Engineering Synthetic Microbial Communities: Diversity and Applications in Soil for Plant Resilience

Abstract

Plants host a complex but taxonomically assembled set of microbes in their natural environment which confer several benefits to the host plant including stress resilience, nutrient acquisition and increased productivity. To understand and simplify the intricate interactions among these microbes, an innovative approach—Synthetic Microbial Community (SynCom)—is practiced, involving the intentional co-culturing of multiple microbial taxa under well-defined conditions mimicking natural microbiomes. SynComs hold promising solutions to the issues confronted by modern agriculture stemming from climate change, limited resources and land degradation. This review explores the potential of SynComs to enhance plant growth, development and disease resistance in agricultural settings. Despite the promising potential, the effectiveness of beneficial microbes in field applications has been inconsistent. Computational simulations, high-throughput sequencing and the utilization of omics databases can bridge the information gap, providing insights into the complex ecological and metabolic networks that govern plant–microbe interactions. Artificial intelligence-driven models can predict complex microbial interactions, while machine learning algorithms can analyze vast datasets to identify key microbial taxa and their functions. We also discuss the barriers to the implementation of these technologies in SynCom engineering. Future research should focus on these innovative applications to refine SynCom strategies, ultimately contributing to the advancement of green technologies in agriculture.

1. Introduction

Conventional agriculture faces significant challenges in maintaining soil fertility and ensuring sustainable food production for the exponentially growing global population. In the past few decades, an upsurge in food production has been achieved by pesticides and synthetic fertilizers, but on the other hand, the intensive and long-term use of these chemicals imposes severe environmental threats. Recent studies have indicated that these environmental constraints and the degradation of agricultural land could cause a 12% reduction in the world food supply over the next 25 years [1]. Furthermore, new legislative frameworks such as the European Green Deal demand major cutbacks in the usage of agrochemicals to guarantee the long-term survival of the environment. There is growing interest in tackling these issues in alternative ways such as biopesticides made from plant extracts, genetically modified plants, naturally occurring insect hormones and beneficial soil microorganisms [2].

Plants have diverse microbial communities either residing inside the plant tissues as endophytes or on the external surfaces of plants as epiphytes. The whole microbial component of the plant is referred to as the microbiome or microbiota. Among the plant’s microbiome, some species are found to be consistently associated with a given plant host across various conditions. This assembled subset of microbes, called the core microbiota, carries vital functional genes specifically for the host plant. Some strongly interacting members of this core microbiome form the hub microbiota, significantly affecting the makeup of microbial communities in plant hosts [3]. This core microbiota plays critical roles at all stages of plant development from seed germination to seed production [4,5]. Overall, all the soil microbiota are recruited by the plant itself via chemical communication through root exudates. The substantial allocation of approximately one-third of the total photosynthate as root exudates underscores the significance of this strategy [6,7]. Moreover, the composition of this biota varies depending on several factors such as pathogen infection, physiological age, genotype, immune system, nutritional status and the specific stage of the plant’s life cycle [8,9,10]. Plants can also attract transient microbes with varying compositions and abundances to help in the mitigation of environmental stresses [11]. Besides the plant–microbe interaction, microbe–microbe and microbe–environment social interactions are also prevalent in the environment [12]. Microbes’ social lives provide them with the benefits of cooperation and the resulting communities consequently exhibit advantages over single-strain microbial populations in terms of stability and functionality [13]. Yet, a better understanding of plant–microbe–environment social interactions is needed to effectively utilize microbes to improve plant resilience [14].

Contemporary agricultural methodologies predominantly employ specific strain inoculants to enhance plant growth and yield or alleviate environmental stress. These single-strain inoculants are screened either in vitro for the exhibition of plant growth-promoting (PGP) traits or through plant growth promotion experiments conducted under controlled greenhouse environments or in the field [15]. Although these approaches are widely practiced within the agricultural community, they do not sufficiently address the intrinsic complexity in plant–microbe and microbe–microbe interactions. Moreover, the beneficial effects of microbiota are frequently described as being provided by synergistic interactions between microbes. Recent research findings have highlighted the intricate and multifaceted nature of these interactions in natural complex microbial communities often called consortia. A microbial consortium refers to a naturally occurring or artificially assembled group of microorganisms that work together. These consortia are often structural and functionally complicated, with a diverse range of species and interactions. This complexity can make plants more resilient and effective in real-world applications but also more challenging to study, control and reproduce [16]. A fresh approach in agricultural research aiming at improving crop resilience and output is SynComs [17]. A SynCom is a deliberately assembled group of known microbial species. These communities are designed under controlled conditions to study specific interactions and functions. SynComs are often used in research to simplify and understand complex microbial interactions by using a defined set of microbes [18]. Generally, SynComs are less complex, with a specified number of species and controlled conditions. This simplicity allows for more precise studies and the manipulation of microbial interactions [19].

SynComs can increase plant growth, improve soil health and nutrient absorption and strengthen resistance against biotic and abiotic stresses [20]. New developments in SynCom technology have proven its capacity to transform the rhizosphere microbiome, thereby promoting sustainable agriculture practices. However, the intricacy of microbe–microbe interactions and fluctuating environmental circumstances makes it extremely difficult to translate SynCom applications from controlled laboratory settings to field conditions [21]. Despite these challenges, innovative approaches leveraging synthetic and systems biology tools have provided new opportunities to construct and optimize SynComs for diverse agricultural applications [19]. This paper aims to report SynCom technology’s current state as well as that of synthetic and systems biology tools to construct and promote this emerging application. We then assess the possible effects of this method on crop resilience to environmental stresses, the enhancement of plant development, biocontrol and disease suppression and decreasing ecological impacts connected to traditional agricultural methods. This review emphasizes the integration of machine learning (ML) and artificial intelligence (AI) in developing and implementing SynCom in agricultural settings. Finally, we discuss the current challenges that limit the efficiency of SynCom and propose strategies to enhance the competitiveness of bioinoculant-dependent biotechnologies in agricultural settings.

2. The Plant–Rhizosphere–Microbe Nexus: A Crucial Triangle

Plants, microbes and the rhizosphere show high interdependence in natural ecosystems and agroecosystems and their interactions form a complex and dynamic nexus often called the plant–rhizosphere–microbe triangle. Bacterial communities also serve as a crucial link between different groups of microorganisms (fungal, archaeal and protistan) in the rhizosphere community. The trophic relationships among these different phyla modulate the assembly and functioning of the rhizosphere soil microbiota [12]. The different vegetation types and soil edaphic factors, e.g., pH [22], soil organic carbon (SOC) and light-fraction organic carbon (LFOC), also affect this diversity and composition [23,24]. Research reveals that plants recruit some microbes across diverse ecological and soil-related circumstances to fulfill their needs [25]. For instance, in plants such as S. breviflora living at an elevation of 1350 m, actinobacteria are the predominant bacterial species. These bacteria can access water and scarce nutrients, enabling them to endure challenging environments. However, in the same plant species found at higher elevations, proteobacteria were the key bacterial group found in the rhizosphere (

Table 1. Different relationships between soil rhizospheric microbes and plants.

Microorganism	Plant Species	Region/Source	Possible Interaction	Reference
Pseudomonas khavaziana	Arabidopsis thaliana; Triticum aestivum	Wheat	Promote root growth and synthesize plant hormones	[30]
Bacillus subtilis; Paenibacillus polymyxa; Bacillus aryabhattai	Gossypium hirsutum	Cotton	Improves plant nutrient absorption	[31]
Sphingomonas azotifigens; Rhizobium deserti	Triticum aestivum	Wheat	Antagonism of pathogenic microorganisms	[32]
Rhizophagus irregularis	Lolium perenne	Maize	Improves water and nutrient absorption by plants	[33]
Mortierella alpine	Triticum aestivum	Wheat	Promotes water absorption and regulates water balance	[34]
Arbuscular mycorrhizal fungi (AMF)	Zea mays; Glycine max	Maize	Heavy metal detoxification	[35]

). These microbial communities, or consortia, are valuable in a variety of contexts [26,27]. Ultra-performance genotyping and bioinformatic approaches, such as co-occurrence network analysis and marker gene amplicon sequencing, have made it possible to identify the critical microbiomes of numerous crops grown in a variety of environmental circumstances. To create a dynamic functional community and enable further research, it is more efficient to focus on the core microbiota as opposed to the diverse natural microbiome [28]. The assemblage of a SynCom consisting of two or more microbial populations can overcome these restrictions. The transition from static monocultures to dynamic consortia ultimately hinges on the use of modern synthetic biology techniques and technology [29].

3. Synthetic Biology Tools and Approaches for Engineering SynComs

3.1. Conventional Approaches to Construct SynComs

Conventional approaches to effective SynCom construction are either top-down or bottom-up. The top-down approach creates SynComs similar to those of natural populations. It aims to include all the beneficial species, minimizing the chance of missing important ones. So, when utilizing this approach in designing SynComs, it is crucial to precisely assess the species diversity in the microbial community. This assessment subsequently helps us understand the functioning, structure and dynamism of the designed SynCom. On the other hand, the bottom-up method involves building a community from individual microbial species according to the desired functions of the SynCom [36]. In the following steps, the individual strains are assembled and SynComs are finely tuned by modifications and iterative testing to achieve the desired functions (Figure 1).

3.2. Experimental Techniques to Construct SynComs

SynComs are constructed using microbiological protocols initiating from the sampling, culturing, functional and genomic characterization and assembling of microbes in a specific proportion. Finally, the SynComs are tested and optimized in controlled laboratory conditions before being deployed in the field. In genetically modified SynComs, gene expression can be altered via genetic, transcriptional or translational modifications in the microbial genome [28]. However, the selection of microbial species to design a SynCom with desired traits beneficial for a variety of plant genotypes is a tedious task. While designing SynComs, it is crucial to consider the intricate dynamics observed in natural microbial populations. The balanced ratio of each microbial population in a SynCom could affect its functionality. For instance, in a plant root microbiome, if the population of Rhizobium (a nitrogen-fixing genus) is increased, it may outcompete the phosphorus-up-taking species of Azospirillum, leading to an imbalance in nutrients [37,38,39,40]. Task allocation is another important aspect that should be considered in assembling SynComs to ensure the division of labor. Based on previous research, B. subtilis could be assigned the role of promoting plant growth through the production of plant hormones, while P. fluorescens could defend plants against pathogens [41]. The other aspects that must be considered to regulate the performance of SynComs are niche complementarity and spatial organization. Different microbes within a plant’s root microbiome may thrive in distinct environmental niches. For instance, some microbes might prefer the oxygen-rich outer root zone, while others prefer the relatively low-oxygen conditions found deeper in the root. By occupying different ecological niches within the same plant, these microbes can coexist and enhance the plant’s health [42]. The spatial arrangement of microbes on the plant surface can significantly impact their interactions and efficiency. For example, placing Bacillus species near the root tips might allow them to better assist with nutrient uptake, while Trichoderma could be positioned in areas where it can prevent pathogens from entering the plant’s vascular system [43,44].

The binary association assay is a conventional experimental technique widely used to determine the pairwise association between microbial species in controlled environments. It provides insights into the metabolic compatibility and interactions between the tested microbial strains. These interactions can include cooperation, competition and signaling between different microbial species. These beneficial microbial interactions can protect plants against pathogens and improve growth under stress conditions [45]. Researchers have utilized the plant–bacterium binary association assay for the construction of a SynCom in phosphate-starved Arabidopsis thaliana plants. They found that the microbe–microbe interaction in the SynCom is the main contributor to phosphate accumulation in plants rather than the bacterial colonization in plant roots. These assays were found to successfully design a SynCom with predictable functions in a host plant. The work demonstrates that studying a limited number of bacterial combinations can infer the behavior of untested SynComs [43]. In the process of constructing artificial microbial communities, the strain compatibility stands as a critical issue. Researchers have devised sophisticated detection and regulation strategies to ensure the absence of inhibitory effects among strains, thereby achieving stable coexistence and efficient collaboration within the community. Notably, Zhepu Ruan [46] employed an innovative approach combining “top-down” and “bottom-up” methods. By first domesticating the initial microbial community and subsequently utilizing advanced metabolic modeling techniques to simulate the metabolic activities of different strain combinations, Ruan could accurately predict and validate optimal strain combinations, effectively mitigating inhibitory interactions among strains. Lin Wang [47] introduced a novel strategy of artificial spatial segregation, utilizing microencapsulation technology to isolate and encapsulate diverse microbial subgroups. This approach successfully achieved the stable construction and precise regulation of cross-species microbial communities. While this method cleverly addresses the coexistence challenges posed by competitive relationships among strains, it also maximizes the community’s overall functionality. These pivotal research advancements provide robust technical support for constructing synthetic microbial communities, significantly advancing the in-depth development and broad application of synthetic biology. In another study on Herbaspirillum frisingense, kChip, a microfluidic droplet-based physical device, was developed that allows SynComs to be automatically created with any combination of microbes from a list of species. kChip also has been found to characterize the phenotype of microbes across diverse ecological conditions. It is rapid and enables the parallel construction of 1,000,000 SynComs per day by utilizing the bottom-up approach. Using this device, SynComs for any desirable function can be produced such as biocontrol agent facilitation, the degradation of pollutants and the promotion of plant health [44].

3.3. Computational Models and Genomic Databases to Construct SynComs

Computational models are used to simulate and predict the outcomes of the above-mentioned techniques. These models integrate AI and ML approaches to optimize SynComs using dynamic patterns of microbial population and metabolic cross-feeding networks. A lot of plant beneficial features, e.g., effective colonization and useful metabolite production, are in strict accordance with gene markers. So, from this perspective, genomic studies are essential to find desired bacteria [48,49]. Moreover, researchers usually focus on several gene markers of plant-beneficial properties rather than focusing on a sole trait to develop more efficient SynComs. Traditional methods of genomic characterization are labor-intensive, costly and time-consuming. On the other hand, retrieving genomics-available datasets from a huge collection of databases is easier and more cost-effective than conventional genomic approaches. So, these computational approaches rely on genome and genome expression databases to identify useful microbes with favorable functional features [50]. For example, in genome-scale metabolic modeling (GEM), the assembled genomes of different microbes retrieved from databases are analyzed to predict the metabolic capabilities between microbes and their host plants. In a recent study, GEM was implemented in Campos Rupestres (grasslands) from 270 previously described microbial metagenome-assembled genomes deriving from the plant species Vellozia epidendroides and Barbacenia macrantha. The authors determined intricate metabolic interactions between microorganisms and their host plants via in silico approaches. The microbial species with complementary metabolic capabilities were selected and a minimal community (MinCom) was formed that was almost 4.5 (68 species) times smaller than the original (270 species). Despite its reduced size, this community retained essential plant growth-promoting traits, including nitrogen fixation, iron acquisition and phytohormone and exopolysaccharide production. GEM predicts metabolic pathways based on the genetic pathways of a microbe and how these pathways interact to influence the stability and function of the SynCom [51,52]. Conversely, another computational model, Bayesian (based on Bayes’ theorem), integrates prior knowledge with experimental data, making possible the quantification of a SynCom. In a study, scientists implemented a Bayesian model by taking strains (N), quorum sensing (A) and bacteriocin (B) as input units. Based on these input parameters, they tested all two- and three-strain systems and found the strongest options for generating stable steady-state communities [53]. Combining omics methods with conventional methods and deep insights into mechanisms at the host plant–microbial interface proved crucial for tailoring SynComs for enhanced beneficial effects in agriculture settings [54] (Figure 2). Moreover, high-throughput technologies like imaging-based phenotyping, spectroscopy and electrophysiological techniques may provide a powerful toolkit for the accurate and quantitative tracking of the growth rate, leaf area, chlorophyll content, water-use efficiency and other morpho-physiological attributes. Geospatial mapping and tracking using a global positioning system (GPS) have enabled researchers to robotically (automated data collection and analysis) monitor plant health and development throughout the plant’s whole life. GPS provides accurate coordinates within a plot for each single plant, facilitating the detailed mapping of plant phenotypes. Besides analysis, these approaches can also be used to establish the robust relationship between plant morpho-physiological attributes and plant performance metrics, including the yield, biomass and stress tolerance. By studying such correlations, the most suitable SynComs and the plant’s key attributes related to the desired outcomes can be evaluated [55,56,57].

4. Leveraging Multifunctional Microbes in SynComs

Microbes, being essential living elements of the lithosphere, are automatically incorporated into agricultural systems as soon as a seed enters soil for germination [58]. Plant growth-promoting rhizobacteria (PGPR) have been identified and used for plant growth promotion for many years. However, as a single inoculant, PGPR have several limitations in field trials aimed at achieving desirable goals.

To address these limitations, the development of SynCom requires us to consider PGPR’s multiple beneficial aspects rather than focusing on a single trait. To design effective SynComs, PGPR should be selected based on their beneficial traits for plant growth and their ability to thrive in various environments and colonize plant tissues. PGPR with nutrient acquisition properties such as N-fixation, P-solubilization, Fe-sequestration (by siderophore production) and synthesizing plant growth-promoting hormones and enzymes could be considered as a constituent of SynComs [59,60,61] (Table 2). Further PGPR key traits to focus on include the production of exometabolites, volatile organic compounds (VOCs) and the formation of strong biofilms [62]. Moreover, co-inoculation of bacteria with arbuscular mycorrhizal fungi (AMF) in a SynCom can create a synergistic effect, enhancing the overall benefits to plants. A study revealed that in maize roots, AMF interact with rhizospheric bacteria and promote the acquisition of P and K [63]. AMF and bacterial community interactions are also reported to enhance plant growth by increasing the root surface area through hyphal networks, improved nutrient uptake and water retention, especially during drought [64,65].

Given the dynamic nature of soil conditions, a successful inoculant must be able to compete with native bacteria, effectively colonize plant organs and establish stable and resilient relationships [66,67]. Field trials have also confirmed that conventional microbial selection techniques based merely on a single character are not adequate to produce desirable outputs. Therefore, it is necessary to include extra factors for microbial selection to guarantee that bacteria can produce the required phenotype in plants [48,67].

Table 2. Functional properties of the most commonly used SynCom candidates.

SynCom Candidates	Functional Traits	Reference
Arthrobacter sp.	Synthesis of IAA, which directly regulates plant growth and development	[68]
Enterobacter sp.	Release of IAA and ammonia, solubilizing phosphate as simple orthophosphate that plants can take up	[68]
Brevibacterium sp.	Release of ammonia, ultimately aiding healthy plant growth	[68]
Plantibacter sp.	Solubilizing phosphate as simple orthophosphate that plants can take up	[68]
Clostridium phytofermentans, Escherichia coli	Nutrient procurement through amino acid, organic acid, sugar and plant polymer catabolic pathways	[69,70]
Pseudomonas simiae WCS417r, Ralstonia sp. strain UNC404CL21Col and P. putida KT2440	Production of phytase to catalyze mineralization	[71]
Bacillus spp., Acinetobacter spp., Enterobacter sp., Xanthomonas sp. and Burkholderia sp.	Release of IAA, which directly regulates plant growth and development	[20]
PGPR strains	Release of ACC lowers ethylene levels	[72]
Azotobacter, Microbacterium, Bacillus, Burkholderia, Enterobacter, Flavbacterium, Erwinia, Rhizobium and Serratia	Solubilization of phosphate enhances plant growth and yield	[73]
Azotobacter chroococcum, Enterobacter agglomerans, P. putida, Bradyrhizobium japonicum, Cladosporium herbarum and Rhizobium leguminosarum	Microbial species in potato, tomato, wheat and radish solubilize phosphorus	[74]
Bacillus subtilis, Trichoderma harzianum, Trichoderma asperellum and Aspergillus sp.	Foster plant growth and development by producing a variety of enzymes and signaling molecules, including organic acids, proteases, plant hormones, volatile organic compounds (VOCs) and amino acids	[75]
Pseudomonas chlororaphis subsp. piscium PS5, Bacillus velezensis BN8.2, and Trichoderma virens T2C1.4.	Delayed Fusarium wilt in Banana disease progress over time, with significant reductions in incidence and severity	[76]
23 bacterial species, including Bacillus spp., Enterobacter spp., Pseudomonas spp., Serratia spp., and others, and 26 fungal species, including Acremonium spp., Aspergillus spp., Botryosporium sp., Cladosporium spp., Gibellulopsis spp., Penicillium spp., Trichoderma spp., Mortierella spp., and Wardomyces spp.	SynComs confer pronounced Fusarium wilt disease resistance to tomato plants compared to the controls during the entire growth period	[63]

Abbreviations: PGPR: plant growth-promoting rhizobacteria; IAA: indole acetic acid; ACC: 1-aminocyclopropane-1-carboxylase.

Table 2. Functional properties of the most commonly used SynCom candidates.

SynCom Candidates	Functional Traits	Reference
Arthrobacter sp.	Synthesis of IAA, which directly regulates plant growth and development	[68]
Enterobacter sp.	Release of IAA and ammonia, solubilizing phosphate as simple orthophosphate that plants can take up	[68]
Brevibacterium sp.	Release of ammonia, ultimately aiding healthy plant growth	[68]
Plantibacter sp.	Solubilizing phosphate as simple orthophosphate that plants can take up	[68]
Clostridium phytofermentans, Escherichia coli	Nutrient procurement through amino acid, organic acid, sugar and plant polymer catabolic pathways	[69,70]
Pseudomonas simiae WCS417r, Ralstonia sp. strain UNC404CL21Col and P. putida KT2440	Production of phytase to catalyze mineralization	[71]
Bacillus spp., Acinetobacter spp., Enterobacter sp., Xanthomonas sp. and Burkholderia sp.	Release of IAA, which directly regulates plant growth and development	[20]
PGPR strains	Release of ACC lowers ethylene levels	[72]
Azotobacter, Microbacterium, Bacillus, Burkholderia, Enterobacter, Flavbacterium, Erwinia, Rhizobium and Serratia	Solubilization of phosphate enhances plant growth and yield	[73]
Azotobacter chroococcum, Enterobacter agglomerans, P. putida, Bradyrhizobium japonicum, Cladosporium herbarum and Rhizobium leguminosarum	Microbial species in potato, tomato, wheat and radish solubilize phosphorus	[74]
Bacillus subtilis, Trichoderma harzianum, Trichoderma asperellum and Aspergillus sp.	Foster plant growth and development by producing a variety of enzymes and signaling molecules, including organic acids, proteases, plant hormones, volatile organic compounds (VOCs) and amino acids	[75]
Pseudomonas chlororaphis subsp. piscium PS5, Bacillus velezensis BN8.2, and Trichoderma virens T2C1.4.	Delayed Fusarium wilt in Banana disease progress over time, with significant reductions in incidence and severity	[76]
23 bacterial species, including Bacillus spp., Enterobacter spp., Pseudomonas spp., Serratia spp., and others, and 26 fungal species, including Acremonium spp., Aspergillus spp., Botryosporium sp., Cladosporium spp., Gibellulopsis spp., Penicillium spp., Trichoderma spp., Mortierella spp., and Wardomyces spp.	SynComs confer pronounced Fusarium wilt disease resistance to tomato plants compared to the controls during the entire growth period	[63]

Abbreviations: PGPR: plant growth-promoting rhizobacteria; IAA: indole acetic acid; ACC: 1-aminocyclopropane-1-carboxylase.

4.1. SynCom in Abiotic Stress Resilience

Microbial communities are widely used to enhance plant resistance to various abiotic stresses, as demonstrated by plant species thriving in extreme environments such as deserts. Plants ‘cry for help’ under abiotic stress, attracting helpful microbes from their surroundings, thus enhancing their development. Recently, several researchers have demonstrated the potential of SynComs to mitigate drought stress in maize [77], wheat [78] and rice [79]. SynComs alleviated the adverse effects of drought by regulating the turgor pressure of leaf and increasing the root surface area for more water uptake, thereby increasing plant biomass and yield. The implementation of SynComs rescued salinity-stressed tomato plants through the differential expression of salt-stress-related genes, osmotic regulation and ion accumulation in shoots. Moreover, microbes also balanced the production and scavenging of reactive oxygen species produced because of salinity stress [80]. Work in estuarine soils has studied the potential of a nodule-derived SynCom against several combined abiotic stresses such as HM, salinity, drought and temperature in Medicago sativa. The four-strain SynCom successfully alleviated all the stresses by some common mechanisms such as increases in antioxidant enzymes, the photosynthetic capacity, the dry weight, nodulation and the nitrogen content [81]. Under aluminum (Al) stress in an acidic soil, tolerant microbes assemble around the rhizosphere, improving the stress tolerance through the provision of essential nutrients and the modification of the root shape [82]. Microbiome profiling showed that bacterial members of an efficient SynCom may effectively inhabit plants and attract other rhizospheric beneficial bacteria by chemical signaling, hence boosting plant resilience against abiotic stresses. SynComs are designed and tested to understand these intricate interactions between microbes and plants in stressful environments (

Table 3. Effective SynComs and their mechanism of action in mitigating stress in plants.

Microbial Strains	Mechanism of Action	Plant Resistance	Reference
Deltaproteobacteria, Acidobacteria and Actinobacteria	Upregulate the synthesis of phytohormones involved in plant’s cell division and growth	Boost rice endurance in water-scarce conditions	[83]
P. pseudoalcaligenes and B. pumilus	Diminish caspase activity, malondialdehyde content and programmed cell death and increase antioxidant capacity	Salinity endurance of rice	[84]
Bacillales, Actinomycetales, Rhizobiales and Oceanospirillales	1-Aminocyclopropane-1-carboxycarboxylate (ACC) deaminase production under salt stress	Enhanced seed germination and root growth against salt stress in Oryza sativa	[74]
PGP bacteria	Associated with plant roots and producing some osmolytes (e.g., carbohydrates)	Alleviate osmotic stress	[85]
Kocuria erythromyxa EY43 and Staphylococcus kloosii EY37	Reduce the absorption of excess ions (sodium and chloride) from saline soils	Improve the growth of strawberry plants	[86]
Streptomyces species AMF and Bradyrhizobium	In dried soil, the diffusion pathways become reduced, leading to nutrient deficiency. Microbes must accumulate osmolytes inside their cells to lower the internal solute potential to avoid water loss to their environment	Resistant to drought stress	[87]
Pseudomonas putida UW4	ACC-deaminase-producing bacteria decrease ethylene levels. This enzyme regulates the protein profile, which plays a significant role in nutrient metabolism, defense stress and antioxidant activity	Enhanced growth of basil (Ocimum sanctum) under anoxic conditions	[88]
Pseudomonas cedrina, Brevundimonas terrae and Arthrobacter nicotianae	Release of enzymes and osmolyte accumulation	Have the potential to maintain plant health under low temperatures	[89]
M. alpina, E. nigrum	Together, these have a negative effect on the lateral roots and root hairs of wheat.	Leads to more sensitivity of wheat to drought stress	[34]
Trichoderma sp.	Induce plant systemic resistance against pathogens and pests. Produce multiple volatile compounds that mediate numerous activities	Improve plant growth and tolerance to abiotic stresses	[90]

) [77]. In summary, SynComs help plants to mitigate stress by increases in antioxidant enzyme activities and secreting hormones (mostly auxins and gibberellins) and other bioactive secondary metabolites [83].

4.2. SynCom in Soil Health

One of the main challenges to achieve agricultural sustainability is the deterioration of soil health. In agriculture, soil is considered healthy if it has a continued capacity as a vital ecosystem to sustain plant life, support biodiversity and provide essential services for agriculture, ensuring sustainable food production and environmental protection [91]. However, poor farming practices and the overuse of agrochemicals such as pesticides and fertilizers may degrade the soil and its proper functioning [92]. The potential of SynCom in this regard has already been exploited by many researchers. In a study, ammonium sulfate pollution in areas where rare earth elements are mined was significantly lowered (24.8%) by applying a SynCom consisting of ten bacterial strains. The strains in SynCom were isolated from the root microbiota of Miscanthus floridulus based on their nitrifying and denitrifying characteristics. A more drastic reduction (32.6%) in ammonium sulfate was seen when the same SynCom was inoculated into plant roots rather than into soil. The plants inoculated with SynComs successfully converted the excessive ammonium sulfate into nitrogen nutrients, thereby enhancing plant growth and decreasing soil pollution [93]. In other research, on alfalfa, the co-inoculation of Paenibacillus mucilaginosus and the metal-resistant rhizobium Sinorhizobium meliloti increased the Cu uptake in plant vegetative parts. An increase in the soil microbial biomass, enzymatic activities, total nitrogen, available phosphorus and soil organic matter was also recorded compared with the uninoculated control. Moreover, the nutrient (N, P and K) contents in plant tissues were increased and reactive oxygen species and lipid peroxidation were decreased by increasing the activities of antioxidant enzymes [94]. In the scientific field of pesticide biodegradation, SynComs are demonstrating remarkable potential and promise. Through meticulous design and optimization techniques, these microbial communities can precisely identify and degrade pesticide residues in soil, efficiently converting them into harmless or low-toxicity metabolites. Mohamed A. Fahmy [95], utilizing advanced metabolic pathway prediction technology, unveiled the complex mechanism underlying the decomposition of chlorantraniliprole and, based on this understanding, assembled a consortium comprising six highly active bacterial species. When introduced into soil contaminated with 20 mg/kg of chlorantraniliprole, this consortium achieved an impressive degradation rate of 99.33% within just 20 days, underscoring the exceptional efficacy of SynComs in pesticide degradation. Yunxiao Gao [96] conducted a thorough investigation into the correlation between microbial community composition and degradation efficiency, successfully screening for bacterial populations belonging to the Paracoccus and Achromobacter genera from in situ contaminated soil. These populations achieved a degradation rate of 98.55% for imidacloprid within 15 days, outperforming the degradation capabilities of individual bacterial strains. This discovery further confirms the unique advantages and immense potential of SynComs in pesticide degradation. This biodegradation process is not only crucial for restoring soil ecological balance and health but also provides an effective solution to mitigate the potential threats posed by pesticides to the natural environment and human health. As related research continues to deepen and broaden in scope, the application prospects for SynComs in the field of pesticide microbial degradation will become even brighter, promising to inject powerful biotechnological momentum and safeguards into the sustainable development of agriculture. SynComs also showed a major impact on the soil physico-chemical properties, composition of soil microbiota and, hence, plant vigor and health [97]. A significant improvement in soil aggregate formation has been observed in response to microbial secreted extracellular polymeric compounds (EPS) [98]. In essence, SynComs can mitigate soil pollution through the genetic regulation of enzymes linked to the detoxification of harmful pollutants (

Table 4. Effective SynComs and their mechanism of action in mitigating soil pollution.

SynCom Composition	Pollutant	Nature	Mechanism/Enzymes	Reference
Acinetobacter and Pseudomonas spp.	Hydrocarbons	Organic	The release of hydroxylase and dioxygenase enzymes leads to the degradation of aromatic hydrocarbons	[100]
Lactococcus lactis and Kluyveromyces marxianus	Ni, Cu, Cd and Pb	Inorganic	Biosorption and reduction leads to removal of pollutants	[101]
Pseudomonas sp., Achromobacter sp., Delftia sp., Enterobacter sp., Advenella sp., Flavobacterium sp., Duganella sp., Stenotrophomonas sp., Ochrobactrum sp., Phyllobacterium sp., Comamonas sp., Oerskovia sp. and Rhizobium sp.	Cd	Inorganic	Increased cytoplasmic invertase, vacuolar invertase, hexokinase, phosphoglucoisomerase, glucose 6-phosphate dehydrogenase, phosphofructokinase; maintained ROS balance; downregulation of HM-related genes	[102]
Comamonas sp. and Alicycliphilus sp.	Herbicide swep	Organic	Degradation by amidase	[103]
Mycobacterium sp., Novosphingobium pentaromativorans and Bacillus sp.	Pyrene	Organic	Degradation by pyrene-degrading enzymes	[104]
B. flexus, Proteus mirabilis and Pseudomonas aeruginosa	2-Naphthol indanthrene blue RS dye	Organic	Degradation by lignin peroxidase, laccase, tyrosinase and NADH–DCIP reductase	[105]
Microbacterium, Pseudomonas, Streptomyces, Arthrobacter and Rhodococcus	Polycyclic aromatic hydrocarbons (PAHs)	Organic	Degradation by dioxygenases and monooxygenases	[106]
B. subtilis and B. safensis	Cr, Zn, Pb, Cd and Ni	Inorganic	Adsorption and reduction resulting in HM bioremediation	[107]
Oudemansiella radicata and Serratia marcescens	Fluoranthene and Pb	Organic/inorganic	Bioaccumulation/microbial ligninolytic enzymes (laccase and MnP), and soil enzymes (dehydrogenase and acid phosphatase), leading to degradation of pollutants	[108]
Funneliformis mosseae and Enterobacter sp. EG16	Cd	Inorganic	Biosorption, chelation and bioaccumulation	[108]
Mycobacterium spp. Novosphingobium pentaromativorans and Bacillus sp.	Benzopyrene	Organic	Fluoranthene dioxygenase and putative 9-fluorenone-1-carboxylic acid dioxygenase	[104]
Fusarium proliferatum	Naphthalene	Organic	Dioxygenase	[109]
Comamonas sp. and Alicycliphilus sp.	Carbamate	Organic	Amidase	[93]
Staphylococcus warneri, P. putida and Stenotrophomonas maltophilia	Chlorpyrifos	Organic	Organophosphorus hydrolase	[110]
Paenibacillus spp.	Di-n-butyl phthalate	Organic	3,4-Phthalate dioxygenase and carboxyesterase	[111]

Abbreviations: Ni: nickel; Cu: copper; Pb: lead; Cd: cadmium; Zn: zinc; Cr: chromium.

) [99].

4.3. SynComs in Biocontrol and Disease Suppression

The plant’s resistance to pathogens depends heavily on the symbiotic link between plants and their microbiota [112]. Plants in their native microbiota preferentially ‘choose’ bacteria with disease-resistant potential. Microbes can trigger induced systemic resistance (ISR)—an immunological reaction of the plant. Moreover, microbes can also limit the growth and colonization of pathogens by competing for space and resources and building a healthy microbial community surrounding the plant’s roots. These strategies improve the plant’s defense ability against a wide range of pathogens [113].

While designing a SynCom for biocontrol potential, strains within a SynCom are carefully selected to achieve a desired outcome. Some strains are primarily selected for nutrition acquisition, while others might specialize in controlling disease and enhancing general plant health [114] (Table 5). For example, some strains within the communities exhibit direct antagonism against specific pathogens (for which the SynCom is designed) by producing antimicrobial bioactive substances, therefore stopping the onset of diseases [20], while the other strains may perform indirectly by competing for resources and thus limiting the pathogen’s spread, improving plant nutrient absorption and regulating phytohormone levels. These mechanisms may promote plant growth and development, in turn reducing the disease susceptibility [115]. In addition, the formation of a biofilm on the plant surface by some members of the SynCom creates a physical barrier to pathogens, inhibiting their entry into plant tissues [116]. Moreover, enzymes such as chitinases produced by different microbes can degrade fungal cell walls, leading to fungal cell death. The breakdown products of chitin, chitooligosaccharides, can act as elicitors that trigger the plant’s immune responses. This includes the activation of systemic acquired resistance (SAR), which enhances the plant’s overall defense against a broad spectrum of pathogens [117]. The alteration of soil physiochemical properties may make it less favorable for pathogen growth—another strategy adopted by SynComs [118].

Though SynComs have potential to combat diseases, their actual use in agriculture presents various difficulties. For example, the biocontrol efficiency of a SynCom depends on the synergistic interactions among its constituent strains. Furthermore, environmental factors can greatly affect SynComs’ performance; therefore, their efficacy in the field might not necessarily coincide with the findings from controlled laboratory or greenhouse environments. More field-based studies are thus required to maximize their efficacy and guarantee their safe integration into current agricultural methods [76,119].

Table 5. Pathogenic fungus suppression in host plants by use of SynComs.

Plant	SynCom	Source	Pathogen	Reference
Astragalus mongholicus	2 SynComs 13 bacterial strains 4 bacterial strains	diseased plant roots	Fusarium oxysporum	[113]
Zea mays	6 different SynComs composed of Bacillus strains	roots and leaves	Rhizoctonia solani	[120]
Nicotiana attenuata	6 bacterial strains	rhizospheric soil	Fusarium–Alternaria disease	[121]
Lycopersicum esculentum	Bacteria and fungi 4:1	rhizospheric soil	Fusarium	[63]
Solanum lycopersicum	Many SynComs were tested, composed of 205 strains	rhizospheric soil	Fusarium oxysporum f. sp. lycopersici (FOL)	[63]
Gossypium spp.	4 Bacillus strains	rhizospheric soil	Verticillium	[122]
Solanum tuberosum	18 SynComs were tested	healthy leaf tissue	Fusarium solani	[123]
Lycopersicum esculentum	Flavobacteriaceae sp. TRM1	rhizospheric soil	Ralstonia solanacearum	[117]
Musa paradisiaca	SynCom 1.0, 1.1 and 1.2 composed of 44, 11 and 03 isolates, respectively	rhizospheric soil	Fusarium oxysporum	[76]
Triticum aestivum	7 SynComs composed of 14 strains in different ratios	rhizospheric soil	Rhizoctonia solani AG8	[124]
Arachis hypogaea	Seed-borne bacterial strains	seed	Aspergillus flavus and Fusarium oxysporum	[125]
Arabidopsis thaliana	SynCom of 5-bacterial-strain	rhizospheric soil	Pseudomonas syringae	[52]
Beta vulgaris	2-strain SynCom, i.e., Chitinophaga and Flavobacterium	plant roots	Rhizoctonia solani	[126]
Cucumis sativus	10 strains (Pseudomonas, Bacillus, Stenotrophomonas and Bacillus spp.)	rhizospheric soil	Phytophthora capsici	[127]
Soybean	Pseudomonas protegens and Lysobacter enzymogenes via T4ASS	rhizospheric soil	Rhizoctonia solani	[128]

Table 5. Pathogenic fungus suppression in host plants by use of SynComs.

Plant	SynCom	Source	Pathogen	Reference
Astragalus mongholicus	2 SynComs 13 bacterial strains 4 bacterial strains	diseased plant roots	Fusarium oxysporum	[113]
Zea mays	6 different SynComs composed of Bacillus strains	roots and leaves	Rhizoctonia solani	[120]
Nicotiana attenuata	6 bacterial strains	rhizospheric soil	Fusarium–Alternaria disease	[121]
Lycopersicum esculentum	Bacteria and fungi 4:1	rhizospheric soil	Fusarium	[63]
Solanum lycopersicum	Many SynComs were tested, composed of 205 strains	rhizospheric soil	Fusarium oxysporum f. sp. lycopersici (FOL)	[63]
Gossypium spp.	4 Bacillus strains	rhizospheric soil	Verticillium	[122]
Solanum tuberosum	18 SynComs were tested	healthy leaf tissue	Fusarium solani	[123]
Lycopersicum esculentum	Flavobacteriaceae sp. TRM1	rhizospheric soil	Ralstonia solanacearum	[117]
Musa paradisiaca	SynCom 1.0, 1.1 and 1.2 composed of 44, 11 and 03 isolates, respectively	rhizospheric soil	Fusarium oxysporum	[76]
Triticum aestivum	7 SynComs composed of 14 strains in different ratios	rhizospheric soil	Rhizoctonia solani AG8	[124]
Arachis hypogaea	Seed-borne bacterial strains	seed	Aspergillus flavus and Fusarium oxysporum	[125]
Arabidopsis thaliana	SynCom of 5-bacterial-strain	rhizospheric soil	Pseudomonas syringae	[52]
Beta vulgaris	2-strain SynCom, i.e., Chitinophaga and Flavobacterium	plant roots	Rhizoctonia solani	[126]
Cucumis sativus	10 strains (Pseudomonas, Bacillus, Stenotrophomonas and Bacillus spp.)	rhizospheric soil	Phytophthora capsici	[127]
Soybean	Pseudomonas protegens and Lysobacter enzymogenes via T4ASS	rhizospheric soil	Rhizoctonia solani	[128]

5. Challenges and Information Gaps—SynComs in Sustainable Agriculture

Plant–microbe interactions are quite intricate and several elements control community assembly and functioning. The dynamic interactions of SynComs suggest that it would be valuable to move from controlled conditions to natural field environments. This shift would enhance our understanding of the complex interactions between organisms (bacteria, phages, fungi and protozoa) and physio-chemical factors (pH, electrical conductivity and nutrient bioavailability) [54]. Many developed microbial inoculants have shown poor effectiveness in the field. Beyond implementation loopholes, there are several major challenges to constructing effective SynComs.

Strain selection is the most crucial and initial point for SynCom creation. While screening strains for SynCom, one should concentrate on interactions between microbes that may lead to strain dropouts and gene knockouts, hence changing the stability and function [129]. The microbe selection must also be based on various functional characteristics to incorporate into one multipurpose SynCom. Further challenges confronted during SynCom construction are the order of microbe inoculation during SynCom assembly and the determination of a balanced ratio based on the growth rate of the microbes [130]. While using SynComs to study interactions, one must keep in mind the complex nature of microbes, as their behavior may vary across diverse environmental conditions. We emphasize the need to track the community composition and structural integrity of SynComs using next-generation sequencing, RT–PCR data or fluorescence tags during several phases of assembly and implementation [131,132].

The integration of AI and ML approaches offer the promise of revolutionizing SynCom construction, but several challenges are also faced in their implementation. Firstly, the available datasets regarding microbial interactions are either insufficient or biased. Secondly, variations in microbial experiments hinder the collection of standardized and reproducible data. Both these issues hamper the construction of competitive computational models. Additionally, numerous AI models based on deep learning are difficult to interpret and comprehend [133]. A lack of standards or criteria by which to evaluate model efficacy further complicates efficient SynCom construction. All these issues can be overcome to some extent by consistent methods for data collection, developing benchmark datasets to assess model efficacy and improving the AI model interpretability. Moreover, the execution of pilot tests prior to extensive implementation and the use of omics and synthetic biology in assessing the SynCom efficacy is also advisable [130]. Ultimately, a paramount step towards advancing SynCom research hinges on collaborative efforts between microbiologists and AI experts to translate theoretical predictions into real-world outcomes [129].

6. Conclusions

Plant microbiome research has been revolutionized through multidisciplinary approaches such as meta-omics, bioengineering, bioinformatics and theoretical and experimental biology. These efforts have produced significant insights into plant–microbe relationships. SynCom engineering is the major witness of this advancement. In future, potential SynComs could be deployed in agriculture settings to maintain soil health, boosting tolerance to abiotic stresses and the suppression of phytopathogens. Here, we have described several methodological and technological obstacles to the construction and implementation of SynComs. These hinderances can be resolved by multiscale computational simulations with refined AI and ML models and the utilization of multi-omics databases. Emphasis should be placed on implementing computational models in field trials rather than in controlled environments to enhance the stability and effectiveness of SynComs. Furthermore, customized SynCom designs that take into account geographical and environmental conditions are more likely to improve plant health and productivity. It can also be anticipated that engineered SynComs can be reliably and efficiently implemented in large-scale field settings while ensuring safety, leading to more sustainable and improved outcomes.