Introduction

Soil microorganisms are a vital component of soil and plant health. Naturally, soil contains a vast and diverse community of microorganisms, which governs both direct and indirect roles in the ecosystem to promote plant growth and suppress the proliferation of soil-borne plant pathogens through antagonistic activity [1]. For improving crop productivity, the soil microbiome promotes equilibrium and creates stable agrobiodiversity below ground [2]. Further, to understand the relationship and interaction between the soil microbiome and plants, metagenomic approaches have been used to analyze the diversity of the soil microbiome. These diverse soil microbiomes coexist with plant parasitic nematodes, microbial pathogens, and beneficial microbes. These beneficial microbes serve as a potential source of bio-control agents against plant parasitic nematodes [3]. Plant-parasitic RKNs are a significant concern for agriculture worldwide, infecting many crops [4, 5].

The annual loss due to the infestation of plant-parasitic nematodes is estimated to be ~ 14% of crop production [6], thus contributing ~ 30 to ~ 50% of crop loss globally [7, 8]. As a sedentary endoparasite, the life cycle of an RKN gets completed within plant tissue by developing giant cells in roots, which results in the formation of root galls and thus interferes with the plant’s metabolic processes [9, 10]. At present, managing plant parasitic nematodes in an eco-friendly manner remains challenging. Due to the significant economic impact of parasitic nematodes, various nematode-management techniques, including chemical nematicides, have been developed and used commercially to mitigate the infection of plants by nematodes [11].

Using bioagents compatible with the plant-beneficial microbes in the rhizosphere can positively impact the environment and become an effective, sustainable strategy for managing plant parasitic nematodes (PPN). Some bacterial biocontrol agents can hyperparasitize various stages of RKNs and thus reduce the hatching frequency of nematode eggs. In contrast, other biocontrol agents can induce the synthesis of plant-protective secondary metabolites, plant systemic resistance, and the production of phytohormones, antibiotics, and volatile organic compounds [12, 13]. The bacterial antagonists also reduce the invasion of PPN into plant roots [14], produce endospores, bind to nematode cuticles, induce the plant’s defense mechanism [15], and suppress the nematode infestation in tomatoes [16]. Further, among the fungal antagonists, Trichoderma species are widely considered exploitable fungal biocontrol agents to curb PPN [17]. Trichoderma’s mechanism of action includes the production of defensive metabolites, enzymes, and antioxidant compounds that potentially provide physical and chemical protection against RKN [18, 19]. RKN and soil biota interactions might be parasitic, symbiotic, communalistic, or hostile. However, understanding the diversity of bacterial microbiomes will aid in acquiring knowledge regarding the interaction between soil microbiomes and the homeostasis of soil ecosystems for preventing pest attacks and improving crop growth [20]. Further, nematicidal activity and the efficacy of applied biocontrol agents and soil-residing biota could be influenced by microbial interactions in the rhizosphere ecosystem with a wide range of activities, including antagonisms to syntrophic and mutualistic interactions [8].

The rhizospheric microbial diversity strengthens the resistance level of plant systems and disrupts the activity of soil-borne pathogens, including plant-parasitic nematodes [21]. Thus, it has been suggested that the best way to manage and prevent RKN infestation is to modify the composition of the rhizosphere microbial population and thereby retain the soil health [22]. Realizing the significance of microbial communities, their diversity in the rhizosphere was investigated using culture-dependent and -independent strategies. However, both techniques have certain limitations. To overcome these limitations, high-throughput sequencing (HTS) and shotgun metagenomic sequencing were carried out to facilitate the knowledge of the diversity of unculturable microbial communities to elaborate their functional network by monitoring the V1-V9 hypervariable region in bacteria [23]. Many researchers have also elaborated on the diversity of bacterial microbiomes [5, 24,25,26,27] residing in healthy and diseased tomato plants under field conditions. However, investigations of the interactions of rhizosphere microbial diversity and their role in curbing PPN are only in their infancy.

On the other hand, nematode microbiome studies revealed the diverse nature of various taxa involved in suppressing PPN [10, 28]. The knowledge of RKN-infected and healthy tomato roots explains the variations in the diversity of microbial communities associated with parasitism and antagonistic activity [29]. Although the prior research findings have demonstrated the role of microbes in inhibiting plant-parasitic nematodes, attempts made to identify the dominant group of soil bacteria associated with nematode prevalence in the field remain to be explored. This situation has encouraged us to investigate the current knowledge on the linkages between bacterial rhizobiome related to plant parasitic nematodes, biocontrol agents, and nematicides used to manage nematodes. The underlying idea behind this study has been that the individual or combined applications of antinemic biocontrol agents with chemical nematicides to suppress RKN might affect bacterial rhizobiome diversity or promote changes in root biology with effects on specialized rhizosphere organisms. Before this study, there was no evidence regarding the diversity analysis of microbial communities following the combined application of two biocontrol agents to tomato plants at the field level to manage RKN infestation. The present study aimed to differentiate the abundance and diversity of various bacterial communities distributed in biocontrol agent-treated rhizosphere soil and RKN-infected rhizosphere soil at various taxonomical levels thorough amplicon sequencing. Therefore, we investigated the diversity of the bacterial microbiome in tomato rhizosphere soil through the combined application of B. velezensis VB7 and T. koningiopsis TK against RKN infestation to assess the changes in bacterial abundance and diversity in treated and untreated rhizosphere soils using the next generation sequencing (NGS)-based amplicon sequencing by measuring the abundance of the operational taxonomic units (OTUs) in the rhizosphere.

Materials and Methods

Preparation of Nematode Inoculum

Eggs collected from severely infected galled tomato roots were used as the nematode inoculum. The eggs from the gelatinous matrix were separated by chopping roots into 1–2 cm pieces. Chopped roots were placed in a 500-ml plastic container containing 1.5% chlorine solution and shaken forcefully for 3 min. The suspension was then rinsed six times with running tap water through a 250-µm aperture mesh sieve, with the eggs being collected on a sieve with a 20-µm aperture. After 4 days of incubation at 28 ± 2 °C, the hatched second-stage juveniles (J2) were collected from the egg suspension using a modified Baermann dish and utilized for subsequent experiments.

Testing the Nematicidal Activity of Bacterial and Fungal Antagonists Against RKN

The bacterial antagonist B. velezensis VB7 (MW301630) and the fungal antagonist T. koningiopsis TK (KX 555650) were obtained from the Department of Plant Pathology, Tamil Nadu Agricultural University (TNAU), Coimbatore, India. The effect of biocontrol strains such as B. velezensis VB7 and T. koningiopsis TK with nematicide activity on the second-stage juveniles (J2) of M. incognita was evaluated by the nematode mortality rate and the hatching ability of egg mass. The bacterial culture (B. velezensis VB7) was inoculated into LB broth and maintained in an orbital shaker at 150 rpm, at room temperature (28 ± 2 °C), for 48 h to ensure uniform bacterial growth. The fungal discs (T. koningiopsis) were inoculated into Potato Dextrose Broth (PDB) at 28 °C and maintained for 5 days. Supernatants were collected by centrifugation of culture suspensions at 10,000 rpm for 10 min at 5 °C, which contained no viable cells. The supernatants were collected, and 3 ml of cell-free culture filtrates of biocontrol agent was poured separately into a 6-cm (diameter) Petri dish. 2-egg masses of M. incognita were added using a camel hair brush for each replication and incubated at 28 ± 2 °C. The hatching percentage was observed at 24 h, 48 h, 72 h, and 96 h after incubation. Sterile water was used as a control. Similarly, for the mortality assay, hatched second-stage juveniles (J2) were adjusted to the concentration of 50 juveniles ml−1. Two milliliters of nematode suspension (100 juveniles) was inoculated into 6-cm Petri plates containing 3-ml cell-free culture filtrates of the biocontrol agents. Three replications were maintained and incubated at room temperature (28 ± 2 °C). After 24 h, 48 h, 72 h, and 96 h, the number of surviving and dead individuals was recorded using a 1-ml Hawksley counting slide. The formula calculated the percentage mortality:

$$\text{Mortality }\left({\%}\right)=\frac{{C}_{1}-{C}_{2}}{{C}_{1}} \times 100\dots$$

where C1 is the number of live juveniles released, and C2 is the number of live juveniles counted. Analysis of variance was used to conduct a statistical analysis of the data collected from the in vitro experiments. For analysis of variance (ANOVA), Tukey’s test was performed using SPSS 20.0 (IBM, SPSS statistics 20) with a significance threshold of 0.05.

Collection of Samples

Rhizosphere soils were collected by a random sampling technique at 35 days after combined applications of 1% Trichoderma koningiopsis TK formulation (3 × 108 cfu/g) [30] and a liquid formulation of Bacillus velezensis VB7 (5 × 108 cfu/ml) as per the protocol of Vinodkumar et al. [31] from six different treatments including T1 - B. velezensis VB7 alone; T2 - B. velezensis VB7 + RKN; T3 - T. koningiopsis alone; T4 - T. koningiopsis + RKN; T5 - B. velezensis VB7 + T. koningiopsis TK; T6 - untreated control in RKN-infected tomato fields at Thondamuthur in Coimbatore province, Tamil Nadu, India (GPS coordinates: 10.5484° N 76.2857° E), by maintaining three biological replicates for each sample. Each replicate was laid over an area of 40 m2. Collected samples were stored in sterile polypropylene bags, immediately placed on an ice box, transported to the laboratory, and stored at − 80 °C until processing.

PCR Amplification of 16S rRNA and Purification

Metagenomic DNA was separately extracted from the collected rhizosphere soils for all treatments, including T1 - B. velezensis VB7 alone; T2 - B. velezensis VB7 + RKN; T3 - T. koningiopsis alone; T4 - T. koningiopsis + RKN; T5 - B. velezensis VB7 + T. koningiopsis TK; T6 - untreated control in RKN-infected tomato fields, using Power Soil DNA Isolation Kit (QIAGEN, India) 30 days after planting. Quantitative and qualitative analysis of DNA was performed using the nanodrop method, followed by 1% agarose gel electrophoresis using a TAE electrode buffer. A total of 50 ng DNA from each sample was subjected to 16S rRNA PCR gene amplification using V1-V9 region-specific primers of 27F 5′AGAGTTTGATCMTGGCTCAG3′ and 1492R 5′TACGGYTACCTTGTTACGACTT3′. The amplicons obtained from the samples were confirmed by agarose (1%) with EtBr gel electrophoresis. The PCR products were purified using 1.6X Ampure XP beads (Beckmann Coulter, USA).

Library Preparation and Sequencing of DNA Product

A total of ~ 50 ng from each amplicon DNA was end-repaired (NEBnext ultra II end repair kit; New England Biolabs, MA, USA) and cleaned with 1 × AmPure beads. Barcoding adapter ligation (BCA) was performed with NEB blunt/TA ligase and cleaned with 1 × AmPure beads. Qubit quantified adapter-ligated DNA samples were barcoded using PCR reactions, pooled at equimolar concentration, and end-repair was performed using NEBnext ultra II end repair kit (New England Biolabs, MA, USA) and cleaned. Adapter ligation (AMX) was performed for 15 min using NEB blunt/TA ligase (New England Biolabs, MA, USA). Library mix was cleaned using Ampure beads and then eluted in 15 μl of elution buffer (GENE, Bengaluru, India). Sequencing was performed for prokaryotic and eukaryotic organisms through the Oxford Nanopore sequencing method using MinION flow cell R9.4 (FLO-MIN106). Nanopore raw reads (“fast5” format) were base-called (“fastq5” format) and de-multiplexed using Guppy1 v2.3.4.

Processing of Sequencing Data and Taxonomic Profiling

Sequencing data were processed with the Guppy v2.3.4 tool kit for base calling the sequencing data to generate a pass read. The adapter and barcode sequences were trimmed using the Porechop tool. The reads were filtered by size using SeqKit software ver. 0.10.0, and the average Phred quality score was assessed using the SILVA database. A comprehensive taxonomic microbial community analysis was performed on each set’s processed reads. The obtained rRNA reads were imported into Qiime2 in the Single End Fastq Manifest Phred33 input format for diversity analysis. The readings were de-duplicated and then grouped into operational taxonomic units (OTUs) based on their 97% similarity [32]. BLAST with QIIME (categorize-consensus-search) was used to classify representative sequences using percent identity 0.97 against the SILVA full-length 16S database. The long-read amplicon data was sequenced using Nanopore MinION and validated against SILVA databases to determine their proper classification. The relative microbial abundances were determined to categorize the microbial community of each sample according to their taxonomic profile (Kingdom, Phylum, Class, Order, Family, Genus, Species) in a stacked bar chart. Singletons and sequences classified as mitochondria, chloroplast, Archaea, and unassigned sequences were removed. Only the top 20 bacterial OTUs were used for the analysis.

Diversity Index Analysis

The microbial diversity analysis was estimated by calculating the alpha and beta diversity indexes using the obtained OTU cluster. The alpha diversity was carried out using the evenness vector, Jaccard observed features vector [33], and Shannon vector [34] algorithms to determine the diversity and richness of the microbial community within a sample. In contrast, beta diversity represents the diversity between different samples using the Bray–Curtis dissimilarity statistic [10, 35]. Samples were compared with a comprehensive set of multivariate statistical tools, including PERMANOVA and visualization tools. According to Zheng and collaborators, OTU comparisons were performed using the Venn diagram package [25]. All of these indices in our samples were calculated using QIIME (version 1.7.0, http://qiime.org/1.7.0/). Principal coordinate (PCoA) plots were generated to determine the community structure using QIIME1 version 1.9.1 [36]. Statistical analyses were conducted using R statistical software with diverse sub-programs [37]. All significance tests were two-sided; P values < 0.05 were considered statistically significant. Gephi 0.10.1 was used to construct the co-occurrences co-efficient network using each sample’s mean average of OTUs.

The Taxonomic Abundance of the Microbial Population Through Cluster Heatmap

According to the abundance of information on microbial communities at the taxonomic level, the heatmap was constructed by clustering similar communities of each sample to determine the frequency of microbial communities.

Venn Diagrams

Venn diagrams were constructed to determine the relationship between bacterial communities in treated and untreated soil. They were compared at the individual as well as combined applications of bioagents and nematicide at the genus and species level by using the Muthor program and then submitted to VENNY (http://bioinfogp.cnb.csic.es/tools/venny/index.html) to show the shared and unique OTUs [29].

Results

Efficacy of Culture Filtrate of B. velezensis VB7 and T. koningiopsis TK Against Egg Hatching and Juveniles’ Mortality of Root-Knot Nematode M. incognita Under In Vitro Conditions

The cultural filtrates of B. velezensis VB7 and T. koningiopsis TK and water (control, T6) were screened for their efficacy on egg hatching and juvenile mortality of M. incognita under in vitro conditions. B. velezensis VB7 combined with T. koningiopsis TK effectively inhibited 94.50% of juveniles’ mortality of M. incognita after 96 h exposure. B. velezensis VB7 alone accounted for 85.70% mortality, and the cultural filtrates of T. koningiopsis TK alone inhibited 77.45% mortality, whereas lower juveniles’ mortality of 4.6% was observed in control (T6 – Sterile water), respectively (Fig. 1A). The egg hatching rate of M. incognita, treated with B. velezensis VB7 + T. koningiopsis TK, was 0% of egg hatching with 100% reduction in egg hatching after a 96 h exposure, a noticeable drop (P < 0.05) compared with the control (T6). At the same time, the cultural filtrates of B. velezensis VB7 (T1) and T. koningiopsis TK (T3) alone showed 94.45% inhibition of egg hatching and 87.15%, respectively (Fig. 1B). The efficacy of the nematicide activity was correlated with the exposure time.

Fig. 1
figure 1

Efficacy of culture filtrate of B. velezensis and T. koningiopsis TK against egg hatching and juveniles’ mortality of root-knot nematode (RKN) M. incognita at different intervals, under in vitro conditions. A Percentage of juvenile mortality. B Percentage of inhibition of egg hatching. Means indicated by the same lower-case letters show significant and insignificant differences according to Tukey’s test at P ≤ 0.05. Each value is the average of three replicates, and the error bar indicates ± standard deviation (SD) (VB7 + RKN = B. velezensis VB7 + root-knot nematode, VB7 = B. velezensis VB7 alone, TK + RKN = T. koningiopsis TK + root-knot nematode, TK = T. koningiopsis TK alone, VB7 + TK + RKN = B. velezensis VB7 + T. koningiopsis TK + root-knot nematode; RKN, root-knot nematode (M. incognita))

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OTU Identification and Taxonomic Annotation for Bacteria

Approximately 1,210,401 raw reads were generated from Illumina MiSeq sequencing of the six samples, with an average of 201,734 reads per sample. After quality control, 1,025,357 clean reads were obtained from the raw reads for six samples, with an average of 170,893 reads per sample. The quality-filtered reads were further size-filtered to obtain the classified OTU to retain 1200–1950 bp sequences for the V1-V9 region. In total, 16,892 sizes of filtered reads ranging from 865 to 3517 were identified after processing of QC filtered reads. A total of 3788 OTUs were identified as classified reads in all six samples. Among these, 746 OTUs (19.70%) were found in T1, 561 OTUs (14.80%) were present in T2, 291 OTUs (7.70%) were analyzed in T3, 737 OTUs (19.45%) were recorded in T4, 737 OTUs (19.45%) were observed in T5, and 716 OTUs (18.90%) were identified in T6, which were used for further analysis.

Analysis of the Bacterial Community’s Composition

Relative Abundance

According to the microbial classification, 21 phyla, 48 classes, 105 orders, 135 families, 205 genera, and 252 species with different bacterial communities were identified from the tomato rhizosphere soil, irrespective of other treatments. Taxonomic annotation of each sample was grouped at each level to determine the proportion of relative abundance of each sample at different taxonomic classification levels. The abundance of each taxa level for each sample was represented in the stacked bar chart (Fig. 2A–E).

Fig. 2
figure 2

Relative abundances of rhizosphere bacteria communities distributed in treated and untreated rhizospheric soil. A Relative abundances of different bacterial phyla distributed in different treatments. B Relative abundances of different bacterial classes distributed in different treatments. C Relative abundances of various bacterial orders distributed in different treatments. D Relative abundances of various bacterial families distributed at different treatments. E Relative abundances of various bacterial genus distributed in different treatments. F Relative abundances of various bacterial species distributed in different treatments (VB7 + RKN = B. velezensis VB7 + root-knot nematode, VB7 = B. velezensis VB7 alone, TK + RKN = T. koningiopsis TK + root-knot nematode, TK = T. koningiopsis TK alone, VB7 + TK + RKN = B. velezensis VB7 + T. koningiopsis TK + root-knot nematode; RKN, root-knot nematode (M. incognita))

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Comparative analysis of all the rhizosphere soil samples revealed the presence of major bacterial Phyla such as Proteobacteria, Firmicutes, Actinobacteriota, Acidobacteriota, Chloroflexi, Cyanobacteria, Bacteroidota, Myxococcota, Gemmatimonadota, Nitrospirota, Patescibacteria, Planctomycetota, Verrucomicrobiota, Spirochaetota, Campilobacterota, Desulfobacterota, and Methylomirabilota. Among the different phyla, Proteobacteria (42.16%), Firmicutes (19.57%), and Actinobacteria (17.69%) were the three predominant phyla in all six samples, including the RKN control (Fig. S1). The relative abundance of Proteobacteria was always much higher than Firmicutes and Actinobacteria; for example, Proteobacteria abundance was 52.59% in the tomato rhizosphere soils drenched with B. velezensis VB7 + Trichoderma koningiopsis TK. It increased to 40.71% in the tomato rhizosphere drenched with B. velezensis VB7 + RKN soil and 30.71% in B. velezensis VB7 alone inoculated soil. Similarly, the relative abundance of Proteobacteria was 30.67% in T. koningiopsis TK + RKN sample, while 25.03% of abundance was found in T. koningiopsis TK alone. Interestingly, the relative abundance of Proteobacteria in the rhizosphere soil of RKN-infected samples was 23.26%. The relative abundance of a second dominant phylum, Firmicutes, increased to 20.64% in tomato rhizosphere soils drenched with B. velezensis VB7 + T. koningiopsis TK. At the same time, B. velezensis VB7 + RKN soil had an abundance of 18.72% compared to 15.17% in B. velezensis VB7 alone drenched soil. However, RKN-infected soils had a lower abundance of Firmicutes, i.e., 15.38%. Further, the abundance of Firmicutes was comparatively higher in both T. koningiopsis TK + RKN (11.61%) and T. koningiopsis TK alone drenched soils (9.72%), compared to the RKN alone infested soils. Thus, the application of B. velezensis VB7 and T. koningiopsis TK significantly promoted the proliferation of Proteobacteria and Firmicutes (Fig. 2A). The relative abundance of Actinobacteria ranged from 5.80 to 13.00%, irrespective of all the treatments. Interestingly, the relative abundance of Actinobacteria was more abundant in rhizosphere soils drenched with B. velezensis VB7 + T. koningiopsis TK soil (13.69%). In contrast, B. velezensis VB7 + RKN soil treated soil had 11.00%, while soil drenched with B. velezensis VB7 alone had 9.47%, compared to the RKN-infested rhizosphere soil samples (2.61%).

Analysis of top 20 bacterial classes present in different rhizosphere soil samples revealed the existence of γ proteobacteria, Bacilli, α proteobacteria, Actinobacteria, Thermoleophilia, Bacteroidia, Cyanobacteria, Acidimicrobia, Anaerolineae, Vicinamibacteria, Rubrobacteria, Campylobacteria, Planctomycetes, Gemmatimonadetes, Saccharimonadia, Myxococcia, Blastocatellia, Verrucomicrobiae, Myxococcia, and Nitrospiria (Fig. S2 ). At the class level, γ proteobacteria (24.80%), α proteobacteria (21.61%), Bacilli (19.86%), and Actinobacteria (12.14%) were dominant in both treated and untreated rhizosphere samples. Examination of the relative abundance of γ proteobacteria from various rhizosphere soils indicated that B. velezensis VB7 combined with T. koningiopsis TK-treated soil had 30.48% of γ proteobacteria population. However, in B. velezensis VB7 + RKN soil, 26.62% γ proteobacteria abundance was found, whereas 24.48% was observed in B. velezensis VB7 alone treated soil. Similarly, T. koningiopsis TK-drenched soil had 18.5%. However, the population of γ proteobacteria was increased to 21.58% T. koningiopsis TK + RKN treatments. The soil drenched with B. velezensis VB7 + T. koningiopsis TK + RKN induced the proliferation of γ-Proteobacteria rather than the absence of RKN infection. However, 17.98% of γ proteobacteria abundance was found in RKN-infested soil, which was lower than in treated soil. Similarly, the relative abundance of α-proteobacteria in the rhizosphere soil treated with B. velezensis VB7 in the presence of RKN was 15.96%, compared to 19.06% in soil treated with B. velezensis VB7 alone. Moreover, the abundance increased to 24.97% in B. velezensis VB7 + T. koningiopsis TK + RKN soil. However, the T. koningiopsis TK + RKN treatment had 14.91%, whereas T. koningiopsis TK without RKN soil had 11.45% of the α-proteobacteria population. Thus, combining B. velezensis VB7 + T. koningiopsis TK + RKN treatment increased the α proteobacteria population density due to synergistic interaction between antagonistic biocontrol agents and the rhizosphere bacteriome. On the contrary, the relative abundance of α proteobacteria was comparatively lower in RKN infestation soil (8.45%) than in biocontrol agents treated soil. The maximum relative abundance of Bacilli was present in B. velezensis VB7 + T. koningiopsis TK + RKN treatment with the range of 24.35%. The lower population of was found in B. velezensis VB7 alone (19.5%) as compared to 21.52% in B. velezensis VB7 with RKN-infested soil. However, with the plant’s defense, the abundance of Bacilli was 14.50% in T. koningiopsis TK + RKN treated soil and 12.45% in T. koningiopsis TK alone drenched soil (Fig. 2B). The abundance of Bacilli was comparatively very low in RKN-infested soil (10.14%). The abundant population of Cyanobacteria was found in T. koningiopsis TK drenched rhizosphere soil (6.06%), followed by 4.65% in T. koningiopsis TK + RKN soil, whereas low in RKN soil 2.2%. The B. velezensis VB7 + T. koningiopsis TK showed a significant increase in the populations of γ proteobacteria and bacilli. However, the soil drenching of B. velezensis VB7 and T. koningiopsis TK with or without RKN association positively influenced the abundance of the Bacilli population. Similarly, the combined application of B. velezensis VB7 + T. koningiopsis TK with RKN and B. velezensis VB7 + RKN treatment positively impacted the abundance of α proteobacteria population. However, applying T. koningiopsis TK increased the presence of a Cyanobacteria population.

The bacterial orders include Bacillales, Sphingomonadales, Burkholderiales, Micrococcales, Rhizobiales, Xanthomonadales, Oceanospirillales, Pseudomonadales, Streptomycetales, Tistrellales, Paenibacillales, Gaiellales, Propionibacteriales, Campylobacterales, Anaerolineales, Caulobacterales, Cyanobacteriales, Vicinamibacterales, Cyanobacteriales, Cellvibrionales, Solirubrobacterales, and Campylobacterales were ranked higher abundance in the rhizosphere region of all different soil samples (Fig. S3). Among the top 20 orders, Bacillales (17.47%), Sphingomonadales (7.60%), Burkholderiales (7.60%), Micrococcales (6.42%), and Rhizobiales (5.17%) were the most prominent ones in rhizosphere soil abundance. The soil treated with B. velezensis VB7 + T. koningiopsis TK in RKN infestation had the most significant influence on the abundance of the Bacillales population, accounting for 29.33%, followed by 25.93% in B. velezensis VB7 + RKN soil, and 23.34% in B. velezensis VB7 only applied soil. On the other hand, T. koningiopsis TK + RKN had a relative abundance of 21.34% of Bacillales compared to 18.28% in T. koningiopsis TK alone drenched soil. However, a lower population of Bacillales was observed in RKN-infested soil at 16.43%, which was lower than the biocontrol agents’ drenched soil. A comparison of the relative abundance of Sphingomonadales order between different treatments indicated an abundance value of 15.43% in the soils applied with B. velezensis VB7 + T. koningiopsis TK in RKN soil. However, 9.59% of Sphingomonadales populations were found in B. velezensis VB7 + RKN soil and 6.64% in B. velezensis VB7 applied to the soil. Moreover, it was only 7.66% in RKN-infested soils. On the other hand, T. koningiopsis TK alone and T. koningiopsis TK + RKN treatment had abundances of 13.71% and 7.14%, respectively. A lower abundance of 5.82% was observed in RKN-infected soil alone. Similarly, a significant percentage of the rhizosphere population was found in B. velezensis VB7 and T. koningiopsis TK + RKN treatment, accounting for 12.46%, 8.49% in B. velezensis VB7 with RKN soil, and 4.51% in RKN infested soils, respectively. The maximum population of Rhizobiales of 10.54% was found in B. velezensis VB7 + RKN. However, the population was reduced in soil drenched with T. koningiopsis TK alone (5.20%) compared to T. koningiopsis TK + RKN (7.43%). Combined application of B. velezensis VB7 + T. koningiopsis TK might have contributed towards the proliferation of Rhizobiales populations in rhizosphere soil rather than a single application of the biocontrol agents (Fig. 2C).

Distribution of top 20 bacterial families in different soil samples includes Bacillaceae, Sphingomonadaceae, Xanthomonadaceae, Comamonadaceae, Pseudomonadaceae, Marinomonadaceae, Rhizobiaceae, Micrococcaceae, Microbacteriaceae, Geminicoccaceae Alcaligenaceae, Rhodobacteraceae, Beijerinckiaceae, Nocardioidaceae, Vicinamibacteraceae, Caulobacteraceae, Arcobacteraceae, Streptomycetaceae, Gaiellaceae, Gemmatimonadaceae, and Rhodanobacteraceae (Fig. S4). Among those families, Bacillaceae (17.08%), Sphingomonadaceae (8.25%), Xanthomonadaceae (4.10%), and Rhizobiaceae (4.30%) populations were dominant in the rhizosphere region. The B. velezensis VB7 + RKN soil constituted 33.69% of the Bacillaceae population compared to the 31.05% in B. velezensis VB7 treatment alone. Similarly, the soil drenched with T. koningiopsis TK in RKN constituted 27.49% of Bacillaceae, while 25.32% in T. koningiopsis TK alone applied to the soil. Moreover, a significant proportion of Bacillaceae was found with a B. velezensis VB7 + T. koningiopsis TK + RKN treatment. A lower population was noticed in RKN-infected tomato rhizosphere (21.45%). On the contrary, the relative abundance of Sphingomonadaceae was detected in B. velezensis VB7 + RKN with the range of 19.67% compared with 17.93% in B. velezensis VB7 alone drenched soil. In addition, the tomato rhizosphere soil drenched with B. velezensis VB7 + T. koningiopsis TK + RKN had a maximum relative abundance of Sphingomonodaceae of up to 23.93%. Though the abundance level was lower than the soils drenched with T. koningiopsis TK (i.e., 11.34%), it was higher than T. koningiopsis TK + RKN treatment (15.43%). However, the abundance of the Sphingomonodaceae population was low (9.87%) in the RKN-infected soil. Hence, the RKN-infected tomato rhizosphere soil drenched with B. velezensis VB7 + T. koningiopsis TK was comparatively higher in the population than when applying individual biocontrol agents. Similarly, the relative abundance of Rhizobiaceae was 7.70% in the B. velezensis VB7 + RKN compared to 6.05% in B. velezensis VB7 alone. However, in RKN soils, it was only 3.07%. But, the population of Rhizobiaceae in T. koningiopsis TK alone was 4.34% and 5.98% in T. koningiopsis TK + RKN soil. The highest abundance of the Rhizobiaceae population, i.e., 9.45%, was found in T. koningiopsis TK + B. velezensis VB7 + RKN compared to 3.90% in untreated control soil (RKN). However, the Rhizobiaceae abundance is also high in the soils amended with bacterial and fungal antagonists in RKN soil. The relative abundance of the Pseudomonaceae was 12.14% and 10.89% in B. velezensis VB7 + RKN soils and B. velezensis VB7 TK alone treated soil, respectively. However, the abundance of Pseudomonaceae was 14.81% in B. velezensis VB7 + T. koningiopsis TK applied soils, as against 3.6% in RKN soils (Fig. 2D). Thus, in the presence of RKN, the population dynamics of different bacterial families were increased with the single or combined application of B. velezensis VB7 and T. koningiopsis TK.

The top 20 bacterial genera prevalent in rhizosphere soil were identified as Bacillus, Sphingomonas, Pseudomonas, Marinomonas, Microvirga, Cellvibrio, Microbacterium, Vicinamibacteraceae, Pseudoxanthomonas, Nocardioides, Gaiella, Achromobacter, Arthrobacter, Marinomonas, Ochrobactrum, Stenotrophomonas, Achromobacter, and Mitsuaria (Fig. S5). Three bacterial genera, including Bacillus (16.07%), Sphingomonas (5.56%), and Pseudomonas (3.6%), were observed in the highest numbers in all soil samples. The soil with B. velezensis VB7 + RKN contained 33.24% Bacillus genera compared to 29.24% in soils with B. velezensis VB7 alone. In addition, a Bacillus abundance of 39.70% was observed in soil drenched with B. velezensis VB7 + T. koningiopsis TK + RKN. T. koningiopsis TK + RKN treated soil had 27.24% Bacillus abundance against 25.25% in T. koningiopsis alone. At the same time, RKN-infected soil had a lower proportion of the Bacillus population, 35.87%. The abundance of Sphingomonas was lesser in T. koningiopsis TK + RKN and T. koningiopsis TK soil, i.e., 8.75% and 5.34% compared to B. velezensis VB7 + RKN and B. velezensis VB7 alone which was 11.75% and 9.45%, respectively. The maximum proportion of Sphingomonas was highly increased in B. velezensis VB7 + T. koningiopsis TK + RKN sample. But, the RKN-infected soil had a comparatively lower abundance of Sphingomonas (3.85%). The abundance of Pseudomonas in B. velezensis VB7 + RKN soil was 10.04% compared to 8.13% in B. velezensis VB7 alone. The abundance of Pseudomonas was 12.45% in T. koningiopsis TK + B. velezensis VB7 applied soils with the presence of RKN. At the same time, the T. koningiopsis TK + RKN soil had 8.75% Pseudomonas compared to 5.52% in T. koningiopsis TK alone and 2.54% in RKN-infected soil. The Microvirga genus had a comparatively higher percentage of B. velezensis VB7 + T. koningiopsis TK + RKN treatment, which was 9.24%. However, the abundance was decreased in T. koningiopsis TK + RKN soil to 1.20%. However, it was increased in B. velezensis VB7 alone applied to soil to 5.45%, followed by RKN-infected soil, which was 1.20%. The abundance was lower in both B. velezensis VB7 + RKN and T. koningiopsis TK + RKN soil (Fig. 2E).

The species diversity observed in various rhizosphere soils comprised of Bacillus sp., B. megaterium, Pseudomonas borbori, Marinomonasm sp., Sphingomonas sp., Mitsuaria chitosanitabida, Gaiella sp., Microbacterium sp., Ochrobactrum intermedium, Streptomyces sp., Sphingomonas sp., Pseudomonas sp., Solirubrobacter sp., Limibaculum sp., Pseudoxanthomonas mexicana, Cellvibrio sp., Achromobacter sp., and Pseudoxanthomonas mexicana (Fig. S6). Sphingomonas sp. and Gaiella sp. are considered uncultured bacterial species. The culturable Bacillus spp. was abundant in B. velezensis VB7 + T. koningiopsis TK + RKN soil (27.56% abundance), followed by 24.67% abundance of B. velezensis VB7 + RKN and 21.06% abundance in B. velezensis VB7. However, T. koningiopsis TK, with the presence of RKN, constituted the Bacillus spp., with an abundance of 17.56% and 15.45% in T. koningiopsis TK alone. The RKN-infected soil had a lower abundance of 12.90% Bacillus sp. when compared to treated soil samples. The Bacillus megaterium in B. velezensis VB7 + T. koningiopsis TK drenched in RKN soil had a relative abundance of 12.45%. It was 10.34% in B. velezensis VB7 + RKN and 7.29% in B. velezensis VB7 alone applied soil. The lowest relative abundance of 4.78% was found in RKN-infested soil. Pseudomonas barbori was abundant in T. koningiopsis TK + B. velezensis VB7 + RKN soil at 9.19%. The B. velezensis VB7 + RKN soil had an abundance of Pseudomonas barbori of 8.01% against 5.21% in T. koningiopsis TK + RKN soil. The RKN soil had a lower abundance of only 1.75%, whereas B. velezensis VB7 alone comprised 6.5% against 3.4% in T. koningiopsis TK alone (Fig. 2F).

The Taxonomic Abundance of Bacterial Microbiome Population

Based on the information regarding microbial communities at the taxonomic level, a heatmap was constructed by clustering similar communities of each sample to determine the frequency of microbial communities. The phylum of Proteobacteria, Firmicutes, and Actinobacteria showed higher frequencies in all the samples, while Acidobacteriota, Cyanobacteria, and Chloroflexi had a lower frequency, including in the control (Fig. 3).

Fig. 3
figure 3

Cluster heatmap analysis of rhizosphere bacterial communities concerning different treatments at the phylum level (VB7 + RKN = B. velezensis VB7 + root-knot nematode, VB7 = B. velezensis VB7 alone, TK + RKN = T. koningiopsis TK + root-knot nematode, TK = T. koningiopsis TK alone, VB7 + TK + RKN = B. velezensis VB7 + T. koningiopsis TK + root-knot nematode; RKN, root-knot nematode (M. incognita))

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Bacillus and Sphingomonas had a relatively higher frequency than similar microbial communities in treated and untreated soil samples (Fig. S7). The higher prevalence of Pseudomonas was observed in the combined application of B. velezensis VB7 + T. koningiopsis TK + RKN treatment, whereas lower frequency was found in T. koningiopsis TK alone drenched soil. Compared to all other treatments, Microbacterium, Ochrobacterium, Achromobacter, and Stenotrophomonas were observed with a maximum frequency in B. velezensis VB7 + RKN soil. This bacterial genus had A comparatively lower frequency in untreated RKN-associated soil samples.

Comparison of OTUs at Different Treatments Using Venn Diagram

A total of 3788 OTUs were obtained from the high-throughput sequencing. The OTUs distribution in the combined applications, i.e., B. velezensis VB7 + RKN, T. koningiopsis TK + RKN, B. velezensis VB7 + T. koningiopsis TK + RKN, and control (untreated) soil samples were compared at genus and species levels to determine similar organisms shared between treatments. At the genus level, a total of 59 OTUs were shared by all four soil treatments. Among them, 29 OTUs were found in untreated RKN control, 37 OTUs were shared in T. koningiopsis TK + RKN, B. velezensis VB7 + RKN had 44 OTUs, and B. velezensis VB7 + T. koningiopsis TK + RKN soil constituted of 43 OTUs, respectively (Fig. 4A). Fifteen OTUs were common for all four of the above-mentioned soil treatments at the species level, with unique OTUs of 27, 33, 31, and 64, respectively (Fig. 4B).

Fig. 4
figure 4

A Comparison of bacterial OTUs distributed in combined application (bioagents with fluazaindolozine) treatments at the genus level. B Comparison of bacterial OTUs distributed in the individual application (bioagents alone and biocontrol alone) treatments at genus level. C Comparison of bacterial OTUs distributed in the combined application (bioagents with fluazaindolozine) treatments at the species level. D Comparison of bacterial OTUs distributed in the individual application (bioagents alone and biocontrol alone) treatments at species level (VB7 + RKN = B. velezensis VB7 + root knot-nematode, VB7 = B. velezensis VB7 alone, TK + RKN = T. koningiopsis TK + root-knot nematode, TK = T. koningiopsis TK alone, VB7 + TK + RKN = B. velezensis VB7 + T. koningiopsis TK + root-knot nematode; RKN, root-knot nematode (M. incognita))

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Similarly, the OTU distribution in the individual applications, i.e., B. velezensis VB7 alone, T. koningiopsis TK alone, and control (untreated) soil samples, was compared at the genus and species levels. A total of 45 OTUs at the genus level and species level were commonly distributed in all combined application treatments, followed by similar OTUs of 25, 13, and 5 at the genus level and 36, 23, and 16 OTUs at the species level for B. velezensis VB7 alone, T. koningiopsis TK alone and control (untreated) soils (Fig. 4C and D).

Diversity of Bacterial Communities in Different Rhizosphere Soils

Alpha Diversity Indexes

Alpha diversity is applied to analyze the richness and diversity of microbial communities present in the soil. The rhizosphere soil of B. velezensis VB7 + T. koningiopsis TK + RKN had the maximum Shannon index in all taxonomic levels from phylum to species with the range of 1.57 to 2.90, followed by B. velezensis VB7 + RKN (1.87 to 2.32). The bacterial communities in RKN-infested soil and T. koningiopsis TK-drenched soil had almost similar levels of richness with lower indexes than other samples (Fig. 5A). The evenness vector algorithm indicated that tomato rhizosphere soil drenched with B. velezensis VB7 + T. koningiopsis TK + RKN contained higher species diversity with more bacterial community richness than other treatments. T. koningiopsis TK, T. koningiopsis TK + RKN, and RKN-infected soil samples had similar levels of bacterial species (evenness) with greater richness except for RKN soil, which had a higher level of microbial diversity with lesser homogeneity of organisms observed (Fig. 5B). The refraction curve showed a significant increase in bacterial species, indicating that nematicide-treated soil had the maximum bacterial communities with more diverse bacterial species compared to other soil treatments (Fig. 5C). The Species Diversity Curve analysis showed that the number of bacterial species in the soil varied according to each treatment. The RKN-infested tomato rhizosphere soil treated with the combined form of B. velezensis VB7 + T. koningiopsis TK had greater species diversity when compared to T. koningiopsis TK, B. velezensis VB7 treated soil without RKN. At the same time, lower species diversity was observed in untreated RKN-infected soil (Fig. 5D).

Fig. 5
figure 5

Alpha diversity index for rhizosphere bacterial communities distributed with respect to different treatments. A Shannon index. B Evenness Vector Algorithm. C Refraction Curve. D Species Diversity Curve (VB7 + RKN = B. velezensis VB7 + root-knot nematode, VB7 = B. velezensis VB7 alone, TK + RKN = T. koningiopsis TK + root-knot nematode, TK = T. koningiopsis TK alone, VB7 + TK + RKN = B. velezensis VB7 + T. koningiopsis TK + root-knot nematode; RKN, root-knot nematode (M. incognita))

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Beta Diversity Indexes

The beta diversity was measured using the Bray Curtis algorithm and Jaccard algorithm index. The Bray Curtis algorithm showed that the B. velezensis VB7 + T. koningiopsis TK + RKN had substantially different communities (0.15), whereas other samples exhibited 99% similar bacterial community compositions. The values of the Jaccard algorithm index indicated that the diversity and similarity between the organisms varied for each sample at every taxonomic level. The bacterial communities in the rhizosphere soil had greater diversity with lesser homogeneity between populations (Fig. 6A).

Fig. 6
figure 6

Beta diversity index for rhizosphere bacterial communities distributed with respect to different treatments. A Jaccard Index (VB7 + RKN = B. velezensis VB7 + root-knot nematode, VB7 = B. velezensis VB7 alone, TK + RKN = T. koningiopsis TK + root-knot nematode, TK = T. koningiopsis TK alone, VB7 + TK + RKN = B. velezensis VB7 + T. koningiopsis TK + root-knot nematode; RKN, root-knot nematode (M. incognita))

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Co-occurrence Clustering Coefficient Analysis of Bacterial Communities in Treated and Untreated Soil Samples

The co-occurrence patterns of all networks differed significantly among treated and untreated RKN-infected soil samples. Co-occurrence network analysis revealed similar nodes (phylum) among the bacteria in the soil communities obtained from the biocontrol agents and nematicide-treated and untreated soil samples (Fig. 7A–F). However, the numbers of nodes and their interconnecting edges (lines) differed. The bacterial phyla, Proteobacteria, Firmicutes, and Actinobacteria, were clustered in more significant proportions with strong interactions (thicker edges with bold letters—greater occurrence) in all the soil samples. The nodes of Proteobacteria interconnecting with other bacterial edges are highlighted in green, whereas the interconnection of Firmicutes is represented in orange (nodes and edges), while brown-colored nodes and edges indicate the population of Actinobacteria. The strong correlation between the abundance of bacterial phylum (thicker lines) with a higher number of interconnection edges was more significant in Proteobacteria, Firmicutes, and Actinobacteria phyla in RKN-associated soils treated with B. velezensis VB7 + T. koningiopsis TK (Fig. 7F). The interaction in B. velezensis VB7 with RKN-associated soil had a diverse effect with higher interactions than B. velezensis VB7 alone without RKN (Fig. 7D and E). The T. koningiopsis TK + TKN and T. koningiopsis TK alone treated soils had sparse edges with a lower coefficient and stronger interactions than RKN samples (Fig. 7B and C). The untreated control RKN soil had diverse, with thinner interconnecting edges (more inferior) noticed between bacterial communities (Fig. 7A). Collectively, the data provided further evidence that the bacterial community in tomato rhizosphere soil was increased in the presence of the combined application of B. velezensis VB7 or T. koningiopsis TK, exhibiting a significant increase in the relative abundance of bacterial communities due to synergistic interactions.

Fig. 7
figure 7

Co-occurrence clustering coefficient networks of bacterial communities obtained from treated and RKN-infected soil. A Co-occurrence clustering coefficient network of different bacterial phyla present in RKN-infected soil alone. B Co-occurrence clustering coefficient network of different bacterial phyla present in the T. koningiopsis TK alone applied soil. C Co-occurrence clustering coefficient network of different bacterial phyla in B. velezensis VB7 alone treated soil. D Co-occurrence clustering coefficient network of different bacterial phyla present in T. koningiopsis TK treated soil with the association of RKN. E Co-occurrence clustering coefficient network of different bacterial phyla in B. velezensis VB7 treated soil with the association of RKN. F Co-occurrence clustering coefficient network of different bacterial phyla in B. velezensis VB7 + T. koningiopsis TK with RKN associated soil. Each node represents a bacterial phylum, whereas the edges represent a clustering coefficient, with a magnitude of 0.01 to 1.00 between the nodes. Each node is labeled at the phylum level. The thickness of the edges represents the strength of clustering and interaction of bacterial species. The thicker edges, with a boldness of bacterial phylum, had a more significant clustering and strong interaction between bacterial communities. The green color nodes and edges represent the interaction and co-occurrence of Proteobacteria; orange color nodes and edges represent the interaction and co-occurrence of firmicutes; brown color nodes and edges represent the interaction and co-occurrence of Actinobacteria

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Discussion

Recently, plant pathogenic nematodes have emerged as a major yield-limiting factor in vegetable crops across the globe. At this juncture, the reduction of plant parasitic nematodes depends on the interaction of different biotic and abiotic factors in the soil. In our investigation, T. koningiopsis TK was highly compatible with B. velezensis VB7. Combined application of B. velezensis VB7 + T. koningiopsis TK had the highest nematicide activity on the hatching of eggs and its mortality compared to individual application of B. velezensis VB7, T. koningiopsis TK, and the untreated control. Consistent with these findings, Tian et al. [38] reported that, within 12 h of exposure to the secondary metabolites of B. velezensis-25, 100% mortality of juveniles of M. incognita was observed. Similarly, the cultural filtrates of B. velezensis CE and B. thuringiensis KYC had the lowest egg-hatching rates of 2.5% and 9% at 40% concentration, respectively [39]. B. amyloliquefaciens had the highest level of suppression in egg hatching and induced the mortality of M. incognita J2s, besides promoting plant growth [40]. According to Sreenayana et al. [41], the conidial suspension of T. koningiopsis TRI 41 effectively reduced the egg hatching of RKN up to 71.51% and 73.0% of juvenile mortality in cucumber. These findings agreed with our findings that combining B. velezensis VB7 and T. koningiopsis TK reduced juvenile mortality and inhibited the egg-hatching of root-knot nematode in tomatoes.

Hence, we conducted a field trial for RKN infection in tomatoes in an endemic location through the combined application of B. velezensis VB7 and T. koningiopsis TK. Metagenomic analysis was carried out from the field samples to investigate the diversity and richness of bacterial communities in the rhizosphere region. Microbiome investigations have targeted bacterial and fungal biomes that are phylogenetically well-characterized and were strongly associated with plant health. It reflects the relative abundance and biodiversity of the microbiome in the rhizosphere [42]. Tomato plant roots are strongly related to diverse microbial communities in the soil [43], which have a unique role in plant growth and development. Several investigations on rhizomicrobiome emphasized the presence of diverse microbes in the root zone, thus contributing towards the enhanced antagonistic efficacy against PPN in vegetable crops [44]. Combining beneficial microorganisms can effectively induce defense mechanisms against PPN [45]. Based on the scientific evidence by Zaim and his coworkers [46], we investigated the compatibility of Trichoderma spp. with Bacillus spp. to determine the synergistic effect of biocontrol agents to promote the microbiome’s diversity. In addition, several previous investigations have also confirmed that parasitism by RKN had a positive correlation with the richness and diversity of microbial communities associated with the suppression of RKN [14. 49–52].

The present study aimed to apply effective BCA, B. velezensis VB7, and T. koningiopsis TK against RKN infestation in tomato rhizosphere. Maintaining high population densities of these microorganisms after inoculation is a substantial constraint since they deteriorate with time and even at a distance from the source of the inoculum. Hence, we conducted the present study to assess the variations in microbial diversity in the rhizosphere region by the combined application of B. velezensis VB7 and T. koningiopsis TK and through the individual application of bioagents on tomato plants in the soil endemic to RKN infestation. The composition relative abundance of the phyla Proteobacteria (42.16%), Firmicutes (19.57%), and Actinobacteriota (17.69%) being dominant in the soil samples drenched with the combined application of B. velezensis VB7 and T. koningiopsis TK. Our findings were consistent with evidence from numerous reports [22, 51]. The high abundance of Proteobacteria in the rhizosphere largely reflects the abundance of γ-proteobacteria, followed by α-proteobacteria. Hitherto, the microbial consortia can substantially modify the composition of the bacterial rhizosphere community by promoting the establishment and development of beneficial bacteria. Rhizobiales were frequently noticed in the rhizosphere of healthy tomato plants and reduced richness and abundance in the bacterial rhizobiome. The reduction in Rhizobiale diversity was attributed to a change in the composition and richness of microbiota during nematode infection. Tian and coworkers [29] reported changes in the abundance of the dominant species of Rhizobiales in the root microbiome during the infestation of RKN in crop plants.

In particular, the genus Bacillus was dominant in all rhizosphere soil samples. However, the relative abundance was highest in the RKN-associated soils treated with a combined application of B. velezensis VB7 + T. koningiopsis TK. Bacillus spp. were ubiquitous in the rhizosphere; consequently, these bacteria suppressed the interaction of RKN with tomato roots and thus acted as a bio-nematicide [52]. Rhizobacteria, such as Bacillus, may positively influence tomato plant growth, functioning as biofertilizers to increase tomato growth [53]. On the other hand, Bacillus was dominant in the presence of RKN, while Cellvibrio and Steroidobacter were more abundant in RKN-infested soils drenched with nematicide [54].

The drenching of RKN-associated rhizosphere with B. velezensis VB7 also increased the abundance frequency of Bacillus, Sphingomonas, and Pseudomonas, while Microbacterium Achromobacter and Ochrobactrum were more abundant in B. velezensis VB7 in the presence of RKN, as compared to Marinomonas and Stenotrophomonas in T. koningiopsis TK, treated soil. In agreement with these findings, the work of Kour et al. [55] confirmed the presence of rhizospheric microbes, including Acinetobacter, Achromobacter, Bacillus, Burkholderia, Flavobacterium, Micrococcus, and Pseudomonas, has a positive impact on plant growth in RKN-infested soil samples. B. velezensis Bv-25 had significant nematicidal activity against M. incognita, significantly reducing the severity of M. incognita and increasing the cucumber yield under field conditions [38]. Similarly, the bacterial genera Sphingomonas, Micrococcus, Bacillus, Methylobacterium, Rhizobium, and Bosea were dominant in soils suppressing M. hapla [56]. In agreement with earlier findings of many researchers in discussion [22, 38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56], our work detected an increase in the relative abundance of Bacillus spp., Firmicutes, Sphingomonas spp., and Pseudomonas spp. in soils drenched with B. velezensis VB7 and T. koningiopsis TK in RKN infested soil. Thus, the synergistic interaction and compatibility of biocontrol agents might have triggered defense mechanisms, plant growth promotion, and yield, as suggested by earlier researchers [46].

The dominance of Vicinamibacteria and Microvirga was also observed in soils without RKN infestation drenched with B. velezensis VB7 alone. The added biocontrol agents might have served as signals to increase the microbiome’s diversity and abundance level.

The tomato rhizosphere soils drenched with biogents of T. koningiopsis TK and B. velezensis VB7 increased the relative abundance of Pseudomonas, Marinomonas, Microvirga, and Marinomonas might have reduced the RKN infestation. The microbes associated with the suppression of RKN pertain to Acinetobacter, Bacillus, Enterobacter, Microbacterium, Paenibacillus, Pseudomonas, and Streptomyces [57].

The Pseudomonas genus from the Pseudomonaceae family and the Marinomonas genus from the Oceanospirillaceae family were also observed abundantly in tomato rhizosphere soil drenched with T. koningiopsis TK in RKN-infested soil, compared to untreated RKN-infested soil samples. An increased abundance of γ-proteobacteria, including Pseudomonas species, produces antimicrobial compounds responsible for suppressing RKN [58]. Certain Pseudomonas strains’ role in suppressing M. javanica in tomato plants concludes that Induced Systemic Resistance (ISR) was the primary mechanism for reducing nematode infestation [59]. The relative abundance of bacterial genera Cellvibrio, Microbacterium, Gaiella, and Nocardioides was abundantly distributed and proliferated more in individual applications of B. velezensis VB7 and T. koningiopsis TK without RKN than in untreated RKN infested soil samples. The aforementioned genera were previously identified in cumber rhizosphere soil and shown to stimulate plant development and decrease plant diseases [60]. Similarly, in the present study, an increase in the abundance of the above bacterial community might have also contributed to plant growth and reduced RKN infestation.

In the present study, Alpha and Beta diversity indexes indicated a higher microbiome abundance and diversity in treated than in untreated soils, which agreed with the findings of Tian et al. [29]. The reduction in the severity of RKN infestation has been attributed to the combined actions of several biocontrol agents [61,62,63]. In concert with this data, our finding also indicated that Pseudomonas spp. was abundant in plant roots treated with B. velezensis VB7, both with and without RKN.

Several investigations have confirmed that Trichoderma spp. inhabits and colonizes plant roots, enhancing their defensive mechanisms against nematodes. The induction of defense against RKN by Trichoderma spp. is also mediated through JA and SA pathways [64,65,66]. In addition, biotic pressure on the root microbial community increased the microbial diversity under the persistent disease state, which resulted in an increase in some minor taxa and a reduction of the dominant core species [67]. The increased interaction between Proteobacteria, Firmicutes, Actinobacteria, and other bacteria in the rhizosphere bacterial network defends against RKN infestation in tomatoes. The increased relative abundance of beneficial bacterial taxa, especially Proteobacteria, in the rhizosphere of microbial consortia-treated soil plots and the higher abundance of genes associated with the indicated cellular processes influenced tuber yield in potatoes [68].

Hence, the diverse community of microorganisms that inhabit the soil around plant roots can play an essential role in helping the plants defend themselves against plant-parasitic nematodes. The diversity of microbial communities has also facilitated metabolic interaction to execute intricate functions that an individual organism might have accomplished. Members of the consortia or communities may interact by exchanging metabolites or molecular signals to regulate their activity through the temporal and geographical expression of essential processes [69]. Using genomic approaches, the four best-suited genera for developing biocontrol inoculants are Bacillus, Chitinophaga, Rhizobium, and Burkholderia [70]. This suggests that changing the rhizosphere microbiome may positively influence plants to tolerate RKN infestation.

The biocontrol agents immediately control the nematodes and other pathogens and reduce the risk of developing resistance [71,72,73,74,75,76,77,78]. Further, compatibility between B. velezensis VB7 and T. koningiopsis TK also increased the relative abundance of the beneficial bacterial community in the tomato rhizosphere, which might have complimented the multiple modes of action to suppress RKN in the tomato rhizosphere.

Conclusion

We investigated the effects of microbial diversity from biocontrol agents and nematicide-treated tomato plants through high-throughput sequencing analysis. Our results revealed that treated and untreated soil had a larger microbial community abundance. The bacterial phylum Proteobacteria, followed by firmicutes and actinobacteria, were dominant. The soil treated with biocontrol agents had the maximum diversity and relative abundance with biologically active microbial genera. Bacillus, Pseudomonas, and Sphingomonas were bionematicides against RKN and enhanced plant growth. Overall, the study suggested that manipulating the bacterial microbiome in the rhizosphere through the use of nematicidal molecules along with microbial biocontrol agents could be an effective strategy for managing plant nematode infections, thereby improving plant growth and yield.