Agricultural production must increase by about 70% from its current level by 2050 to meet the demand for a growing population (ELD Initiative, 2015; Singh et al., 2020). However, current studies estimate that global food production will decrease by 12% over the next 25 years due to the degradation of agricultural lands (ELD Initiative, 2015). After the second industrial revolution, traditional agricultural practices shifted towards the use of synthetic chemical fertilizers and pesticides to improve crop production (Melillo, 2012; Dixon, 2018). The intensive use of these agrochemicals has led to the deterioration of the quality of both the soil as well as the environment (Meena V. S. et al., 2017). A possible solution to mitigate some of these problems might be the development of sustainable agriculture practices that harness crop-associated microbiomes to either increase or sustain higher yields while maintaining overall soil health and fertility (Toju et al., 2018; Singh et al., 2020).

Regardless of whether animals or plants are considered, microbial communities play vital roles in their respective ecosystems. The soil microbiome is defined as the microbial communities present in the soil and their encoded functions. Within the soil microbes can be found as both free-living or in symbiotic relationships with higher organisms (Banerjee et al., 2018), and are often considered key drivers of beneficial processes such as nutrient cycling and carbon sequestration (Fierer, 2017; Wallenstein, 2017; Qiu et al., 2019). Microorganisms that can form complex co-associations with plants obtain their carbon sources and other metabolites from the plant while performing these beneficial processes (Backer et al., 2018; Trivedi et al., 2020).

With up to 20-40% of a plant’s photosynthate becoming root exudate (Lynch and Whipps, 1990), it is not surprising that plants encourage microbial growth, and that changes in the exudation components can modify the composition of the associated microbial community (Wallenstein, 2017; Vives-Peris et al., 2020). Although many microorganisms can respond to plant exudates, it is becoming clear that plants harbor a specific subset of microorganisms, termed the core microbiome, that is consistently associated with a particular plant host across a wide range of environments (Toju et al., 2018; Walters et al., 2018). The core microbiome has been shown to provide several functional benefits to plants that include, but are not limited to, enhancing plant mineral nutrient uptake, and suppressing soil borne diseases (Lemanceau et al., 2017a; Banerjee et al., 2018; Singh et al., 2020). Additionally, it has also been observed that plants can recruit transient microbes that vary in composition and abundance to alleviate environmental stress (Berg et al., 2020).

Over the past two decades, microbes with plant growth-promoting (PGP) traits have been isolated and used as inoculants to improve crop production (Finkel et al., 2017; Banerjee et al., 2018; de Souza et al., 2020). Microbes assist plant growth either by enhancing nutrient acquisition such as nitrogen fixation, phosphorus solubilization, and siderophore production or producing plant growth promoting substances (Olanrewaju et al., 2017; Saleem et al., 2018; Chaudhary et al., 2021; Joshi et al., 2021). In addition, microbial inoculants have the potential to suppress several pathogenic organisms (Yasmin et al., 2016; Olanrewaju et al., 2017; Abbasi et al., 2021). The main drawback of microbial application is that it often fails to yield consistent results because the plant-microbe association has not been considered with respect to various biotic and abiotic stress conditions that can affect the outcome (Finkel et al., 2017; de Souza et al., 2020; Hawkins & Oresnik, 2022). For inoculums to be successful in the field, an in-depth knowledge of microbial abundance, diversity, as well as plant-microbe interactions, is essential to be able to predict overall functionality (Chodkowski and Shade, 2017).

The synthetic community (SynCom) approach is an emerging research field that incorporates a synthetic biology approach that is coupled with the knowledge that has been generated from microbial community analysis, metagenomic, and bioinformatic approaches that have become more accessible with the advent of Next Generation Sequencing technologies. Understanding the dynamic interactions within microbial ecosystems is useful to engineer microbial consortia with robust, stable, and predictable behaviours (McCarty and Ledesma-Amaro, 2019).

Briefly, SynComs are constructed by co-culturing multiple taxa under well-defined conditions to mimic the structure and function of a microbiome. The underlying principle is to reduce the complexity of the original microbial community, while still preserving some of the essential interactions between the microbes and their hosts (Vorholt et al., 2017; Kaminsky et al., 2019; de Souza et al., 2020). The goal is to facilitate an increase in community stability through synergistic interactions between its members (de Souza et al., 2020). Several studies have been reported that SynCom application enhanced plant growth under greenhouse conditions (Armanhi et al., 2021; Chai et al., 2021; Lee et al., 2021) as well as field conditions (Santhanam et al., 2015; Wang et al., 2021).

Advancements in high-throughput sequencing technologies and their associated bioinformatics tools has provided the opportunity to discover the complexities associated with plant-microbe interactions and the functionality they can provide to the plant. The aim of this review is to encapsulate factors which can play contributing roles to the outcome of an engineered plant-microbe interaction, the current state of SynCom technology, and to consider whether this type of approach has the ability to affect crop production.

The interaction of microbes with plants occurs across their entire life cycle. These interactions can occur both above as well as below ground level. Whereas some are due to chance, many interactions are orchestrated by the plant. It can occur through vertical transfer, such as when endophytes living within a plant are transmitted via vascular connections or when bacteria become incorporated within a developing seed and may play a role in seed germination and the development of a root system to aid in initial establishment and plant survival (Mitter et al., 2016). In addition, plant roots interact with the soil and actively exude carbon containing compounds that influences all microbial growth around a root. Bacteria drawn to the plant in this manner are horizontally transferred from bulk soil to the rhizosphere.

The rhizosphere is defined as the soil under direct influence of root exudates (Moe, 2013; Reinhold-Hurek et al., 2015; Hartman and Tringe, 2019). This zone has been further subdivided into the endorhizosphere, the ectorhizosphere, and the rhizoplane ( Figure 1 ). The endorhizosphere consists of the zone of tissue in the plant root that can be occupied by microorganisms (McNear, 2013). The endorhizoshpere is delineated by the rhizoplane, which is the surface of the root, and beyond this is the ectorhizosphere which is influenced by root exudation and rhizodeposition (Reinhold-Hurek et al., 2015).

The bulk soil microbiome acts as a potential source of inoculants for the rhizosphere microbiome. Composition of the rhizosphere microbiome is structured differently from the soil microbiome (Crecchio et al., 2018). This differentiation is initiated by plants through root exudates that attract specific microbes to the rhizosphere to support plant growth and development (Wallenstein, 2017; Vives-Peris et al., 2020). By regulating the secretion of signaling compounds and activation of plant immune responses, the plant can influence the recruitment of a subset of microbes from the rhizosphere to attach to the rhizoplane and subsequently to move from the rhizoplane to the endorhizosphere (Hacquard et al., 2015; Hartman and Tringe, 2019). In general, it has been observed that plants modify their rhizosphere to attract organisms that have beneficial traits such as plant growth promotion, solubilization of nutrients, and inhibition of pathogen growth (Andreote et al., 2014).

When compared to bulk soil, the rhizosphere microbiome has a richer, and functionally, better-characterized microbiome. Bulgarelli et al. (2015) reported that the rhizosphere and root microbiomes of barley differentiated from the soil microbiomes as a gradient. The soil microbiomes showed higher bacterial richness and diversity compared with root samples, while the rhizosphere microbiota composition was intermediate between soil and root samples. Similarly, higher microbial richness was reported in the bulk soil surrounding the rhizosphere soil of maize (Walters et al., 2018). Additionally, it was observed that the rhizosphere microbial communities had greater network connectivity than the bulk soil in maize and wild oat (Peiffer et al., 2013; Shi et al., 2016; Walters et al., 2018). Collectively this suggests that roots can promote the development of niches with dominant taxa that favor greater interactions and more complex co-occurrence patterns over time.

Defining the major drivers for microbial diversity is a challenging task since plant-microbe interactions form a complex relationship. Several factors influence the composition of the microbial communities such as plant, microbe-microbe interaction, and edaphic factors. These factors influence the selection of microbes primarily through root exudates. Root exudates consist of a variety of chemicals, primary metabolites, and secondary metabolites (Rasmann and Turlings, 2016; Tsunoda and van Dam, 2017; Vives-Peris et al., 2020). Primary metabolites, such as the labile carbon of root exudates, increase the growth of fast-growing microorganisms with higher nutritional requirements enabling them to outcompete slow-growing microorganisms with lower nutritional requirements (Terrazas et al., 2016). Several studies indicate that bacteria belonging to the phylum Proteobacteria, which are known to respond to labile carbon (Peiffer et al., 2013), are enriched in the rhizosphere compared to bulk soils (Aira et al., 2010; Chauhan et al., 2011; Lundberg et al., 2012; Peiffer et al., 2013; Zhang et al., 2020). Likewise, secondary metabolites trigger varying responses in organisms. Flavonoids, for example, attract symbionts in nodule formation, stimulate mycorrhizal spore germination and hyphal branching, and influence quorum sensing in legumes (Philippot et al., 2013). By regulating the composition of root exudates, the microbial diversity in the plant ecosystem can be substantially altered.

Plant factors consist of the plant species, genotype, immune system, physiological age, nutritional status, and pathogen infection (Hawkes et al., 2007; van Overbeek and Elsas, 2008; Sharma and Verma, 2018; Zhalnina et al., 2018; Vives-Peris et al., 2020). Plant species strongly influences the structure of rhizosphere communities through differences in root morphology and exudation of different metabolites (Philippot et al., 2013). Colonization of different bacterial populations due to root exudates was observed in the rhizosphere of four plant species – wheat, maize, rape, and barrel clover (Haichar et al., 2008). Similarly, activity and dynamics of the indigenous Pseudomonas spp. In the rhizosphere were significantly influenced by host plant species (Bergsma-Vlami et al., 2005). Enrichment of antifungal microbial communities was reported in barley rhizosphere after the infection of Fusarium graminearum (Dudenhoffer et al., 2016). A high rate of nitrogen application increased the relative abundances of ammonia-oxidizing and denitrifying bacterial communities in maize rhizosphere (Zhu et al., 2016). It has also been reported that plant genotypes in A. thaliana (Micallef et al., 2009), Solanum tuberosum (Inceoglu et al., 2010), grapevine (Berlanas et al., 2019), and Zea mays (Aira et al., 2010) influence the production of root exudates thereby changing their microbial communities. Aira et al. reported that the rhizosphere microbial communities of two maize hybrids were strongly influenced by plant genotype (Aira et al., 2010). In contrast, a large-scale longitudinal study conducted in five fields with 27 maize inbred lines reported that plant age was the strongest factor shaping the rhizosphere microbial community followed by location and genotype (Walters et al., 2018). However, within a given field, plant genotype significantly influenced the richness of the microbiome (Peiffer et al., 2013). A study focused on the sugarcane microbiome under field conditions demonstrated that microbial communities were primarily influenced by the plant compartments followed by the growing region, the age and variety of the crop (Hamonts et al., 2018). The influence of plant factors on the composition of microbes is obvious under the same environmental conditions.

In addition to host-microbe associations, microbe-microbe interactions also affect the structure of microbial communities in the rhizosphere (Bulgarelli et al., 2015). There are a wide range of microbe-microbe interactions ranging from synergistic to antagonistic which could shape the composition of the plant microbiota (Hacquard et al., 2015; Terrazas et al., 2016). Soil microbes can also affect the root exudation process by consuming primary root exudates or releasing secondary compounds to stimulate specific metabolite production (Canarini et al., 2019). Specific microbial taxa on tomato rhizosphere were found to modify the chemical composition of root exudates, for example acylsucrose exudation was induced by Bacillus subtilis (Korenblum et al., 2020). Further, microbial interactions assist the host plant to mitigate several abiotic stresses through direct antagonization against pathogens or induction of systemic resistance by priming plants (Meena K. K. et al., 2017; Arif et al., 2020). Several microbes secret an enzyme, 1-aminocyclopropane-1-carboxylate (ACC) deaminase, which regulates the level of stress hormone ethylene in the plant. Strains of Arthrobacter spp., Bacillus spp., and Pseudomonas spp. Have been reported to enhance plant growth through the production of ACC deaminase (Compant et al., 2019). This indicates the bi-directional relationship between plants and their microbial communities.

Edaphic factors such as pH, soil type, indigenous microflora, oxygen, nutrient, and light availability (Hacquard et al., 2015; Kaul et al., 2018) exert considerable impact on the developmental stage and physiological status of the host plant (Hacquard et al., 2015). A recent plant phytometer study with six plant species, across diverse edaphic conditions and land use gradient, indicates that indigenous soil microflora were the direct drivers of active bacterial communities (Vieira et al., 2020). The composition of the rhizosphere microbiome was strongly dictated by soil texture, water content, and soil type instead of plant properties and root exudates (Vieira et al., 2020). Another phytometer study was conducted on clonal oak saplings (Quercus robur L., clone DF159) under different field sites with similar climatic conditions. This study revealed that the effect of environmental factors was greater than the plant effect in shaping soil microbial communities. Similar microbial compositions were observed in sites with comparable pH, soil organic carbon, and C/N ratios (Habiyaremye et al., 2020). In contrast, similar rhizosphere communities were reported in three different fields having distinct physiochemical properties (Peiffer et al., 2013). Thus, plant-soil-microbe interaction is highly complex and their effect on the composition of the microbial community is determined by the interaction between them rather than each factor alone.

A core microbiome is comprised of microbes that are recruited by a plant regardless of the environment ( Figure 2 ). These core microbiomes contain key microbial taxa carrying essential functional genes for the plant host (Bergsma-Vlami et al., 2005; Hacquard et al., 2015; Vandenkoornhuyse et al., 2015; Astudillo-García et al., 2017; Naylor et al., 2017; Toju et al., 2018; Berg et al., 2020; Trivedi et al., 2020). The functional redundancy of microbes i.e., the coexistence of multiple taxa performing a particular biochemical function, allows for environmental variation without comprising plant host fitness (Lemanceau et al., 2017b; Louca et al., 2018). In addition, the network of interactions between organisms provides a buffer against disturbance by recruiting different microbial combinations to fulfil specific functions (Konopka et al., 2015). A dynamic functional community can be formed by focusing on the core microbiome instead of the highly complex native microbiota for further studies (Ramirez-Villacis et al., 2020; Durán et al., 2021).

Recent advances in high throughput sequencing and bioinformatic tools have enabled the discovery of the core microbiomes of different crops. Marker gene amplicon sequencing has been widely used to study microbial association with different plant parts over a range of environmental conditions (Hacquard et al., 2015; Durán et al., 2021). Through co-occurrence network analysis of the resulting genomic data, it is possible to identify a core microbiome. It also explores the positive or negative relationship between members based on their occurrence or abundance (Rodriguez et al., 2019; Berg et al., 2020; Xue et al., 2022). Further, the positions of microbes in the network can indicate their importance within the microbial community. Highly interactive members of the core microbiome, which are called “hub” microbes, have been shown to have a strong influence in shaping the microbial communities of plant hosts (Agler et al., 2016; Muller et al., 2018; Trivedi et al., 2020).

Several studies have reported the taxonomy of core microbiomes in different crops ( Table 1 ). Walters et al. (2018) found that seven bacterial operational taxonomic units (OTUs) were observed consistently in the maize rhizosphere at different ages and field conditions. All seven OTUs were taxonomically assigned to the phylum Proteobacteria with differences at the genus level. Likewise, the core microbiome of the citrus rhizosphere was identified through an extensive study of soil samples from twenty-three locations in eight citrus-producing countries across six continents (Xu et al., 2018). These studies show that the core microbiome can select for key members of the microbial community that can be screened in vitro for microbe-microbe interactions and putative functions (Lebeis, 2014).

Overall, there is general agreement in the literature that organisms do have strong associations with certain microbes. Many studies carry out their analysis at the genus level which does give a descriptive analysis of what organisms can be present. In some cases, more functional metatranscriptomic studies can provide more insight into which species are present as well as what genes are being expressed under a given set of conditions. Together these data are helping to develop hypotheses of how microbes might be affecting plant responses and are allowing work to be designed to ask key ecological questions regarding plant microbe interactions to be asked more directly.

Numerous studies have reported that beneficial microbes can be effectively used as inoculants for agricultural production since the 19^th century (Bhattacharjee et al., 2008; Bhattacharyya et al., 2016; Mitter et al., 2016; Alori and Oluranti Babalola, 2018; Kaminsky et al., 2019; Qiu et al., 2019). Rhizobia-legume symbiosis and arbuscular mycorrhizal associations are examples of well-studied plant-microbe relationships that have been successfully used in agriculture (Bhattacharyya et al., 2016). Conventionally, beneficial microbes are selected based on in vitro screening for specific taxa with one or more PGP traits, such as nitrogen fixation, phosphorus solubilization, production of growth-regulating hormones, etc., with limited assessment under controlled environmental conditions (Glick, 2012; Choi et al., 2021).

Inconsistent production under field conditions is a major problem as inoculants often fail to compete with indigenous soil microbes under different climatic conditions, soil type and other environmental factors (Finkel et al., 2017; Baliyan et al., 2018; Qiu et al., 2019; de Souza et al., 2020). A successful inoculant must be able to compete with other microbes, efficiently colonize, and establish a stable association with plants throughout the growing season (Vessey, 2003; de Souza et al., 2020). Therefore, it is not surprising that current inoculants, which are formulated with pure isolates, can have problems with effectiveness. A SynCom could be a great alternative to overcome the problems associated with conventional inoculants as it can incorporate different microbial communities that can partly mimic the functional environment of those microorganisms (de Souza et al., 2019; Kaminsky et al., 2019).

The SynCom approach has become a promising technology as it integrates the concept of microbial ecology and genetics. A SynCom can be constructed using either a top-down approach or a bottom-up approach (Großkopf and Soyer, 2014). The top-down approach focuses on functional definition for a community to characterize its structure and dynamics in details. (Toju et al. 2020) applied the functional core microbiome concept to discover the best combinations of species/strains that potentially maximize functionality at the community/ecosystem level. This method produces communities with natural representation and high reproducibility while lowering the chances of missing important species. However, the effectiveness is dependent on the ability to accurately measure species diversity in a complex community. The bottom-up approach identifies common interaction patterns and processes among species. Paredes et al. (2018) used binary-association assays to design a SynCom for Arabidopsis thaliana that led to predictable plant phenotypes. Even though it facilitates establishing causality, it requires technological advances to manage high complex communities and increases the chances of missing important community members. Recently (Kehe et al. 2019) introduced a microfluidic droplet-based platform, the kChip, to automatically construct SynComs with all possible microbe combinations using a set of species. This has made the SynCom approach more efficient and viable for large scale studies but has limitations that may make it difficult to replicate for field trials

An effective SynCom can be produced by identifying functional communities through a top-down approach and then applying the bottom-up approach to study the interactions between the members of those communities. Genomic information and gene expression profiles could be used to select the microbes with beneficial functional traits or metabolic capability to design the best microbial combination for the microbial consortia (Toju et al., 2018; de Souza et al., 2020). Since multiple genes are responsible for important traits, such as colonization efficiency, and prevalence, genomic analysis for multiple markers may be key to identifying relevant microbes (de Souza et al., 2016; Cole et al., 2017; Levy et al., 2018; de Souza et al., 2019; de Souza et al., 2020). Computational tools can be used to screen for beneficial microbial candidates from existing genomic datasets, which would be less laborious than traditional methods (Finkel et al., 2017). Then, the SynCom could be constructed using a bottom-up approach by addition, elimination, or substitution at the strain level (Vorholt et al., 2017; Liu et al., 2019).

An extensive microbial culture collection is essential to building a SynCom since it is comprised of culturable microbes (Finkel et al., 2017; Vorholt et al., 2017; de Souza et al., 2020; Choi et al., 2021). The SynCom approach is initiated from the isolation of microbial cultures from the natural ecosystem and then formulated through manipulations of the selected microbiota to perform the desired functions for the host plants (de Souza et al., 2016). Since nearly 99% of bacteria are unculturable, novel approaches are necessary to generate extensive microbial collection. One approach is to use metagenomic analysis to identify appropriate media and culture conditions (Oberhardt et al., 2015). Also, high-throughput bacterial cultivation methods, such as the limiting dilution method (Zhang et al., 2019; Zhang et al., 2021), cell sorting (Bai et al., 2015), and colony picking (Armanhi et al., 2018) provide potential solutions for capturing diverse bacterial species on a large scale (Liu et al., 2019).

The effectiveness of SynComs can be quantitatively and qualitatively assessed with plant hosts under controlled environments using different axenic systems such as agar-based (highly artificial and uniformly controlled), clay-based (mimic soil), and FlowPot (autoclaved and washed soil) systems (Bai et al., 2015; Castrillo et al., 2017; Liu et al., 2018; Paredes et al., 2018; Finkel et al., 2019; Zhang et al., 2019). Axenic systems allow for detailed investigations of its components under controlled and reproducible conditions, which facilitate the establishment of causal links between genotypes and phenotypes. Changes can also be made at the functional level by removing or adding specific functions via gene expression (Liu et al., 2019). Further, the consequences of biotic or abiotic perturbations can be monitored at all levels (Liu et al., 2018; Liu et al., 2019; Melnyk et al., 2019).

Finally, an efficient SynCom could be tested under real field conditions to offset the limitations of the traditional approach. Assessment of a SynCom on plant phenotypic traits could be done through high-throughput phenotyping technologies as they offer multiple advantages such as automated, non-destructive and dynamic monitoring of morphological and physiological traits related to growth, yield, and performance throughout their entire lifecycle (Rouphael et al., 2018). This would facilitate an effective SynCom with more compatible, efficient, and adaptable microbes (Hart et al., 2018; Choi et al., 2021).

SynCom approaches have been used in experimental ecology and evolution studies to understand ecological interactions as well as ecological processes (Castrillo et al., 2017; Finkel et al., 2017; Cairns et al., 2018; Levy et al., 2018; Teixeira et al., 2021). The SynCom approach started being used to test evolutionary interactions in plant-microbe studies. Then, the focus has shifted towards the improvement of plant growth and production.

Several studies have been conducted in the model plant A. thaliana as well as agricultural crops – maize, soybean, sorghum, and tomato – to understand plant-microbe interactions using SynComs under controlled environments ( Table 2 ). Bodenhausen et al. (2014) showed that host genotype influences the phyllosphere community composition and abundance using fifty-five A. thaliana plant mutants inoculated with a SynCom. Castrillo et al. (2017) studied the effect of plant Pi stress response on the A. thaliana immune system function and microbiome assembly a SynCom composed of thirty-five members. Niu et al. (2017) constructed a simplified seven-species SynCom from microbes associated with maize root to investigate the dynamics of root colonization, interspecies interactions, and the role of each member in the community. The SynCom approach has also been used to examine the role of specialized metabolites on the colonization of bacteria in the A. thaliana rhizosphere (Voges et al., 2019). In another SynCom study, it was reported that root colonization was regulated by microbe-associated molecular patterns (MAMPs) -triggered immunity (Teixeira et al., 2021). Thus, SynComs can effectively be used to explore plant-microbe interactions, which must be considered when using microbes in large-scale agricultural applications.

Pathogens are a major threat in agriculture as they can lead to complete yield loss. Several studies have reported that SynComs can be effectively used to suppress pathogenic organisms while improving the crop performance (Santhanam et al., 2015; De Vrieze et al., 2018; Santhanam et al., 2019; Ali et al., 2021; Li et al., 2021). Li et al. (2021) constructed two SynComs by adding both high and low abundance bacteria isolated from diseased plants. Results indicated that high abundance bacteria protected host through plant growth promotion and inhibition of the pathogenic fungus, while low abundance bacteria controlled diseases by enhancing plant induced systemic resistance. It is important to note that SynComs showed a superior effect on disease suppression and growth promotion compared to the mono-inoculated plants (Ali et al., 2021; Li et al., 2021). Synergistic interactions between the members of SynComs facilitate improved plant protection as well as growth.

Regardless of other benefits, crop productivity is always a prime concern. Inoculation with a SynCom constructed from sugarcane-associated microbes increased the biomass of maize plants compared to the uninoculated controls (Armanhi et al., 2018). The same SynCom also improved drought tolerance and reduced yield loss in maize (Armanhi et al., 2021). Another SynCom, composed of desiccation-tolerant bacteria, showed increased plant growth parameters such as dry weight of shoot and root, plant height, and plant diameter when compared with either non-inoculated control or mono-inoculated treatments (Molina-Romero et al., 2017). Further, Wang et al. (2021) reported that functionally assembled SynComs improved soybean yield up to 36% under field conditions. Thus, recent studies suggest that SynCom could be effectively incorporated in agriculture to enhance crop yield.

The above studies reiterate that the SynCom approach is an effective tool for exploring plant-microbe interaction and microbe-microbe interaction. Even though most of the SynCom experiments were conducted under controlled conditions, it gives a valuable information about the interaction between each member in the community assemblage and identifying keystone members. For example, Niu et al. (2017) reported that the removal of one species from the SynCom led to drastic changes in community composition. The simplicity of this approach allows repeated experiments to ensure reproducibility which could prevent the problems in the future large-scale application. Recently, the interest in the SynCom approach has been focused on improving crop yield by extending the research in the greenhouse to field conditions which is an important milestone of the SynCom application. Despite there being a long way to go, the current application of SynCom indicates the possibilities to be incorporated into the large-scale application in the near future.

Interest in rhizosphere research has continually grown exponentially since 1994 to the present day,with the term “plant microbiome” first being used as a key word in publications in 2011 (Oresnik et al., 2016). The application of microorganisms in agriculture has emerged as a promising, sustainable approach to improve crop production as the microbiome play an essential role in several plant processes and soil fertility. Poor performance of microbial inoculants is a challenge in developing stable inoculants for agriculture. However, recent advances in high-throughput sequencing technologies create an opportunity to identify the core microbes associated with plants and facilitate the formation of effective SynComs.

Although the SynCom approach is a promising technology, several challenges must be addressed before it can be used in large-scale applications. Designing SynComs with hundreds of microbes is not practical due to a lack of industrial technologies and difficulties in handling them. This issue can be addressed by constructing SynComs with microbes that have multiple beneficial traits and synergistic interactions. Nevertheless, keeping multiple species is challenging as medium composition plays a critical role in population dynamics. Stochastic events can also cause fluctuations of population in mixed communities. Therefore, it will be necessary to monitor the population dynamics of a SynCom to ensure all members are functioning and having enough viable cell counts.

Prediction of SynCom interaction with host plant and soil microbes in natural environment is challenging due to the influence of the native microbes. Thus, maintaining the long-term stability of SynCom is another task to be attained as introduced inoculants are exposed to an environment with competitive species. The SynCom may change over time due to genomic evolution and horizontal gene transfer. In addition, some microbes show differential expression with varying environmental conditions. Sustaining the community robustness and function over a timescale is a crucial aspect. Biosensors and marker gene technologies could be incorporated to trace the interaction and behaviors of introduced SynCom.

The ability to genetically modify or to engineer both host plants as well as microbes has increased dramatically over the last five years. Whereas in the past, there were relatively few microbial genetic model systems (Miller, 1991), the ability to sequence genomes as well as tools such as CRISPR/Cas9 have allowed the genetic modification of many diverse bacteria (Shelake et al., 2019; Rubin et al., 2021). With respect to the development of a SynCom, this can lead to modifying certain community member(s) to allow desired interactions with target crops. Recently it has been shown that endophytic bacteria could be engineered to contain inducible nitrogenase activity (Ryu et al., 2020), which in principle can be combined with plants which have been modified to produce signals for targeted regulation of bacterial genes (Geddes et al., 2019).

So far, most studies have been conducted in controlled systems which are opposite to diverse natural environments. Assessing their stability and plant performance under field conditions is the ultimate target. Production of the required amount of SynComs for large-scale application is also problematic as it would require additional technologies like bioreactors. Determining the effective method of application, whether it is liquid application or seed coating, is another hurdle to be overcome. Extensive field studies with a range of climatic conditions are required to ensure the activity of the applied inoculants.

The development of an effective SynCom is a novel opportunity to improve sustainable food production. It is clear from the literature that microbes are capable of positively affecting plant health and productivity. However, the complexity of dealing with multiple microorganisms that are interacting with field crops with real world climate is challenging. It has been previously pointed out that these types of technologies would have to be transformative to growers for them to be adopted (Oresnik et al., 2016). In the short term, the SynCom approach is an opportunity to delve into the intricacies of plant-microbe interactions as well as microbial ecology. These advances are crucial to better understand how microbes can be manipulated to deliver desired traits to plants. The complex SynComs constructed are clearly important for an academic understanding but are not a pragmatic agronomic solution. The lessons that will be learned from these approaches however can provide valuable information to either produce SynComs that contain fewer microbes, or to develop SynComs that can work synergistically with the native microbial communities already present in the field. The utilization of this technology will require a long-term multidisciplinary approach that includes microbiologists, plant biologists, agronomists, as well as fermentation specialists to facilitate the delivery of a working system. Even though the application of SynCom for crop production is in its infant stage, advances in technologies are occurring at a remarkable pace and it is an approach that has the potential to deliver a solution in our quest toward sustainable agricultural.

AS, PO, and IO conceived and wrote the manuscript. All authors contributed to the article and approved the submitted version. All authors contributed to the article and approved the submitted version.

This work was supported by a grant from the Canadian Agriculture Partnership and Manitoba Pulse and Soybean Growers (grant number 1000227246) to IO.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.