (ii) SynCom inoculation would not alter the native soil microbiome, showing coalescence, and (iii) a transcriptomic analysis would reveal the activation of plant gene expression patterns associated with beneficial microbial interaction.

2. Materials and Methods

2.1. Isolation and Construction of the Rhizosphere Consortium and Growth Conditions

Bacteria for the consortium were isolated from the rhizosphere soil of tomato (Solanum lycopersicum) and pepper (Capsicum annuum) using a selective culture method. The rhizospheric soil was sieved to 2 mm and suspended in sterile saline solution (8.5% NaCl) and vigorously shaken for 1 h. Then, serial decimal dilutions were inoculated onto plates with various selective media: Sucrose-Asparagine (SA), as a pseudomonads-specific medium [16], Plate Count Agar (PCA), as a nutrient-rich medium [17], PCA diluted 1:10, a nutrient-limited medium, Yeast Mannitol Agar (YMA), as a semi-specific medium for rhizobia [18], and the Minimal Salt Medium (MM) [19] supplemented with 1 mL/L of phosphate-buffered mineral medium salts (PAS, [20]) and adding succinic acid, malic acid, and fructose (1:1:1), at a final concentration of 1 g/L, as the carbon and energy sources.
The inoculated plates were incubated for two days at 28 °C, and visually different colonies were picked and subcultured. These isolates were identified by amplifying the almost full-length 16S rRNA gene using the 27F(AGRGTTYGATYMTGGCTCAG) and 1492R (RGYTACCTTGTTACGACTT) universal primers and Sanger DNA sequencing (Sequencing Service of the Universidad Complutense de Madrid, Spain). Sequences were analyzed using the MEGA X software (11.0.11 version, [21]) and compared with the sequences deposited in the NCBI nt database [22]. In addition, the Clustal Omega 1.0.2 platform was also used for multiple-sequence alignment to compare homologous sequences [23].
Isolated strains selected for inclusion in the SynCom were identified as putative PGPR based on 16S rRNA gene sequence homology to previously described plant-growth-promoting taxa [3,24,25]. This taxonomic assignment was further supported by the identification of key functional genes involved in PGP traits, as revealed by metagenomic analysis of the consortium [12,23]. The SynCom was constructed in November 2022 using seven isolated strains: six of them were obtained from the rhizosphere of tomato, and one strain, Pseudomonas ogarae, was isolated from the rhizosphere of pepper. These selected bacteria were individually cultured in approximately 5 mL of MM and incubated during 48 h at 28 °C with shaking (200 rpm). Each of the cultures were aliquoted and preserved with glycerol (25%) at −80 °C. SynCom was routinely constructed by mixing 1 mL of each strain grown from the glycerol stock in PCA. The possible antagonist effect of each of the isolates against the others was tested by a dual-culture method [26].

2.2. Extraction of DNA, Shotgun Metagenome Sequencing, and Genomes Reconstruction

Metagenomic DNA was extracted from the SynCom with the NucleoSpin® Microbial DNA kit (Macherey-Nagel, Düren, Germany). The quality was determined on an agarose gel and quantified using a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). The library was prepared with a total of 1.4 µg of the DNA mixture in a final volume of 48 µL (approximately 30 ng/µL of DNA for each bacterium) using the Nanopore Ligation Sequencing kit (SQK-LSK-110). The metagenome was analyzed on a MinION sequencer (Oxford Nanopore Technologies, Oxford, UK) with a MinION flow cell (R10.4.1 pores). The base calling details and bioinformatic analysis (quality checking, filtering of ONT reads, de novo assembly, annotation, taxonomic classification, and genome reconstruction) are described in [27]. Furthermore, the species identification or closest relative genome was determined by the Type-Strain Genome Server (TYGS) platform [28]. Relevant genes for plant growth promotion were determined by the NCBI nucleotide database using blastn from BLAST® service [29]; additionally, gene clusters of bioactive compounds were identified using the AntiSMASH (v7.0) web application [30].

2.3. Bacterial Soil Community Analysis

An agricultural soil (terra rossa [31]) from a vine orchard at Mota del Cuervo in the Castilla-La Mancha region (Spain) was used to study the impact of the SynCom on the soil and tomato rhizosphere microbiomes. A total of twelve 50 mL tubes were arranged in quadruplicate with the following treatments: 1. control without plant and without SynCom (Soil), 2. plant without SynCom (Plant), and 3. plant with SynCom application (Plant + SynCom). Isogenic F1 seeds of Mistela tomato were sterilized by submerging them in 70% ethanol for 3 min with shaking, followed by a 10% sodium hypochlorite solution for 1 min with shaking. Then, ten washes were carried out for 10 min in sterile distilled water with shaking and the tomato seeds were placed on Petri dishes with 1% water–agar O/N in the dark at 28 °C for 48 h. The tubes were filled with 25 mL of a mixture of soil, sterile river sand, and sterile vermiculite in a ratio of 1:1:1, and 3 mL of sterile water was added. All systems (with and without seeds) were grown for three weeks in a plant chamber with a photoperiod (16 h of light 100 μmol/m2/s and 8 h of darkness) under controlled temperature (24 °C lights on/18 °C lights off). After the first week of growth, four of the tubes containing seedling were inoculated with 1 mL of the SynCom, grown on Plate Count Broth (PCB) at a concentration of 107 colony forming units per ml, and the rest of the tubes were mock-inoculated with 1 mL of PCB medium. In the second week all treatments were watered with 5 mL of a 8 mM KNO3-supplemented FP as the mineral solution [32] and five days later, with another additional 5 mL of this FP solution. At the end of the experiment, the aerial parts were cut close to the soil and removed. To all samples, sterile 75% NaCl was added up to the 25 mL mark and shaken for twenty minutes on a MultiReax shaker at 1200 rpm (Heidolph Instruments GmbH, Schwabach, Germany) in a cold chamber. After that, the samples were decanted for 15 min in a rack and the supernatant was transferred to a sterile Eppendorf and centrifugated for 5 min at 14,000 rpm.
The pellet obtained was used for the DNA extraction employing the commercial FastDNATM Spin Kit Soil (MP Biomedicals—Fisher scientific, Waltham, MA, USA) and quantified using a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). Microbial community composition was assessed by amplifying the V3-V4 hypervariable region of the 16S rRNA gene using the primers 16S_F (341F) (CCTACGGGNGGCWGCAG) and 16S_R (785R) (GACTACHVGGGTATCTAATCC). Genomic samples were sent to STABvida for library preparation and sample sequencing. Library preparation was performed using the MiSeq Reagent Kit v3 (Illumina), and sequencing was conducted on an Illumina MiSeq platform using a 2 × 300 bp paired-end protocol to achieve a sequencing depth of approximately 100,000 reads per sample. Denoising, dereplication, and chimera removal were performed using the R package DADA2 v1.18 [33] to identify Amplicon Sequence Variants (ASVs) in samples. Taxonomic classification was conducted in R using the naïve Bayes classifier implemented in DADA2 with the Silva v138.1 training set [34]. The ASV sequences were then exported to QIIME2 v2-2021.2 [35] for multiple sequence alignment and phylogenetic tree construction using “fasttree2” [36]. The resulting phylogenetic tree was imported back into R using the qiime2R package [37]. A phyloseq object [38] was then created to facilitate downstream statistical analyses and data visualization.

2.4. Plant RNA Isolation and Transcriptomic Analysis

The RNA-Seq assay was performed with twelve isogenic F1 tomato plants, six being used for the SynCom application and the rest for the non-inoculated plants that were used as controls. Seedlings were transferred to sterile 50 mL tubes containing 25 mL of sterile hydrated vermiculite and incubated under the controlled conditions of a plant chamber. After seven days, six tubes were inoculated with 1 mL (1 × 107 CFU/mL) of the SynCom culture, and the other six tubes were mock-inoculated with 1 mL of PCA liquid medium. Plants were grown for another two weeks, and then shoot and roots were collected and frozen for the transcriptomic analysis.
Regarding the extraction of plant RNA, frozen shoots and roots’ samples were ground separately with a mortar and liquid N2 was used until obtaining a powder. Plant tissue was transferred to a 1.5 mL Eppendorf tube and homogenized using the Z6 extraction buffer method [39] which contain guanidine hydrochloride 8M. Direct RNA extraction was then performed with phenol/chloroform and precipitated from the aqueous phase. The RNA was washed with sodium acetate 3M and 70% ethanol and finally dissolved in water [39]. Using this procedure, we isolated high-quality DNA-free RNA samples without DNase treatment that were then quantified using a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). The RNA sequencing was custom performed by Novogene. Directional mRNA libraries were prepared using a poly(A) enrichment approach to remove ribosomal RNA. The libraries were sequenced in triplicate on the Illumina NovaSeq X Plus platform in Strand-Specific Paired-End mode (2 × 150 bp), achieving an average sequencing depth of 6 gigabases per sample. RNA-Seq data from Solanum lycopersicum var. cerasiforme plants were analyzed using the version 4.1 genome reference provided by the International Tomato Genome Sequencing Project. The analysis pipeline included the following steps: quality control of the raw reads using FastQC, trimming of low-quality bases and adapter sequences with Trimmomatic, alignment of the reads to the reference genome using STAR, and quantification of gene expression levels by counting reads per gene with HTSeq-count.
Differentially expressed genes (DEGs) were identified using DESeq2 (v1.44) [40] in the R environment (v 4.4.1). Read counts were normalized using DESeq2′s negative binomial distribution model, and DEGs were determined based on a fold-change cutoff of 1.5 and a false discovery rate (FDR) threshold of 0.001.
Functional annotation of the tomato genome was performed to complement the existing annotation available in version 4.1. For this purpose, the BlastKOALA tool from the KEGG database [41] was used to determine KEGG orthology.

2.5. Statistical Analyses

Statistical evaluation of the sequencing data was carried out with R (v4.4.1, R Core Team (2022) using Rstudio (2023.3.0.386, Posit team 2023). The functional annotation for the metagenome was analyzed using the “SQMtools” package [42]. The diversity analyses were generated using “qiime2R” [35] and the “phyloseq” packages [38].
Diversity analysis of bacterial rhizospheric communities was carried out as follows. The Shannon alpha diversity index and the principal coordinate analysis using the Bray–Curtis metric were generated using the “vegan” package [43] and plotted using “ggplot2” [44]. The effect of “treatment” was tested by permutational multivariate analysis of variance (PERMANOVA) using the function “adonis”.

3. Results

3.1. SynCom Design and Construction

A total of 32 bacterial strains were isolated from rhizosphere soil in the different selective media. The isolated strains were identified to the genus level through 16S rRNA sequence analysis. Seven putative PGPR strains, identified as Pseudomonas spp. (two isolates), Rhizobium sp., Ensifer sp., Microbacterium sp., Agromyces sp., and Chryseobacterium sp. were selected for preparation of the synthetic community. The seven isolates did not show any antagonism in dual-culture plates.

3.2. Genomes Reconstruction (MAGs) and Functional Analysis

Metagenomic analysis of the constructed SynCom allowed us to assemble and reconstruct the genomes (MAGs) of the seven strains with a completion from 72 to 99.5% by the Squeeze-Meta pipeline (Table 1). Five MAGs allowed the identification to the species level (>70% Digital DNA/DNA hybridization): Pseudomonas monsensis, P. ogarae, Agromyces mediolanus, Microbacterium oxydans, and Chryseobacterium taeanense, with genomic sizes and %GC content congruent with the identified species. The other two MAGs, Rhizobium sp. and Ensifer sp., were only identified to the genus level and their closest relative genomes were determined to be Rhizobium metallidurans and Ensifer moreliensis, respectively (Table 1).
The results of the functional assignment showed multiple genes and clusters putatively involved in PGP activity of the SynCom (Table 2). Plant-growth promotion activity was divided into three main categories: biocontrol, nutrient mobilization, and phytohormone modulation. Regarding biocontrol, different clusters were identified in the genomes of P. monsensis and P. ogarae for the biosynthesis of potential antifungals such as lokisin and 2,4-diacetylphloroglucinol (DAPG), respectively. In addition, genes encoding components of type VI secretion systems and the cluster for the hydrocyanic acid production (hcnABC) were also found in the genomes of both pseudomonads. Hydrocyanic acid production genes were also present in A. mediolanus. Moreover, other possible antimicrobials identified as putative lantipeptides were also found to be encoded in the strains P. ogarae and C. taeanense. The C. taenensis and Ensifer sp. MAGs showed the presence of the cluster for the biosynthesis of the antifungal compound resorcinol. Two biosynthetic clusters for antimicrobial and antifungal compounds of the β-lactone and mycosubtilin families were also found in the Ensifer sp. strain.
The potential of the SynCom to mobilize nutrients was confirmed by the identification of several genes and clusters involved in phosphate mobilization in all the strains. Regarding iron mobilization, the cluster of the enantio-pyochelin biosynthesis was found in Rhizobium spp. and P. ogarae. Furthermore, the fecAR and hasDEF genes, which are also involved in the biosynthesis of iron siderophores, were also found in both pseudomonads. Both pseudomonads’ genomes showed the potential to produce and secrete the siderophore pyoverdine. The clusters for parabactin and achromobactin production were also found in Microbacterium and Rhizobium, respectively. The denitrification pathway clusters were also present in P. ogarae and Rizhobium sp.
Regarding the genes involved in phytohormone production, the gene cluster iaaHM, responsible for the biosynthesis of the auxin indole-3-acetic acid (IAA), was identified in P. monsensis. Moreover, a cluster for the degradation of auxin phenylacetic acid (PAA), which may be involved in plant–bacteria interaction, was also found in all strains of the SynCom. In addition, the P. ogarae genome showed the presence of the 1-aminocyclopropane-1-carboxylicacid (ACC) deaminase gene that contributes to the modulation of the plant hormone ethylene and alleviates abiotic stresses.
Other genes that might be implicated in plant growth promotion are the speE gene for the spermidine biosynthesis present in P. monsensis, Rhizobium, and Agromyces, and the pqq cluster in both Pseudomonas strains. Additionally, the fitD gene, which encodes an insect toxin, was detected in both pseudomonads’ genomes.

3.3. Effect of SynCom on Soil and Rhizosphere Bacterial Communities’ Composition and Diversity

The bacterial community of the soil was not apparently disturbed by the application of the SynCom (Figure 1), showing coalescence between the inoculum and the native bacterial populations. The rarefaction curves of the number of sequences versus the number of ASVs were asymptotic, indicating that complete community coverage was achieved. A comparison of the bacterial communities in the agricultural soil, independent of the treatment, revealed that Acinetobacter, Bacillus, Pseudomonas, Sphingomonas, and Rhizobium were the most abundant genera (Figure 1).
Alpha diversity was primarily assessed using the Shannon diversity index and showed no significant differences after the SynCom application (Figure 2a), suggesting that the inoculation of the SynCom had no effect on the diversity of the rhizosphere bacterial community. This lack of effect was confirmed by using other biodiversity indexes such as Chao 1, Simpson, and Faith PD. However, the beta-diversity analysis (Figure 2b) showed a clear difference between the soil and both rhizosphere communities. A difference was also observed between the non-inoculated and the SynCom-inoculated rhizosphere communities. Taken together, these results indicate that inoculation with the SynCom produced only slight changes in the rhizosphere community, the plant being the main driver of these changes.

3.4. Differential Gene Expression in Tomato Plants After Application of the SynCom

As shown in Figure 3a, the bacterial SynCom inoculated in this study induced significant differential gene expression in the host plant. Differential expression was determined using a cutoff of |log2FoldChange| ≥ 1.5 and an FDR-adjusted p-value ≤ 0.001. In the root tissue, 142 genes were upregulated, while 227 genes were downregulated. In contrast, the aerial part exhibited a more pronounced response, with 458 genes upregulated and 718 genes downregulated. This substantial differential gene expression in the aerial part highlights the systemic effect triggered by the SynCom.
It is interesting to note that only a small fraction of upregulated (8%) or downregulated (12%) genes were shared by root and aerial parts of the tomato, indicating that the SynCom triggered specific responses in root and aerial parts. Figure 3b shows the transcriptional profiles of root and shoot tissues with or without inoculation with the SynCom. Again, these profiles show the importance of the systemic response, since the modification of the transcriptional profile was more prominent in the shoot than in the root. Furthermore, the clustering analysis of differentially expressed genes (Figure S1) showed that the plant organ was the main driver of the gene expression pattern. Table S1 shows the most upregulated and downregulated genes in root and aerial parts after inoculation with the SynCom. The most differentially expressed genes included several transcriptional regulators, as well as numerous genes involved in plant defense responses, particularly those associated with pathogen-related signaling pathways. These included genes responsive to salicylate, ethylene, and jasmonate, oxidative burst and antioxidant mechanisms, and genes involved in physical barrier reinforcement. Among the most upregulated genes in the aerial part were genes encoding ethylene-responsive transcription factors AIL6 and BBM, ethylene-responsive proteinase inhibitors, defensin, patatin, chitinase, and pathogenesis-related protein 1, a marker of salicylic acid plant response.

4. Discussion

The rhizosphere is a dynamic ecological niche in which interactions between microbes and plants occur to take advantage of essential nutrients in a limited environment [45]. Beneficial bacteria, such as PGPR, live in these complex soil–plant systems, influencing plant growth and yield through a network of interactions [46]. Furthermore, PGPRs are a sustainable fertilization strategy that ensures adequate nutrition for plants [47], reducing the use of chemical fertilizer, maintaining or even increasing the yields [24]. These attributes have led researchers to the idea of co-inoculation by constructing and applying bacterial consortia in several crops [4,48], since consortia are more effective that individually applied strains [24]. According to Kumar et al. [4], the most common PGPR genera used are Rhizobium, Azospirillum, Azotobacter, Bacillus, Enterobacter, and Pseudomonas. In addition, other PGPRs also have important plant growth promotion activities, such as the genus Flavobacteria and Microbacteriaceae [49,50]. Therefore, the selection of PGPR strains for the design of the SynCom was constructed with seven of these putative PGPR strains: two Pseudomonas, Rhizobium, Ensifer, Microbacterium, Agromyces, and Chryseobacterium (Table 1). Moreover, Vessey and Jeyanthi et al. [1,3] suggested that a single PGPR exhibited multiple modes of action. However, functional redundancy in the bacterial consortium is also important, because if different taxa are capable of carrying out the same set of metabolic processes, they can easily replace each other [51,52]. Accordingly, our bacterial consortium was constructed following this principle of functional redundancy (Table 2). In addition, it is important to guarantee the safety of the consortium, avoiding selecting bacteria that may pose an environmental and/or health hazard. In this sense, all the selected strains belonged to Risk Group 1.
The functional potential of this SynCom was determined by high throughput metagenomic sequencing. Sequencing allowed the assembly of seven MAGs corresponding to the seven strains, confirming the initial identification and allowing the identification to the species level of five of the strains. Although most of the MAGs showed a completion higher than 90%, the Ensifer MAG showed only 72% completion and was highly fragmented. This could result in an underestimation of its PGPR capabilities. Our results also revealed that the SynCom might be involved in key plant growth processes, including biocontrol, siderophores production, mobilization of iron and phosphate (P), phytohormone modulation, plant–bacteria interaction, denitrification, and toxin secretion (Table 2).
The rhizobacteria can indirectly exert a positive influence on plant growth by reducing the harmful effect of some pathogens through the production of antagonistic substances [53,54]. One of the most effective mechanisms to prevent proliferation of phytopathogens is the synthesis of antimicrobials. The SynCom was shown to have the potential to produce several of these compounds, such as phloroglucinol, pyoluteorin, pyrrolnitrin, cyclic lipopeptides, and hydrogen cyanide (HCN), well known to be involved in biocontrol against phytopathogens [55]. Another efficient antimicrobial is DAPG, also putatively produced by pseudomonads in the SynCom. DAPG controls a wide variety of fungi by causing damage to the fungal membrane [56]. Garrido-Sanz et al. [57] reported that most P. protegens strains were able to inhibit the growth of the phytopathogens Fusarium graminearum and Pythium ultimum due to the DAPG biosynthesis. The results obtained by Almario et al., Vacheron et al., and Garrido-Sanz et al. [57,58,59] also demonstrated that DAPG-producing pseudomonads included species of the P. fluorescens group and were efficient at antimicrobial control. The potential production of resorcinol [60], lokisin [61], mycosubtilin [62], and 4-hydroxibenzoate [63] by several rhizobacteria in the SynCom can also be useful in combating phytopathogenic fungi, such as Rosellinia necatrix, Magnaporthe oryzae, Verticillium dahliae, Botrytis cinerea, or Fusarium graminearum. Therefore, the SynCom offers an environmentally friendly alternative to chemical pesticides and is an attractive means to protect plants against disease.
The SynCom encodes several mechanisms to influence soil fertility through the mobilization and mineralization of nutrients, such as P, iron, or nitrogen (N). Phosphorus is involved in many metabolic processes of plants, such as photosynthesis, energy transfer, signal transduction, macromolecular biosynthesis, and respiration [64]. Microorganisms improve P availability to plants by mineralizing organic P in soil and by solubilizing precipitated phosphates [25,65]. In this study, all strains in the SynCom showed the genetic ability to mobilize P. Bacterial genera like Flavobacterium, Microbacterium, Pseudomonas, and Rhizobium have been reported as some of the most significant phosphate solubilizing bacteria [50,66]. Several species of Rhizobium have also been shown to enhance growth and yield of several crops by the action of P solubilization [67]. In addition, Yadav et al. [68] indicated that Pseudomonas were phosphate solubilizer bacteria. Furthermore, Stajkovic et al. [69] reported a significant effect of co-inoculation with Pseudomonas and Rhizobium on the growth as well as N and P contents of common bean plants compared to inoculation with Rhizobium alone.
Being the fourth most abundant element, iron is highly important in soils for plant growth [70]. Iron plays a key role in electron transport chain, redox reactions, detoxification of reactive oxygen species, DNA synthesis, and in many other biochemical processes [71]. Our results indicated that many of the SynCom bacteria (except Chryseobacterium, Agromyces and Ensifer strains) could produce siderophores and subsequently increase the iron availability for the plant and/or eliminate this resource to phytopathogens. The ability of the Pseudomonas genus to produce siderophores is further supported in the reports of [72]. Moreover, Corretto et al. [66] reported that Microbacterium strains also produced siderophores.
Indole-3-acetic acid is the most common plant hormone of the auxin class and is produced by plants and microorganisms. [73]. This phytohormone auxin is a key regulator of many aspects of plant growth and development [74] and has been recognized as an important factor in direct plant-growth-promoting abilities of rhizosphere bacteria [75]. Pacurar et al. [76] also reported that the phytohormone auxin played an essential role in root development. In addition, the auxin signal is necessary in the process of mitosis as it regulates cell division [77]. The indole-3-acetamide (IAM) pathway is the best characterized pathway in bacteria. In this two-step pathway, tryptophan is first converted to IAM by the enzyme tryptophan-2-monooxygenase (IaaM). In the second step, IAM is converted to IAA by an IAM hydrolase (IaaH). Indole-3-acetic acid production has been reported by many studies in P. fluorescens and P. putida [78], Rhizobium spp. [79], and Microbacterium strains (although not in M. oxydans BEL163) [66]. This result is consistent with the fact that the IAA gene cluster was not present in the M. oxydans strain in the consortium but was encoded in the annotated genome of the P. monsensis strain. Regarding the second endogenous auxin, PAA has been identified as participating in plant growth and development [80,81]. Furthermore, the paa operon, involved in the PAA catabolic pathway, participates in the regulation of the response to antibiotic and oxidative stress that is relevant as a PGP activity [82]. The PAA gene cluster was present in all strains of this study, and several authors have also observed it in different bacteria such as Pseudomonas or Ensifer [83,84,85]. On the other hand, activities of the ACC deaminase enzyme help in decreasing the production of ethylene through the hydrolysis of the ethylene precursor ACC. The lack of ethylene signaling results in plant growth promotion [86], since the plant is not perceiving stress. In our SynCom, the genome of P. ogarae showed the presence of the ACC deaminase gene. It has been shown [87] that ACC deaminase-producing P. fluorescens can alleviate saline stress and increase plant yield.
Regarding the PGP capabilities of the P. fluorescens strain, Choi et al. [88] reported that the biosynthesis of pyrroloquinoline quinone (PQQ) was a key factor involved in growth promotion in tomato and cucumber. Pyrroloquinoline-quinone is not only an antioxidant but also promotes plant growth through phosphate solubilization and the synthesis of antimicrobials, has a role in inducing systemic resistance in plants, and can assist in the metabolism of other bacteria [89,90,91]. In our consortium, genes involved in PQQ biosynthesis were identified in both pseudomonads’ strains, which belong to the P. fluorescens cluster of species. Although plants do not produce PQQ themselves, this compound is found in plant tissues due to the interaction effect between bacteria and plants [92]. The production of polyamines such as spermidine is another possible PGP property, since it participates in the elongation of roots and in the regulation of ethylene levels [93]. In our SynCom, spermidine was produced by Rhizobium, Agromyces, and P. ogarae. Polyamines production has been described in various PGP bacteria including R. leguminosarum, R. meliloti, or R. loti [94] and P. aeruginosa [95]. Furthermore, Altenburger et al. [96] reported that Agromyces strains, including Agromyces mediolanus, although they contain low levels of polyamines, produced spermidine.
Denitrification involves the reduction of nitrate (NO3) to dinitrogen (N2). These reactions are encoded by the napA/narG, nirK/nirS, norB, and nosZ gene clusters [97,98,99]. These four clusters are present in P. ogarae and Rhizhobium sp. strains of the bacterial consortium as has been reported by Garrido-Sanz et al. and Delgado et al. [100,101], respectively. Denitrification allows bacteria to grow in the absence of oxygen, allowing them to be competitive when oxygen is scarce, for instance, in water-saturated soils [102].
The fit insect toxin gene was detected in the genomes of plant-associated pseudomonads and induces apoptosis in insect cells. The fitD gene was found in both pseudomonads present in the SynCom. Garrido-Sanz et al. [100] reported that the FitD toxin was present in strains from P. fluorescens and P. koreensis groups, among others.
Taken together, these results show that the developed SynCom has the potential to promote plant growth through different mechanisms. They also show its functional redundancy and phylogenetic diversity, properties that have been shown to be important for the success of an inoculant based on SynComs [103].
Genomics and metagenomics have emerged as powerful tools for predicting and understanding the traits of PGPR. Plant-growth-promoting rhizobacteria traits involve complex interactions between microorganisms and plants, influenced by environmental factors. Traditional screening methods may not fully capture these complexities. Metagenomic approaches can provide a more comprehensive understanding by identifying functional genes and pathways associated with PGPR activities. Metagenomic analyses have been successfully used to predict PGPR and carbon cycling traits in maize rhizosphere soils [104]. Furthermore, the role of PGPR against the banana pathogenic fungi Fusarium oxysporum sp. cubense was determined by metagenomic analysis [105]. A genomic analysis of four pseudomonads was also used to predict the PGPR traits and other genetic characteristics of bacteria–plant interaction [106].
An important aspect of the use of inoculants is the impact on native microbial populations. We analyzed the effects of introducing SynCom on soil and rhizosphere bacterial communities. Our results suggested that the SynCom exerted only a limited effect on bacterial communities, since the composition of these communities were similar and diversity indexes did not show a significant change, indicating coalescence between the SynCom and the native community [107]. However, the beta-diversity analysis showed that soil and rhizosphere communities were different, and it was also possible to see a difference in rhizosphere community that could be attributed to the introduction of the SynCom. There is some controversy on the effect of introduced bacteria on the native microbiota. Chowdhury et al. [108] showed that the application of a consortium did not elicit a response from a native soil community. However, Amor et al. and Fu et al. [109,110] suggested that the communities might have the capacity to progress toward an alternative stable state. Furthermore, the native communities can also show resilience and return to their initial structure and composition [111,112]. On the contrary, studies carried out by Mawarda et al. [113] showed statistical differences in alpha-diversity measurements after Bacillus invasion.
Another important aspect is the effect of the SynCom on the physiology of the plant. We analyzed this aspect by determining the root and shoot transcriptomes on tomato seedlings. We observed a clear effect of the SynCom in both transcriptomes, with a greater effect on the stem transcriptome than on the root transcriptome. These results indicated that the effect of inoculating the SynCom was mostly systemic. Differences in gene expression showed an effect of the SynCom in the transcription of genes implicated in plant defense indicating that the SynCom elicited a priming effect on the plant and induced a systemic response based in salicylate, ethylene, and jasmonate signaling, as has been reported for other PGPRs [114,115,116].

5. Conclusions

This research presented the potential of a bacterial synthetic community formulated with the following PGPRs: Agromyces mediolanus, Chryseobacterium taeanense, Ensifer spp. Microbacterium oxydans, Pseudomonas monsensis, Pseudomonas ogarae, and Rhizobium spp. Our metagenomic results revealed the biodiversity and metabolic functionality of the SynCom. We showed the presence of different gene clusters involved in plant growth promotion and disease control in all the strains of the bacterial SynCom, suggesting that it has potential for use in plant growth promotion. The transcriptomic analysis further demonstrated that the SynCom induced distinct and systemic responses in tomato plants, with differential gene expression patterns in roots and aerial parts. Upregulation of genes related to ethylene and jasmonate signaling and defense-related proteins highlighted its role in enhancing stress resilience.
Beyond the functional annotation of the SynCom, our results demonstrate that such communities can modulate plant gene expression without disrupting the soil microbial diversity, suggesting a compatible interaction with native microbiota. This highlights the potential of SynComs as sustainable bioinoculants in agriculture, capable of eliciting beneficial plant responses under non-stress conditions. Future research should focus on field-scale validation, evaluation under abiotic and biotic stresses, and fine-tuning of community composition based on functional expression profiles to optimize inoculant performance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15081794/s1, Figure S1: Sample-to-sample distance heatmap based on variance-stabilizing transformation (VST) of normalized gene expression counts. Hierarchical clustering reveals similarities and differences between samples from SynCom-inoculated and control plants across root and aerial part tissues; Table S1: The most upregulated and downregulated genes in root and aerial parts after inoculation with the SynCom.

Author Contributions

Conceptualization, M.M. (Mónica Montoya), M.M. (Marta Martín) and R.R.; methodology, M.M. (Mónica Montoya) and M.R.-N.; software, M.R.-N.; validation, M.M. (Mónica Montoya), M.M. (Marta Martín) and R.R.; formal analysis, M.M. (Mónica Montoya), M.M. (Marta Martín) and R.R.; investigation, D.D.-W., L.C.-R., D.G.-S. and D.V.-A.; resources, M.M. (Marta Martín) and R.R.; data curation, M.M. (Mónica Montoya) and M.R.-N.; writing—original draft preparation, R.R.; writing—review and editing, R.R., M.M. (Marta Martín) and M.M. (Mónica Montoya); visualization, M.M. (Mónica Montoya); supervision, R.R. and M.M. (Marta Martín); project administration, M.M. (Marta Martín) and R.R.; funding acquisition, M.M. (Marta Martín) and R.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the MICIN FEDER/EU Grant PID2021-125070OB-I00 project from the Ministerio de Ciencia, Innovación y Competitividad (MCIN). M. Montoya was the recipient of a Margarita Salas grant from Ministerio de Universidades and Universidad Politécnica de Madrid (RD 289/2021) supported by the European Union Next-Generation EU. D. Vázquez-Arias and L. Carrera-Ruiz are the recipients of the FPI-UAM grant SFPI/2021-00458 and a grant of MCIN PRE2022-102390, respectively.

Data Availability Statement

All sequencing data generated and used in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1198038.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PGPPlant growth promoting
PGPRPlant growth promoting rhizobacteria
SynComSynthetic bacterial communities
SASucrose-Asparagine
PCAPlate Count Agar
YMAYeast Mannitol Agar
MMMinimal Salt Medium
PASPhosphate-buffered mineral medium salts
TYGSType-Strain Genome Server
DEGsDifferentially expressed genes
MAGsMetagenome-assembled genomes
DAPG2,4-diacetylphloroglucinol
PPhosphate
NNitrogen
IAAAuxin indole-3-acetic acid
IAMIndole-3-acetamide
PAADegradation of auxin phenylacetic acid
ACC1-aminocyclopropane-1-carboxylicacid
HCNHydrogen cyanide
PQQPyrroloquinoline quinone

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Figure 1. Relative abundance of bacteria from agricultural soil at the level of genera based on 16s rRNA taxonomic assignment. Genera with a relative abundance lower than 0.05 are grouped under the “other” category.
Figure 1. Relative abundance of bacteria from agricultural soil at the level of genera based on 16s rRNA taxonomic assignment. Genera with a relative abundance lower than 0.05 are grouped under the “other” category.
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Figure 2. (a) Shannon’s alpha diversity index for the different treatments (Soil, Plant, and Plant + SynCom); (b) phylogenetic structure of microbial communities in the soil samples from different treatments. Ordination is based on a principal coordinate analysis.
Figure 2. (a) Shannon’s alpha diversity index for the different treatments (Soil, Plant, and Plant + SynCom); (b) phylogenetic structure of microbial communities in the soil samples from different treatments. Ordination is based on a principal coordinate analysis.
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Figure 3. Transcriptional effect of the application of the SynCom. (a) Number of upregulated and downregulated differentially expressed genes (DEGs) in root and shoot tissues, and Venn diagrams comparing DEGs between organs. Diagrams indicate shared and tissue-specific DEGs based on differential expression between SynCom-inoculated and control plants; (b) heatmap of log10-transformed normalized expression counts for DEGs across all samples, including both control and SynCom-inoculated conditions, revealing root vs. aerial part’s transcriptional responses to bacterial inoculation.
Figure 3. Transcriptional effect of the application of the SynCom. (a) Number of upregulated and downregulated differentially expressed genes (DEGs) in root and shoot tissues, and Venn diagrams comparing DEGs between organs. Diagrams indicate shared and tissue-specific DEGs based on differential expression between SynCom-inoculated and control plants; (b) heatmap of log10-transformed normalized expression counts for DEGs across all samples, including both control and SynCom-inoculated conditions, revealing root vs. aerial part’s transcriptional responses to bacterial inoculation.
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Table 1. Genomic statistics of the seven nearly complete genomes reconstructed from the whole metagenome sequence of the consortium.
Table 1. Genomic statistics of the seven nearly complete genomes reconstructed from the whole metagenome sequence of the consortium.
AssemblyClosest Relative Genome% dDDHLength (bp)ContigsCompleteness
RhizobiumRhizobium metallidurans31.95,818,479599.00
PseudomonasPseudomonas monsensis94.86,443,6791698.40
AgromycesAgromyces mediolanus72.84,695,1142697.11
PseudomonasPseudomonas ogarae87.37,066,2822995.30
ChryseobacteriumChryseobacterium taeanense74.44,333,4311879.94
EnsiferEnsifer morelensis21.08,190,65046072.33
MicrobacteriumMicrobacteium oxydans91.94,028,011399.50
Table 2. Identification of the genes and clusters and plant growth promotion characteristics of the different bacteria that compound the consortium.
Table 2. Identification of the genes and clusters and plant growth promotion characteristics of the different bacteria that compound the consortium.
Possible SpecieGenes/ClustersFunctionPGP Category
Pseudomonas monsensisfecARTransport of iron dicitrate (III)Siderophore
fitDInsect toxinToxin
hasDEFHemophore biosynthesisSiderophore
hcnABCHydrocyanic acid biosynthesisBiocontrol
hcp (T6SS)Type VI secretion systemBiocontrol
iaaHMAuxin biosynthesisPhytohormone modulation
paaFIKYPhenylacetic acid degradationPhytohormone modulation
phnBCDENWXZPhosphate transportNutrient mobilization (P)
phoBDHH2LPQRUPhosphate transportNutrient mobilization (P)
pqqABCDEPyrroloquinoline quinonePlant–bacteria interaction, antioxidant
pstABCSPhosphate transportNutrient mobilization (P)
pvdAELMNOPRSYPyoverdineNutrient mobilization (Fe)
ubiAProduction of 4-hydroxibenzoateAntibiotic
Cluster 1Type NRPS/lokisinAntifungal
Chryseobacterium taeanensepaaABCDEGHIKYZPhenylacetic acid degradationPhytohormone modulation
phnABPPhosphate transportNutrient mobilization (P)
phoABB1DHLPRPhosphate transportNutrient mobilization (P)
ubiAProduction of 4-hydroxibenzoateAntibiotic
Cluster 1Type lantipeptide class IAntimicrobial
Cluster 2Type Aryl polyene, resorcinolBiocontrol
Rhizobium spp. acsAAchromobactin biosynthesisSiderophore
napABCDNitrate reductaseDenitrification/nutrient mobilization(N)
nirBCDFSNitrite reductaseDenitrification/nutrient mobilization(N)
norBCDEGQRNitric oxide reductaseDenitrification
nosDFLYZNitrous oxide reductaseDenitrification/nutrient mobilization(N)
paaABCDEFGHIKXYZPhenylacetic acid degradationPhytohormone modulation
pchREnantio-pyochelin biosynthesisSiderophore
phnABCDEFGHIJKLMNOPWPhosphate transportNutrient mobilization (P)
phoABB1PDHLQRUPhosphate transportNutrient mobilization (P)
pstABCSPhosphate transportNutrient mobilization (P)
speESpermidine biosynthesisPlant–bacteria interaction
ubiAProduction of 4-hydroxibenzoateAntibiotic
Agromyces mediolanushcnABCHydrocyanic acid biosynthesisBiocontrol
paaABCDEFGHIKYZPhenylacetic acid degradationPhytohormone modulation
phnBCDEFPhosphate transportNutrient mobilization (P)
phoABB1HPLH2UPRPhosphate transport Nutrient mobilization (P)
pstABCSPhosphate transportNutrient mobilization (P)
speESpermidine biosynthesisPlant–bacteria interaction
Microbacterium oxydanspaaDFHPhenylacetic acid degradationPhytohormone modulation
phnBCDEOPhosphate transportNutrient mobilization (P)
phoBHLRUPhosphate transportNutrient mobilization (P)
Cluster 1Type NRP-metallophore/parabactinSiderophore
Pseudomonas ogaraeacdSACC deaminasePhytohormone modulation
fecARTransport of iron dicitrate (III)Siderophore
fitDInsect toxinToxin
hasDEFHemophore biosynthesisSiderophore
hcnABCHydrocyanic acid biosynthesisBiocontrol
hcp (T6SS)Type VI secretion systemBiocontrol
narGHIJNitrate reductaseDenitrification/nutrient mobilization (N)
nirBCDFSNitrite reductaseDenitrification/nutrient mobilization (N)
norBCDEGQRNitric oxide reductaseDenitrification
nosDFLYZNitrous oxide reductaseDenitrification/phytohormone modulation
paaFGIKYPhenylacetic acid degradationPhytohormone modulation
pchREnantio-pyochelin biosynthesisSiderophore
phnABCDEXZPhosphate transportNutrient mobilization (P)
phoBDHH2LPQRUPhosphate transportNutrient mobilization (Fe)
pqqBCDEPyrroloquinoline quinonePlant–bacteria interaction, antioxidant
pstABCSPhosphate transportNutrient mobilization (P)
pvdAELMNOPRSYPyoverdineNutrient mobilization (Fe)
speESpermidine biosynthesisPlant–bacteria interaction
ubiAProduction of 4-hydroxibenzoateAntibiotic
Cluster 1Type 2,4-diacetylphloroglucinol (DAPG)Antifungal
Cluster 2Type lantipeptide class IIAntimicrobial
Ensifer spp. paaFGPhenylacetic acid degradationPhytohormone modulation
phnABPhosphate transportNutrient mobilization (P)
phoABQRPhosphate transportNutrient mobilization (P)
ubiAProduction of 4-hydroxibenzoateAntibiotic
Cluster 1Type β-lactoneAntimicrobial
Cluster 2Type β-lactone/mycosubtilinAntifungal
Cluster 3Type ResorcinolBiocontrol
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Montoya, M.; Durán-Wendt, D.; Garrido-Sanz, D.; Carrera-Ruiz, L.; Vázquez-Arias, D.; Redondo-Nieto, M.; Martín, M.; Rivilla, R. Functional Characterization of a Synthetic Bacterial Community (SynCom) and Its Impact on Gene Expression and Growth Promotion in Tomato. Agronomy 2025, 15, 1794. https://doi.org/10.3390/agronomy15081794

AMA Style

Montoya M, Durán-Wendt D, Garrido-Sanz D, Carrera-Ruiz L, Vázquez-Arias D, Redondo-Nieto M, Martín M, Rivilla R. Functional Characterization of a Synthetic Bacterial Community (SynCom) and Its Impact on Gene Expression and Growth Promotion in Tomato. Agronomy. 2025; 15(8):1794. https://doi.org/10.3390/agronomy15081794

Chicago/Turabian Style

Montoya, Mónica, David Durán-Wendt, Daniel Garrido-Sanz, Laura Carrera-Ruiz, David Vázquez-Arias, Miguel Redondo-Nieto, Marta Martín, and Rafael Rivilla. 2025. "Functional Characterization of a Synthetic Bacterial Community (SynCom) and Its Impact on Gene Expression and Growth Promotion in Tomato" Agronomy 15, no. 8: 1794. https://doi.org/10.3390/agronomy15081794

APA Style

Montoya, M., Durán-Wendt, D., Garrido-Sanz, D., Carrera-Ruiz, L., Vázquez-Arias, D., Redondo-Nieto, M., Martín, M., & Rivilla, R. (2025). Functional Characterization of a Synthetic Bacterial Community (SynCom) and Its Impact on Gene Expression and Growth Promotion in Tomato. Agronomy, 15(8), 1794. https://doi.org/10.3390/agronomy15081794

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