A New Era in the Discovery of Biological Control Bacteria: Omics-Driven Bioprospecting

Abstract

Biological control with beneficial bacteria offers a sustainable alternative to synthetic agrochemicals for managing plant pathogens and enhancing plant health. However, bacterial biocontrol agents (BCAs) remain underexploited due to regulatory hurdles (such as complex registration timelines and extensive dossier requirements) and limited strain characterization. Recent advances in omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have strengthened the bioprospecting pipeline by uncovering key microbial traits involved in biocontrol. Genomics enables the identification of biosynthetic gene clusters, antimicrobial pathways, and accurate taxonomy, while comparative genomics reveals genes relevant to plant–microbe interactions. Metagenomics uncovers unculturable microbes and their functional roles, especially in the rhizosphere and extreme environments. Transcriptomics (e.g., RNA-Seq) sheds light on gene regulation during plant-pathogen-bacteria interactions, revealing stress-related and biocontrol pathways. Metabolomics, using tools like Liquid Chromatography–Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance spectroscopy (NMR), identifies bioactive compounds such as lipopeptides, Volatile Organic Compounds (VOCs), and polyketides. Co-culture experiments and synthetic microbial communities (SynComs) have shown enhanced biocontrol through metabolic synergy. This review highlights how integrating omics tools accelerates the discovery and functional validation of new BCAs. Such strategies support the development of effective microbial products, promoting sustainable agriculture by improving crop resilience, reducing chemical inputs, and enhancing soil health. Looking ahead, the successful application of omics-driven bioprospection of BCAs will require addressing challenges of large-scale production, regulatory harmonization, and their integration into real-world agricultural systems to ensure reliable, sustainable solutions.

1. Introduction

Sustainable agriculture is increasingly demanded worldwide due to environmental and health concerns over chemical pesticides [1,2]. Evidence shows that global pesticide use reached approximately 3.71 million tons of active ingredients in 2022, reflecting a 14% increase over the last decade despite growing regulatory pressure, such as the European Union mandate to reduce chemical pesticide use by 51% by 2030 while promoting biopesticides and integrated pest management [3]. In this context, the bioprospecting of biological control agents has emerged as a compelling and ecologically sound alternative [4]. Far from being a narrow concept, biological control encompasses a broad spectrum of mechanisms and interactions that challenge simple definitions. Biological control is the use of living organisms or their derivatives to suppress pest populations and mitigate their impact on agricultural ecosystems. While a broad range of organisms can serve as biocontrol agents, bacteria play a central role due to their adaptability, ecological interactions, and production of diverse antimicrobial compounds [5]. These biological agents interact with target pests by reducing their population density, reproductive potential, or infection capacity, thereby contributing to the dynamic regulation of pest populations rather than their complete eradication. Such interactions often result in increased mortality or diminished reproductive and competitive abilities of the pests.

Given this multifaceted mode of action, the diversity and abundance of microbial control agents are extensive [4], constituting a significant component of microbial biological control. These include amphiphilic lipopeptides (e.g., surfactins, iturins, fengycins) that permeabilize pathogen membranes, polyketides (e.g., difficidin, macrolactin) that inhibit cell division or virulence genes, and hydrolytic enzymes (chitinases, glucanases) that degrade pathogen cell walls [6,7].

Among these microorganisms, bacteria are one of the most widely utilized due to their versatility and efficacy [8,9]. Notably, various species within the genus Bacillus serve as effective biocontrol agents; Bacillus thuringiensis is extensively employed for insect pest management, while Bacillus subtilis and B. amyloliquefaciens are applied to control plant diseases. In addition, plant growth-promoting bacteria (PGPB), including genera such as Azospirillum, Pseudomonas, and Rhizobium, contribute to crop health by synthesizing antifungal and antimicrobial compounds, enhancing plant defense responses, and improving nutrient solubilization and uptake [10,11]. Integrating bacterial biocontrol agents into agriculture provides a sustainable alternative to chemical inputs, supporting pest management, reducing resistance risk, and enhancing soil and plant health [12,13].

The current global biocontrol agent market is estimated to be $3.7 billion in, rising at ~7% annually, and is projected to reach ~$6 billion by 2030 [14]. There are over 1000 registered active biocontrol products worldwide [15]. However, BCAs as a whole still account for <5% of total crop protection expenditures, indicating substantial room for expansion [16]. From 2022 to 2025, the number of bacterial strains with US EPA approval for controlling plant diseases (mainly species among Bacillus, Burkholderia, Pseudomonas, Streptomyces, Paenibacillus, and Agrobacterium) has tripled, rising from 12 to 36, reflecting both a growing market demand and the stringent regulatory requirements (such as biosafety assessments, variability in field efficacy, and environmental impact evaluations) that continue to limit product registration [17]. Although BCAs show efficacy against insects, weeds, and especially plant pathogens, their adoption in modern agriculture remains limited, with estimates suggesting that only a small fraction of registered BCAs are widely applied in crop production. Importantly, bottlenecks in the bioprospecting pipeline can arise both during discovery (when identifying promising strains and functional traits) and during development, when translating these candidates into safe, field-ready products that meet regulatory requirements.

Despite this potential, the deployment of bacterial biocontrol agents cannot overlook biosafety considerations. Several bacterial genera with agricultural relevance (e.g., Burkholderia, Serratia, and Pseudomonas) also include strains that are opportunistic or pathogenic to humans, animals, or plants [18,19,20]. Therefore, rigorous risk assessment is an essential component of the bioprospecting pipeline. This includes comprehensive strain characterization to confirm the absence of virulence genes, antibiotic resistance determinants, or other traits that could pose biosafety concerns. Standardized assays for pathogenicity, cytotoxicity, and host range are critical for distinguishing safe beneficial strains from potential pathogens [21]. Moreover, regulatory frameworks require extensive toxicological, environmental fate, and ecological impact testing before strain approval [22]. These steps ensure that microbial agents released into the environment will not disrupt non-target organisms, compromise ecosystem stability, or threaten public health. Incorporating biosafety testing and risk management early in the discovery and development process reduces delays during product registration and strengthens public trust in the use of microbial technologies.

Thus, global trends and technological advances point to an expanding role for bacterial biocontrol in sustainable agriculture. Researchers are identifying new microbial strains, genes, and metabolites that enhance efficacy against pests through classical screening with modern omics-driven discovery [11]. In this context, bioprospecting has emerged as a crucial approach to uncover and harness the biological diversity that underpins sustainable agricultural innovations.

Bioprospecting refers to the systematic exploration, identification, and utilization of biological resources (ranging from microorganisms and plants to their associated metabolites) from diverse ecosystems to discover traits or compounds with social, ecological, or commercial value [23]. While the concept encompasses a broad range of disciplines and industrial applications, agriculture has emerged as one of the sectors most significantly enhanced by bioprospecting, particularly through the identification of microbial agents that support sustainable crop production.

In agricultural systems, bioprospecting for beneficial bacteria plays a central role in identifying novel candidates for biological control and plant growth promotion. Traditionally, this process has involved the isolation and functional screening of microbial strains from agricultural soils, rhizospheres, phyllospheres, endospheres, and extreme or underexplored environments. Recent advances in high-throughput sequencing and omics technologies (such as metagenomics, comparative genomics, transcriptomics, and metabolomics) have accelerated this discovery pipeline, although these high-throughput approaches can generate false positives, and reproducibility can be a challenge, necessitating careful validation of candidate strains. These tools enable the identification of microbial taxa, genes, and metabolites with desirable agricultural traits [11]. These tools have enhanced our understanding of microbial ecology and function, supporting the rational selection and formulation of microbial consortia.

2. Genomics: Unlocking the Genetic Potential of Biological Control Bacteria

Genomics, within the context of bacterial systems, involves the comprehensive analysis of the entire genome to elucidate genetic architecture and functional potential (Figure 1). It relies on high-throughput sequencing platforms and bioinformatics pipelines for genome assembly, annotation, structural and functional characterization [24]. The complete sequencing of bacterial genomes, along with bioinformatics tools such as Prokka (rapid Prokaryotic Genome annotation), RAST (Rapid Annotations using Subsystems Technology), and antiSMASH (antibiotics and secondary metabolites analysis shell), has been a powerful approach in biological control [25,26].

These tools have enabled the sequencing of whole bacterial genomes and the subsequent classification of genes related to plant growth-promoting activities and biological control. For example, Bacillus thuringiensis is one of the most commonly used bacteria in pest control; however, recent studies have shown its antifungal potential. In the NBAIR-BtAr strain, genes encoding lipopeptides with activity against Sclerotium rolfsii [27]. On the other hand, bioinformatic analysis revealed that Bacillus inaquosorum TS022 harbors genes associated with plant growth-promoting activities and biological control as well [28]. Moreover, a more accurate taxonomic assignment, while the use of the 16S marker has contributed to taxonomic affiliation at the genus level, phylogenomics allows for inferences at the species level [29,30]. Similarly, 885 B. thuringiensis strains were initially classified within this genus and species; however, after their complete genomes were analyzed using Average Nucleotide Identity (ANI) and digital DNA–DNA hybridization (dDDH), like the genome-to-genome distance calculator (GGDC), 82 strains were misclassified and 805 were confirmed to authentically belong to B. thuringiensis [30]. This precise delimitation is crucial to distinguish non-pathogenic from potentially pathogenic strains, ensuring safe application in biotechnology and agriculture. Furthermore, Martinez-Vidales et al. [26] employed OGRIs (overall genome relatedness indices), antiSMASH, and the Reference sequence Alignment-based Phylogeny builder (Realphy) platform to analyze Bacillus genome strains. They observed that strains with shorter evolutionary distance from Bacillus subtilis have a high potential to produce compounds with activity against pests [26].

The whole genome sequencing allows taxonomic assignments and gene classification (

Table 1. Examples of genomic mining tools used in identifying biological control agents.

Strain	Species	Key Traits	Genomic Tools Used	Agricultural Application	Reference
NBAIR-BtAr	Bacillus thuringiensis	Lipopeptides active against Sclerotium rolfsii	Prokka, RAST, antiSMASH	Antifungal biocontrol	[35]
TS022	Bacillus inaquosorum	Surfactin, Bacillaene, Fengycin, Pipastain, Bacillibactin, among others	ANI, GGDC, Prokka, RAST, antiSMASH	Plant growth promotion, biocontrol against B. sorokiniana	[28]
[29]	Burkholderia ambaria	Broad-spectrum antifungal activity	ANI, dDDH, OGRIs, antiSMASH, Realphy	Biocontrol	[29]
BN	Bacillus velezensis	Fengycin and surfactin	antiSMASH, NR, Swiss-Prot, Pfam, EggNOG, GO, KEGG	Broad-spectrum antimicrobial	[32]
NEB573	Brevibacillus brevis	Unexplored secondary metabolites	Phylogenetic software, pan-genome, secondary metabolite mining tools	Plant disease management, growth promotion	[34,36]
AF23	Bacillus halotolerans	Genes for salt stress tolerance, biocontrol, and plant growth promotion	Comparative genomics	Plant growth promotion in tomato; synergistic with AF12	[36]
TE3T	Bacillus cabrialesii subsp. cabrialesii	Lipopeptides, siderophores, antimicrobial compounds	RAST, PROKKA, PGAP, antiSMASH,	Biocontrol against B. sorokiniana	[37,38,39]
TRQ65	Bacillus paralicheniformis	Lipopeptides, siderophores, antimicrobial compounds	RAST, antiSMASH, BAGEL	Biocontrol against Botrytis, Fusarium, Bipolaris, and other phytopathogens	[37,40]

); however, other tools enable the identification of additional characteristics. Genome mining is a process used to analyze and explore complete genomes to discover new gene functions, predict metabolic pathways, and identify genes useful in agriculture [25,27]. Using genome mining, several Bacillus velezensis strains have been shown to possess strong antifungal and antimicrobial potential. B. velezensis LGMB12 contains biosynthetic gene clusters (BGCs) for iturin and bacillomycin, with 16.2% of its genome dedicated to secondary metabolite production, significantly higher than in other Bacillus species [31]. Similarly, B. velezensis BN encodes genes for fengycin and surfactin, with 19.2% of its genome devoted to antimicrobial biosynthetic pathways, identified using bioinformatics platforms such as the antibiotics and Secondary Metabolite Analysis Shell (antiSMASH), Non-Redundant Protein Sequence Database (NR), Swiss-Prot, Protein Families Database (Pfam), evolutionary genealogy of genes: Non-supervised Orthologous Groups (EggNOG), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) [32]. B. velezensis Ag109, applied as a microbial inoculant for soybeans, also harbors genes suggesting potential for biocontrol of fungi and nematodes, based on antiSMASH analysis [33]. For comparison, B. ambifaria CF3 exhibits biosynthetic pathways for terpene, polyketide, lanthipeptide, T1PKS, and butyrolactone, indicating broad-spectrum antifungal activity (Table 1) [29]. Nine genome sequences of Brevibacillus brevis were analyzed using phylogenetic software, pan-genome tools, and secondary metabolite mining software, the results revealed these strains possess numerous unexplored secondary metabolites and have great potential applications in plant disease management and plant grow promotion, finally, strain NEB573 has the potential to belong to this specie and could represent a novel bacterial strain (Table 1) [34]. Thus, analyzing and exploring the bacterial genome are important to assigning functions; moreover, comparative genomics allows the identification of unique genes, novel bacterial species, and genes involved in virulence, among others.

Comparative genomics using ANI, pan-genome tools, and orthologous analysis facilitates the identification of key virulence genes for biocontrol or genes providing a selective advantage. For example, omics methods have permitted the identification of lipopeptides, antimicrobial, plant growth-promoting, and nematicidal genes in the genus Bacillus; therefore, this genus exhibits antifungal and antibacterial activity [37,39,41,42]. For example, B. velezensis 160; this strain shares 2804 genes and clusters for secondary metabolites with four isolates; however, B. velezensis 160 has three genes coding for surfactin and a cluster associated with the biosynthesis of laterocidine, these metabolites could enhance its biocontrol ability [43]. Furthermore, Martinez-Vidales et al. [26] correlated the taxonomic affiliation of Bacillus isolates with their genomes; they suggested that when Bacillus strains exhibit a shorter evolutionary distance from B. subtilis, they are more likely to produce biological control compounds. B. velezensis AF12 and B. halotolerans AF23 are two different bacterial species; however, comparative genome analysis revealed that both strains harbor genes related to salt stress, biocontrol, and plant growth promotion. F12 and AF23 were used to study plant growth-promoting, showing a synergistic effect in tomato cultivation [36]; this feature promotes the use of microbial consortia.

Furthermore, biological control has traditionally relied on a single strain to manage insect pests or plant disease. While this approach has been highly effective, the use of synthetic microbial communities has increased. Research has revealed that interaction within synthetic microbial communities not only helps control pests and plant diseases but also enhances crop growth and promotes plant development. For example, a study was conducted with six beneficial bacterial strains, either alone or in synthetic microbial communities (each consisting of three strains); the results showed that single bacterial strains had no effect, while the maize grew better and exhibited drought tolerance when the seeds were inoculated with the synthetic bacterial community [44]. A native synthetic bacterial community was used in wheat crops, and these bacteria had positive effects, as shown by inoculation with B. cabrialesii subsp. cabrialesii TE3^T, P. megaterium TRQ8, and B. paralicheniformis TRQ65 increased crop yield, maintained grain quality, and allowed for a 50% reduction in fertilization; the genomes of these bacteria were analyzed and compared, revealing that these isolates shared biofertilization genes [37]. Also, Wang et al., [45] worked with a subset of 23 rice-beneficial bacterial colonizers; first, they created six synthetic bacterial communities (each consisting of five strains) using compatibility tests; then, they inoculated rice plants and observed the results; second, they selected the best synthetic bacterial community, and finally, they obtained two five-strain and one two-strain synthetic bacterial communities’; genome analysis revealed that these contained genes involved in plant growth promotion. These were applied in tomato, maize, and wheat crops as promising biofertilizers; genome analysis revealed that these strains shared genes involved in protection against oxidative stress and other beneficial functions [46].

The use of bacteria in biological control has been crucial for managing pest insects and plant diseases. However, whole-genome sequencing and bioinformatics tools have enabled the classification of genes involved in plant growth promotion, stress response, antimicrobial activity, and the production of secondary metabolites, among others. Additionally, bioinformatic tools have facilitated genomic comparisons to identify differences at the genus and species levels, determine taxonomic affiliations, and distinguish shared and unique characteristics. This has made it possible to combine multiple beneficial microorganisms, allowing them to work synergistically to reduce plant disease and improve crop management. By integrating these genomic insights, researchers can establish rational design rules for synthetic microbial communities (SynComs), selecting strains with complementary traits to optimize plant-beneficial outcomes.

3. Metagenomics: Exploring Uncultured Microbial Reservoirs

Metagenomics is the study of microorganisms using genomic and genetic data obtained from a specific environment, through the detection of functional genes or sequencing analysis. There are two main approaches in metagenomics: functional metagenomics and sequencing metagenomics, also referred to as shotgun metagenomics. While sequencing metagenomics focuses on exploring the microbial diversity of genetic sequences, functional metagenomics aims to explore genes with specific functions, especially those encoding microbial metabolites (e.g., antibiotics, lipopeptides, VOCs, enzymes, siderophores, among others) and bioactive substances of biotechnological interest [47]. For example, functional metagenomics has enabled the discovery of novel enzymes with biotechnological potential, such as those reported by Berini et al. (2017) [48], which explains the workflow in discovering novel enzymes, outlining the multiplicity of possible screening paths from eDNA extraction to heterologous expression of selected protein sequences. This highlights the ability of functional metagenomics to uncover bioactive compounds and enzymes that would remain undetected using traditional culture-based approaches. Metagenomic analysis begins with DNA sampling and extraction from environmental sources such as agricultural soils or rhizospheres. High-throughput sequencing platforms, such as Illumina, PacBio, or Oxford Nanopore, are typically employed to generate comprehensive sequence data, allowing for in-depth characterization of microbial communities. Data processing through metagenomics consists of four key stages: 1. DNA sampling and extraction from environmental sources such as agricultural soils or rhizospheres; 2. Construction of metagenomic libraries; 3. Functional screening, where specific gene activities (such as phosphorus solubilization, nitrogen fixation, or organic matter decomposition) are evaluated, and 4. Bioinformatics analysis (e.g., QIIME2 and Mothur for taxonomic profiling, MetaPhlAn for strain-level analysis, PROKKA and RAST for genome annotation, eggNOG-mapper for functional classification, and KEGG, COG, or PICRUSt for predicting metabolic pathways and ecological functions) is implemented, which helps characterize the identified genes and predict their ecological roles. Metagenomic research has revealed important genes such as nifH and nifA that are crucial for biological nitrogen fixation and are widely used as molecular markers [49]. For instance, metagenomic studies have not only identified key nitrogen-fixation genes in the rhizospheres of crops like maize and legumes, but also uncovered novel genes and microbial taxa with potential biocontrol activities. For example, genes encoding antimicrobial lipopeptides, such as surfactin, fengycin, and iturin, have been detected in previously uncultured Bacillus and Pseudomonas strains, highlighting their ability to suppress soil-borne pathogens [50,51]. Additionally, metagenomic functional screens have revealed enzymes capable of degrading fungal cell walls and genes involved in siderophore-mediated iron competition, both of which are critical traits for effective BCAs [52]. These findings demonstrate that metagenomics enables the identification of specific functional genes and candidate microbial strains, moving beyond general community profiling and directly contributing to the discovery of novel BCAs for sustainable agriculture.

These genes, found in the rhizospheres of crops such as corn and legumes, facilitate the conversion of atmospheric nitrogen into plant-assimilable forms, reducing the need for synthetic fertilizers and enhancing soil sustainability [49,53]. However, quantifying these genes in environmental samples can be challenging due to variability in gene copy number across taxa and the need for carefully designed primers to ensure accurate amplification and detection. In this sense, these studies demonstrate the potential of metagenomics to identify genetic functions by focusing on microbial diversity, its composition, and its functional activities. In addition to identifying genes with potential agro-biotechnological applications, metagenomic techniques also allow the study of microbial dynamics within their natural environment (Figure 2).

In a niche, microorganisms do not exist in isolation or a controlled environment but interact naturally with each other within specific ecological niches, where their functions are mainly determined by environmental factors and the relationships they establish with other species [54]. The ecological niche is defined as a multidimensional space where dimensions are the environmental conditions and resources that define the requirements of a species to prevail [55]. It is estimated that only ≈1% of the total soil microbiota can be grown in the laboratory, because most soil microorganisms have specific metabolic requirements or depend on complex interactions with other organisms in their environment, which makes it difficult to isolate in artificial culture media, in this context, metagenomics has allowed studying the soil microbiome without the need for cultivation [56]. Phylogenetic studies have revealed that closely related species share similar gene sets due to their common evolutionary heritage, which influences their adaptive capacity and ecological niche [57]. Species with recent common ancestors show greater similarity in their genomes, particularly in orthologous genes that retain their functions [58]. Some of the evolutionary mechanisms that are involved in these processes are natural selection, favoring adaptive genes in specific environments, horizontal transfer (the movement of genetic material between microorganisms that are not in a parent-offspring relationship) in microorganisms, modifying ecological niches, and gene duplications that allow functional diversification [59,60]. Thus, the gene composition acts as a bridge between phylogeny and ecology, because the study of metabolic genes allows for determining tolerable temperature or pH ranges of microorganisms, gene regulation systems modulate environmental responses, and microbial interaction genes (quorum sensing) define trophic networks [49]. In this sense, metagenomics integrates these elements by sequencing microbial communities in situ, identifying metabolic profiles through comparison with phylogenetic databases (e.g., NCBI RefSeq, SILVA, Greengenes, KEGG, and eggNOG). Therefore, with the implementation of metagenomics from the perspective of ecological niche, it is possible to predict fundamental niches using adaptive genomic markers, detect evolutionary changes during disturbances, and design bio-remediation strategies based on phylogenetic relationships [61]. Metagenomic analysis of taxonomic and functional diversity in soil microbiomes has shown how agricultural practices can result in the death of key species, leading to loss of functional diversity [62,63]. Therefore, the implementation of metagenomic techniques complemented with metatranscriptomic and metaproteomics studies allows us to understand microbial functions and microbial distribution in soils [62,64].

The application of biological control bacteria has emerged as a promising strategy to modulate the soil microbiome and promote plant health. For example, Kannan and Sureendar (2009) [65] demonstrated that inoculation with a bacterial community suppressed the growth of R. solani, thereby improving plant health and promoting plant growth [66]. However, the efficacy of these bacteria is contingent upon their capacity to establish a viable population within the indigenous microbial community. While the soil microbiome’s capacity to support food production is acknowledged, the specific microbial taxa that facilitate plant growth are not yet fully characterized [67]. The inoculation of non-native biological control bacteria (BCB) in soils has been observed to result in low colonization rates and limited functional expression. This phenomenon can be attributed to the fact that microorganisms adapted to a specific soil environment often have difficulty colonizing new soils and translating their functional capabilities [68]. Additionally, soil physicochemical factors such as pH, nutrient availability, moisture, and texture can further influence microbial establishment and activity in these environments [68]. In addition, native microbial communities tend to be resilient to the introduction of new species and attempt to restore the original microbial community structure [69]. Conversely, it has been documented that native microorganisms exhibit remarkable efficacy in colonizing their respective soils, thereby enhancing soil functionality, particularly in cases of infertile soil [70]. Metagenomics provides a foundation for understanding the functional potential of soil microorganisms. Building on this knowledge, it is essential to explore how biological control bacteria (BCB) interact within soil communities, establish themselves, and exert their effects on plant health and soil ecosystem stability.

The use of BCB represents an alternative to reduce the incidence of phytopathogens, some of the genera most studied for their biological control capabilities are Bacillus and Pseudomonas that, through direct mechanisms such as the secretion of diffusible and/or volatile antimicrobial substances, cell wall degrading enzymes, biosurfactants and mycoparasitism, and indirect mechanisms such as competition for nutrients (e.g., siderophore production) and space, protect plants against pathogens, in addition to the fact that biocontrol bacteria can produce biofilms that contribute to bacterial survival and plant-host colonization [51,68].

The introduction of BCB into the soil can exert ecological effects by displacing native microorganisms through competition for essential nutrients, mainly via the production of siderophores [71]. This competitive mechanism modifies the soil microbial community structure and can reshape the balance of native populations, as reported in field studies where inoculated strains altered microbial interactions and promoted shifts in community composition [52]. In parallel, BCB also displays antimicrobial effects through the secretion of metabolites such as lipopeptides and fengycin, which directly inhibit the growth of phytopathogens. These compounds not only suppress pathogens but also influence the relative abundance of certain microbial groups, leading to changes in soil diversity [50]. Field evaluations have shown that such metabolite-mediated effects contribute to healthier rhizosphere microbiomes and improved plant performance under agricultural conditions [72]. Together, the ecological and antimicrobial mechanisms of BCB highlight their potential as sustainable tools to promote soil fertility and plant health, offering an alternative to chemical inputs while supporting resilient soil ecosystems.

Additionally, metagenomics provides a direct avenue for the discovery and characterization of biological control agents (BCAs). By analyzing environmental DNA, researchers can identify genes associated with antimicrobial activity, nutrient competition, or plant growth promotion, even in microorganisms that cannot be cultured in the laboratory [73]. This genetic insight allows the prioritization of candidate strains for isolation and functional validation, streamlining the development of effective BCAs. Compared to other omics approaches, such as metatranscriptomics or metaproteomics, metagenomics offers a comprehensive view of the microbial potential present in the soil, rather than capturing gene expression or protein activity at a single point in time. Consequently, metagenomics serves as a powerful tool to uncover new BCAs and their functional traits, accelerating their application in sustainable agricultural practices.

Despite its powerful applications, metagenomics has certain limitations that should be considered. Complex microbial communities, such as those in soil, often contain closely related strains and high levels of genetic redundancy, making it challenging to assemble complete genomes and assign functions accurately. Additionally, the presence of horizontal gene transfer can complicate the link between specific genes and microbial taxa. Moreover, functional predictions derived from metagenomic data do not always reflect actual gene expression or activity in situ, as environmental conditions and microbial interactions can strongly influence gene regulation. Interpreting these data requires careful bioinformatic analysis and, ideally, complementary approaches such as metatranscriptomics, metaproteomics, or metabolomics to validate the functional relevance of identified genes. Recognizing these limitations provides a balanced perspective on the capabilities and constraints of metagenomic approaches.

4. Transcriptomics: Deciphering Molecular Interactions

Transcriptomics has the power to decipher signaling factors, pathways, and molecular mechanisms in both microbes and host plants that regulate gene expression patterns. This allows us to gain knowledge on regulatory factors of biological control mechanisms, which leads to optimizing their efficiency [74]. The functional characterization of plant-beneficial bacteria has been greatly advanced by transcriptomic tools, particularly RNA sequencing (RNA-Seq), which enables comprehensive profiling of gene expression under varying environmental conditions. RNA-Seq has emerged as an indispensable tool for unraveling bacterial responses to abiotic stress, as well as the molecular mechanisms underlying plant growth promotion and biological control of phytopathogens (Figure 3) [75,76,77]. This high-throughput technology facilitates the identification of differentially expressed genes (DEGs), regulatory RNAs, and metabolic pathways activated during host colonization or antagonistic interactions, providing critical insights for the development of robust microbial inoculants [78].

For instance, in Bacillus subtilis BsCP1 and BsPG1, a transcriptomic profiling under osmotic and oxidative stress has identified that both strains produced various bioactive metabolites, including antimicrobial compounds (plipastatin, surfactin, bacilysin, etc.), VOCs, and extracellular enzymes, with differentiation in their synthesis pathways. These metabolites contributed to plant growth promotion and stress resilience by activating plant hormonal and defense pathways, such as the ABA signaling pathway, which regulates stomatal closure, osmotic balance, and stress-responsive gene expression [79,80]. The differential production of bioactive metabolites between two Bacillus subtilis strains under stress conditions underscores the importance of strain-level characterization in bioprospecting. Even within the same species, strains can exhibit distinct metabolic and regulatory responses, which directly impact their effectiveness as bioinoculants. These finding highlights the need for functional screening that goes beyond taxonomy. Multi-omic approaches are essential to identify the most promising microbial candidates for specific agricultural applications.

RNA-Seq has also proven invaluable for the understanding of the mechanisms by which beneficial bacteria exert biocontrol over plant pathogens. By profiling gene expression during interactions with target pathogens or host plants, researchers have identified key genes and pathways associated with biocontrol efficacy. For example, an RNA-Seq analysis during root colonization of Brachypodium in the late exponential phase of the Pseudomonas starins has revealed the upregulation of genes involved in carbohydrate and amino acid metabolism, osmoprotectant utilization, secondary metabolite production, and transport systems, reflecting a complex response facilitating plant-microbe interactions [81].

Another example of transcriptomic analysis of tomato roots treated with biocontrol agents such as Bacillus velezensis and Pseudomonas fluorescens [82,83,84]. This results in significantly reducing tomato bacterial wilt by inhibiting the pathogen Ralstonia solanacearum and promoting plant growth [84]. Transcriptomic analysis showed these bacteria induce extensive gene expression changes in tomato roots, activating defense-related pathways such as hormone signaling and secondary metabolite biosynthesis [84]. They also modify the rhizosphere microbial community. These coordinated effects underlie their strong biocontrol efficacy in tomato. This comprehensive molecular insight informs the development of effective biocontrol strategies in tomato crops [82].

Moreover, RNA-Seq enables the simultaneous examination of host, pathogen, and biocontrol agent transcriptomes, providing a holistic view of the tritrophic interactions that determine disease outcomes. This systems-level approach has uncovered host defense responses, such as the activation of pathogenesis-related proteins and phytohormone signaling, in response to both pathogen attack and biocontrol agent colonization. For example, transcriptomic analysis revealed that pre-storage treatment of mango fruit with Bacillus siamensis reduced anthracnose caused by Colletotrichum gloeosporioides [85]. Over 56,000 differentially expressed genes were identified, with enrichment in defense-related pathways such as plant–pathogen interactions, hormone signaling (salicylic and jasmonic acid), and phenylpropanoid biosynthesis. These results indicate that B. siamensis primes host defenses, enhancing postharvest resistance [85]. This study underscores the value of transcriptomic approaches in identifying microbial elicitors and host-response biomarkers, offering insights into the mechanistic basis of microbe-induced resistance. It also highlights how omics-guided bioprospecting can support the development of targeted, residue-free postharvest biocontrol strategies that align with sustainable crop protection goals.

Despite these advances, transcriptomic studies face challenges such as variability in gene expression across conditions, the need for robust controls and replicates, and the complexity of analyzing host–pathogen–microbe interactions, all of which require careful design and advanced computational approaches to ensure reliable insights for biocontrol strategies.

5. Metabolomics: Profiling Bioactive Compounds for Enhanced Biocontrol

Metabolomics has emerged as a critical tool in the bioprospecting of microbial biocontrol agents (BCAs), which enables the comprehensive profiling of secondary metabolites that contribute to antagonistic activity and plant-beneficial traits (Figure 4). Advanced analytical techniques (

Table 2. Key metabolomic technologies used for profiling bioactive compounds in biocontrol research.

Technology	Description	Advantages	Limitations	Typical Applications in Biocontrol
LC-MS (liquid chromatography–mass spectrometry)	Combines chromatographic separation with mass spectrometry for sensitive detection of metabolites	High sensitivity and broad metabolite coverage; suitable for complex mixtures; can detect low-abundance compounds	Requires sample preparation, potential matrix effects, and instrument cost	Discovery and quantification of antimicrobial secondary metabolites (e.g., lipopeptides, phenazines) from bacterial and fungal BCAs
GC-MS (Gas Chromatography–Mass Spectrometry)	Separation of volatile and semi-volatile metabolites followed by mass spectrometry detection	Excellent for volatile compounds; high resolution and reproducibility	Limited to volatile/thermally stable metabolites; derivatization often needed	Profiling volatile organic compounds involved in pathogen inhibition and signaling
NMR (Nuclear Magnetic Resonance) Spectroscopy	Spectroscopic technique providing structural information of metabolites based on nuclear spin properties	Non-destructive, highly reproducible, minimal sample preparation; structural elucidation	Lower sensitivity compared to MS; requires larger sample amounts	Structural characterization of novel antimicrobial compounds; in situ metabolic profiling
Ion Mobility Spectrometry (IMS) coupled with MS	Adds an ion mobility separation step before MS to separate isomers and conformers	Improved separation of complex mixtures; faster analysis	Requires specialized instrumentation; data complexity	Enhanced resolution of structurally similar bioactive metabolites
Direct Injection MS (DIMS)	Direct introduction of the sample into the MS without prior chromatographic separation	Very high throughput; minimal sample prep	Reduced metabolite coverage due to ion suppression/matrix effects	Rapid screening of metabolite profiles in large BCA libraries

), particularly mass spectrometry (MS), often coupled with chromatographic separation methods such as liquid chromatography (LC) or gas chromatography (GC), offer high sensitivity and broad metabolite coverage. In these workflows, LC and GC serve as separation tools that resolve complex mixtures of metabolites before their detection by MS, enabling both untargeted and targeted discovery of secondary metabolites critical for biocontrol activity [86,87]. Complementarily, nuclear magnetic resonance (NMR) spectroscopy provides robust structural elucidation and quantification of metabolites in complex biological matrices with high reproducibility and minimal sample preparation. While MS excels at detecting metabolites at very low concentrations and profiling complex mixtures, NMR provides highly reliable structural information and absolute quantification, even for compounds that are difficult to ionize. The integration of MS and NMR techniques enhances the dereplication (e.g., rapid identification of known compounds) and characterization of novel antimicrobial compounds, facilitating the identification of metabolites that may be overlooked by a single analytical platform [88].

Metabolomic analyses have identified numerous novel antimicrobial metabolites from bacterial BCAs. For instance, Bacillus species are well-known producers of lipopeptides such as surfactins and fengycins, which exhibit broad-spectrum antifungal and antibacterial effects [38]. Metabolite profiling using LC-MS/MS and GC-MS has also revealed volatile organic compounds (VOCs) which trigger plant defense pathways (e.g., pathogenesis-related proteins) from endophytic bacteria (Serratia sp. Ba10, Pantoea sp. Sa14, Enterobacter sp. Ou80, Pseudomonas sp. Ou22, Pseudomonas sp. Sn48, and Pseudomonas sp. Ba35) isolated from grapevines produced VOCs that significantly reduced virulence traits of Agrobacterium tumefaciens, the causal agent of crown gall disease [89]. Furthermore, they provided evidence that GC-MS identified 15 or 16 VOCs per strain, including compounds like dimethyl disulfide and 2-undecanone, which disrupted pathogen motility, biofilm formation, and cell morphology [89]. These approaches have expanded the repertoire of bacterial metabolites with potential applications in sustainable agriculture through the bioprospecting of desired biological control traits with the use of metabolomics.

Also, recent metabolomic studies of co-cultures involving bacterial biocontrol agents have provided compelling evidence of metabolic shifts that enhance antimicrobial compound production, offering new insights into microbial interactions that improve biocontrol efficacy. For example, LC-MS/MS profiling of Bacillus velezensis B115 during co-culture with Fusarium oxysporum revealed significant upregulation of antimicrobial lipopeptides like surfactins and fengycins, which disrupted fungal hyphae and biofilm formation [90]. Genomic analysis further identified 13 biosynthetic gene clusters (BGCs) linked to these compounds, demonstrating how pathogen presence triggers metabolic reprogramming in BCAs to amplify biocontrol activity. Similarly, endophytic Pseudomonas chlororaphis strains isolated from Chamaecytisus albus exhibited broad-spectrum antifungal activity against pathogens like Botrytis cinerea and Fusarium oxysporum when co-cultured, where metabolomic profiling (LC-MS/MS and GC-MS) detected phenazine derivatives (e.g., phenazine-1-carboxylic acid) and diketopiperazines as key antimicrobial metabolites, whose production was stimulated in competitive microbial environments [91]. These findings underscore the importance of designing multi-strain BCAs that leverage metabolic complementarity as the cooperative interaction between microbial strains in which each contributes distinct metabolic functions or produces different metabolites, resulting in enhanced overall activity. Future research should focus on mapping metabolic networks in co-cultures to identify key nodes for intervention, thereby improving the consistency and scalability of biocontrol strategies in agricultural settings. Overall, metabolomics provides a powerful framework to identify, quantify, and characterize microbial metabolites that promote plant health and suppress pathogens, enabling the development of targeted, environmentally friendly biocontrol strategies and supporting more sustainable agricultural practices.

6. Integrating Multi-Omics Data for Precision Biocontrol Strategies: Case Study

The bioprospecting pipeline for bacterial biocontrol agents (BCAs) integrates classical microbiological methods with advanced omics technologies to systematically discover, characterize, and optimize microbial strains for sustainable agriculture (Figure 5). This process begins with environmental sampling from diverse niches such as rhizospheres, endospheres, and extreme habitats, followed by microbial isolation and preliminary screening for antagonistic and plant growth-promoting traits [92,93].

Whole-genome sequencing provides insights into taxonomic identity, biosynthetic gene clusters (BGCs), and functional genes related to antimicrobial production, nutrient mobilization, and stress resilience [94]. Metagenomic approaches complement this by revealing the uncultured microbial diversity and their ecological functions within complex communities [95]. Transcriptomics further elucidates gene expression patterns under different environmental or biotic interactions, highlighting regulatory networks (e.g., quorum sensing or stress-response pathways) and metabolic pathways involved in biocontrol and host colonization [94]. Proteomics validates the translation of key genes into functional proteins, especially enzymes and secretion systems involved in pathogen inhibition and root colonization [96]. Metabolomics captures the downstream chemical phenotype, profiling bioactive metabolites such as lipopeptides, VOCs, and polyketides with antimicrobial properties [96]. Integrating genomic, transcriptomic, proteomic, metabolomic, and metagenomic data provides a comprehensive systems biology view of microbial function. This integrated perspective enables the rational design of effective microbial inoculants, including synthetic microbial communities (SynComs), and supports their formulation, regulatory approval, and field application. By combining multi-omics approaches, the bioprospecting pipeline not only speeds up the discovery of novel BCAs but also improves their consistency and performance under real-world field conditions.

A recent example illustrating the full bioprospecting pipeline (from isolation to field application) is Bacillus cabrialesii TE3^T [97]. The workflow for this strain involved a systematic series of steps, including ecological isolation, whole-genome sequencing, metabolic profiling, and functional validation of its in vitro biocontrol activity. Initially isolated from wheat plants cultivated in the Yaqui Valley, plant growth promotion was analyzed, along with hemolytic assays [92]. Initial greenhouse assays simulating Yaqui Valley conditions revealed that strain TE3^T significantly enhanced wheat chlorophyll content and tolerated multiple abiotic stresses, including high temperatures (>43 °C), drought, salinity, and phosphate limitation [98]. Furthermore, this strain underwent extensive genomic characterization, which revealed the presence of genes associated with key plant-beneficial traits and biocontrol potential [39]. Specifically, the genome encodes biosynthetic pathways for auxin production, siderophore-mediated iron acquisition, and tolerance to environmental stressors, traits that underscore its multifunctional potential in sustainable agriculture [39,97]. A pivotal component of the bioprospecting process involved the characterization of antifungal activity against Bipolaris sorokiniana TPQ3, a major necrotrophic pathogen affecting wheat [38,99]. Metabolomic analysis conducted via liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) identified a complex of bioactive lipopeptides, principally homologs of surfactin and fengycin, as the major antifungal constituents [38]. In vitro bioassays confirmed that the cell-free extract of B. cabrialesii TE3^T exerted potent antifungal effects, reducing mycelial growth of B. sorokiniana by up to 98%. On the other hand, agronomic field trials in the Yaqui Valley across multiple seasons showed that inoculation with TE3^T improved wheat grain yields by 11% to 19%, depending on environmental and fertilization conditions [100,101]. Moreover, the inoculation enhanced thousand kernel weight by up to 12% and increased grain protein content by approximately 6%, indicating improved grain quality. TE3^T also promoted nitrogen uptake efficiency (NUpE) by 15%, which enables a 20–30% reduction in synthetic nitrogen fertilizer use without yield penalties [37]. When applied as part of a native synthetic microbial consortium (SynCom), these effects were further reinforced, offering consistent performance across variable field conditions [37,38,101]. Collectively, these results highlight the multifunctionality and local adaptation of B. cabrialesii TE3^T, positioning it as a promising [37,99,100] bioinoculant for enhancing wheat productivity, sustainability, and resilience in the Yaqui Valley’s intensive agroecosystem.

The case of Bacillus cabrialesii TE3^T underscores the critical role that bioprospecting plays in advancing sustainable agriculture. By systematically identifying and characterizing beneficial microorganisms from agroecosystems like the Yaqui Valley, bioprospecting enables the development of biologically based inputs that align with ecological processes., From isolation and genomic elucidation to functional validation and multi-season field application, it illustrates how microbial resources can be harnessed to enhance crop productivity, reduce reliance on synthetic fertilizers and pesticides, and improve nutrient efficiency. As agricultural systems face increasing pressures from climate change, soil degradation, and input overuse, bioprospecting offers a strategic, science-based pathway to build more resilient, resource-efficient, and sustainable agricultural systems.

7. Conclusions

The integration of omics technologies into the bioprospecting pipeline has significantly advanced the discovery and application of BCAs, offering a sustainable and environmentally sound alternative to chemical crop protection. Through genomics, transcriptomics, proteomics, metabolomics, and metagenomics, researchers can now explore microbial diversity at an unprecedented depth, identify functional traits responsible for biocontrol and plant growth promotion, and develop more effective and tailored microbial formulations. This systems-level approach enables not only the identification of promising strains but also the design of synthetic microbial communities (SynComs) with synergistic effects and improved resilience in field conditions.

However, several challenges remain. First, the translation of laboratory and greenhouse results to field-scale efficacy is still inconsistent due to environmental variability, complex microbial interactions, and limited understanding of host–microbiome dynamics. Second, the commercial development of BCAs is constrained by stringent regulatory frameworks, high production costs, and limited strain registration. Third, the full potential of uncultivable microbes remains largely untapped, despite advancements in culture-independent approaches. Moreover, the integration of large-scale omics datasets poses bioinformatic and analytical challenges, requiring improved pipelines for data standardization, integration, and interpretation.

Looking ahead, future efforts should prioritize the development of robust multi-omics platforms capable of real-time environmental monitoring, the expansion of bioprospecting into underexplored ecosystems, and the implementation of machine learning tools to predict functional traits and ecological performance. A deeper understanding of plant–microbe–microbe interactions is essential for developing practical solutions to current and future agricultural challenges. By combining technological advances with ecological knowledge and regulatory frameworks, omics-guided bioprospecting can unlock the full potential of beneficial bacteria. This approach has the power to drive the transition toward more sustainable and resilient agricultural systems.