Get Our e-AlertsSubmit Manuscript
BioDesign Research / 2022 / Article

Review Article | Open Access

Volume 2022 |Article ID 9858049 | https://doi.org/10.34133/2022/9858049

Christopher M. Dundas, José R. Dinneny, "Genetic Circuit Design in Rhizobacteria", BioDesign Research, vol. 2022, Article ID 9858049, 30 pages, 2022. https://doi.org/10.34133/2022/9858049

Genetic Circuit Design in Rhizobacteria

Received29 May 2022
Accepted31 Jul 2022
Published06 Oct 2022

Abstract

Genetically engineered plants hold enormous promise for tackling global food security and agricultural sustainability challenges. However, construction of plant-based genetic circuitry is constrained by a lack of well-characterized genetic parts and circuit design rules. In contrast, advances in bacterial synthetic biology have yielded a wealth of sensors, actuators, and other tools that can be used to build bacterial circuitry. As root-colonizing bacteria (rhizobacteria) exert substantial influence over plant health and growth, genetic circuit design in these microorganisms can be used to indirectly engineer plants and accelerate the design-build-test-learn cycle. Here, we outline genetic parts and best practices for designing rhizobacterial circuits, with an emphasis on sensors, actuators, and chassis species that can be used to monitor/control rhizosphere and plant processes.

1. Introduction

Engineering interactions between plant roots and the environment is central to addressing many food, energy, and sustainability challenges. Next-generation plants with augmented root physiology or modified root architecture could be used to lower atmospheric CO2 levels [1, 2], improve crop resilience to biotic and climatic stresses [36], and increase agricultural yields to support a growing human population [7]. Plant synthetic biology approaches are poised to enable these advances, particularly through construction of genetic circuits that rewire plant metabolism and development [8, 9]. Ideally, genetic circuits constructed within plant cells could sense environmental conditions, computationally interpret external and intracellular cues, and actuate desired enzymatic and developmental phenotypes. However, forward engineering plants with these capabilities remain a substantial technical hurdle. Ongoing efforts have sought to fill knowledge gaps by uncovering design rules and building genetic part catalogs for plant-based circuits [1013].

Relative to plants, genetic circuit knowledge bases are more readily available within bacterial synthetic biology [14]. Foundational concepts, such as rationally tuning gene expression [1519], optimizing genetically encoded sensors and actuators [2025], and implementing Boolean-type logic [2632], are now routine tasks in bacterial systems (Figure 1(a)). Using these building blocks, genetic circuits have been built to carry out increasingly complex cellular operations, such as memory [33, 34], arithmetic [35], spatiotemporal pattern formation [3638], and materials synthesis [39, 40].

Advances in bacterial transformation and genome engineering have greatly expanded the range of species that can host designed circuits [4143]. This genetic tractability extends to bacteria within the plant root microbiome (termed rhizobacteria), whose members exert drastic effects on plant health, nutrient acquisition, and soil chemistry [44, 45]. Rhizobacteria effectively colonize root tissues and their growth is significantly influenced by root-exuded metabolites (e.g., carbon sources and secondary metabolites). While genetic circuitry within beneficial rhizobacteria could facilitate designer control over plant root chemistry and physiology, progress in circuit design practices and understanding of rhizosphere processes have only recently made this goal appear tangible.

Prior root microbiome engineering efforts have focused on understanding important plant-rhizobacteria interactions and isolating strains that promote plant growth in field settings. Target rhizobacteria phenotypes have included protecting plants from biotic stresses, termed biocontrol, and aiding roots in acquiring growth-limiting nutrients, such as phosphorus and nitrogen [46, 47]. More recent work has sought to understand how rhizobacteria community structure is impacted by changes in root development, environmental conditions, and species composition [4851]. Generally, rhizobacteria research has seldom used genetic circuits beyond simple gene knockout, complementation, and overexpression constructs. Synthetic biology workflows, which have enabled design of complex sensor-computation-actuation circuits in model chassis (e.g., Escherichia coli) [32], remain largely unused to optimize rhizobacterial strain and community phenotypes.

Given the wealth of synthetic biology tools that can be applied towards rhizobacteria, bacterial genetic circuit design is strongly positioned to address fundamental questions in plant science and supplement plant synthetic biology in tackling challenges in agrobiotechnology. While they are yet to be field-deployed, genetic circuits could enable design of “smart” microbiomes that dynamically sense and respond to changes in plant and environmental conditions (Figure 1(b)) [52, 53]. For example, rhizobacterial circuits could be used to spatiotemporally report on changes in root-exuded metabolites that occur due to plant developmental stage or stress. These sensor inputs might trigger genetic circuitry that programs beneficial rhizobacteria colonization patterns to improve protection against competing root pathogens or abiotic stresses. Circuit-rewired rhizobacterial metabolic pathways could also tailor root chemistry to ameliorate plant nutrient limitations and increase stabilization of photoassimilated carbon within soil [54, 55].

Prerequisite to building functional rhizobacterial circuits is identifying genetic parts that target strategic root and rhizosphere processes. The purpose of this review is to highlight bacterial parts that can be applied towards this goal, including those that may be under-appreciated or unused in the context of rhizobacteria. Specifically, we discuss broad-host-range tools and target rhizobacterial chassis for designing circuits that operate in the rhizosphere. We also highlight how to use bacterial transcriptional regulators and unique reporter genes for building sensors of major root exudates and rhizosphere molecules. Finally, we summarize rhizobacterial biosynthetic actuators that yield plant growth promotion/environmental sustainability phenotypes and discuss future prospects of engineered rhizobacteria. This circuit design primer can inform plant scientists of bacterial parts and synthetic biology tools that can be used for engineering plant-rhizosphere interactions.

2. Tools for Building Circuits in Rhizobacteria

The wide-ranging diversity of rhizobacterial species poses a challenge when attempting to build sensor and actuator circuits that function across different strains. Genes and regulatory elements can exhibit highly varied activity within different genetic backgrounds [56, 57]. Construction of functional circuits often necessitates laborious host-specific optimization by testing libraries of promoters and ribosome binding site (RBS) sequences that fine tune gene expression [15, 18]. Plasmid-based circuits may also be challenging to port into new hosts when replication machinery is not cross-species functional [58]. Integrating circuits into host genomes can mitigate this issue, but genome engineering tools are not always available for new hosts. These constraints motivate the design of rhizobacterial circuits built with predictive tools and broad-host-range parts, as doing so can increase successful circuit construction and expedite design-build-test-learn cycles. Here, we highlight different tools that can be employed to “domesticate” rhizobacteria and permit rational assembly of genetic circuits.

2.1. Engineering Transcription and Translation in Rhizobacteria

Transcriptional and translational elements are critical parts for genetic circuits, but their performance can be highly variable between rhizobacterial hosts. To improve part predictability, circuit design workflows can use bioinformatic tools that aid in identifying functional promoter and RBS sequences. For example, the RBS Calculator allows users to design variable strength RBS libraries that function with high accuracy in a target host bacterium [16, 59]. The Calculator integrates sequence information from host 16S rRNA and RBS-adjacent parts to predict mRNA translation rates. This tool has enabled facile optimization of sensor and actuator circuits within diverse rhizobacteria, including Pseudomonas, Klebsiella, Rhizobium, and Bacillus spp. [5962]. In contrast, tools for promoter identification have relied upon rhizobacterial genome mining for common promoter motifs [63, 64], as predicting promoter DNA-protein interactions is more challenging than RBS mRNA-rRNA interactions. However, recent biophysical models have made progress towards elucidating E. coli promoter design rules [65], and similar strategies might be capable of forward engineering promoters in rhizobacterial chassis.

Circuit predictability can also be increased using orthogonal gene expression machinery that decouples requirements from native host resources [66]. An RNA polymerase from T7 bacteriophage (T7 RNAP) is frequently used for this purpose, as it transcribes genes downstream of its cognate promoter independent of host RNAP machinery and with high activity/specificity. Notably, T7 RNAP exhibits broad-host-range functioning across the Proteobacteria and Firmicutes clades. Ryu et al. showed that T7 RNAP could serve as the primary transcriptional controller when optimizing nitrogen-fixation pathways in cereal-colonizing rhizobacteria [61]. Similarly, Wang et al. leveraged T7 RNAP to rapidly prototype fluorescently labeled strains of Brachypodium distachyon-colonizing Pseudomonas simiae [67]. Further orthogonalization of T7 RNAP/promoter variants [68, 69] and the ability to self-regulate T7 RNAP expression [70, 71] demonstrate the system’s versatility for building chassis-independent circuits in rhizobacteria. While host-decoupled translation machinery, such as orthogonal ribosomes [7275], remains a relatively nascent technology, they might also be used to increase the chassis range of designed circuits.

2.2. Broad-Host-Range Plasmids

Genetic circuits are frequently assembled into plasmid DNA molecules that can be used to transform rhizobacteria. Multicopy plasmids replicate separately from the host chromosome and facilitate rapid prototyping of genetic circuits without a priori genome sequence information. However, rhizobacterial circuit design can be hampered by the limited host range of common plasmid origins of replication. Plasmid-based circuits that use the E. coli-functional ColE1 and p15A origins cannot be directly ported into most rhizobacteria [76]. Furthermore, plasmid instability in the absence of antibiotics—a scenario often encountered during plant colonization by rhizobacteria—may lead to circuit failure due to cell division-driven plasmid loss [77]. To address these limitations, rhizobacterial circuits can include genetic parts that decrease their reliance on host replication machinery and increase their maintenance in the absence of selection.

Several toolkits have been developed to ease assembly of broad-host-range plasmids. The Bee Tool Kit (BTK) [78], Bacterial Expression Vector Archive (BEVA) [79], and Standard European Vector Architecture (SEVA) [8082] are a few prominent examples of broad-host-range plasmid systems that use modular cloning practices. These plasmids use genetic parts (i.e., regulatory elements, target genes, resistance markers, and origins of replication) flanked by distinct Type II or Type IIS restriction sites, which enables modular and hierarchical assembly of genetic designs. Notably, these systems report the characterization of broad-host-range origins of replication, such as RK2, BBR1, and RSF1010, that function within diverse plant and animal commensal bacteria. RSF1010 is particularly noteworthy, as it is one of the few known origins that replicates across Proteobacteria, Actinobacteria, and Firmicutes [83, 84]. Although antibiotic resistance is typically required for rhizobacterial plasmid maintenance, antibiotics can affect plant growth. To increase antibiotic-free maintenance, parts for plasmid stabilization can also be incorporated into broad-host-range plasmids. In the BEVA system, the RK2-derived par locus substantially mitigates plasmid loss by rhizobacteria grown in non-selective media [85, 86]. This system and others rely upon plasmid-encoded toxins and antitoxins, whereby plasmid loss results in a lack of antitoxin production and a promotion of toxin-mediated cell death [87, 88]. Together, these broad-host-range plasmid toolkits provide a wealth of useful parts that can be leveraged for rhizobacterial circuit design. Regardless of the cloning system chosen, circuit designers should consider standardizing their plasmid backbones with toolkit-derived origins and stability regions to increase circuit portability and efficacy in new chassis.

2.3. Genome Engineering Rhizobacteria

To avoid potential circuit-impairing effects of plasmids (e.g., variable cell-to-cell copy number) [89], rhizobacterial circuit designers can leverage genome engineering. In contrast to multicopy plasmids, genome-integrated circuits replicate with bacterial chromosomes in the absence of selective pressure and maintain copy numbers close to one. Early methods for genome engineering, such as plasmid-mediated homologous recombination and integrative transposons [90, 91], remain widely used to generate targeted deletions and deliver circuit cargo into rhizobacteria. More recent tools have expedited the timeframe for rhizobacterial genome engineering by possessing wider host ranges, higher genetic specificities, and multiplex editing capabilities.

Improved domestication strategies have been a critical advancement in synthetic biology and enabled circuit design in many previously intractable rhizobacteria. Brophy et al. miniaturized the integrative and conjugative elements from Bacillus subtilis (mini-ICEBs1) to develop a B. subtilis donor strain that conjugates circuits into a wide range of gram-positive recipients [41]. This system integrates circuits at a conserved leucine tRNA locus and was successfully used to transfer inducible GFP expression and nitrogen-fixation circuits into soil isolates and mixed communities. Wang et al. developed a chassis-independent recombinase-assisted genome engineering (CRAGE) tool that domesticates diverse Proteobacteria and Actinobacteria using a randomly integrating transposon [42]. Without requiring a priori genome sequence information, the conjugated transposon chromosomally integrates Cre recombinase and an antibiotic resistance gene flanked by mutually exclusive lox sites. This permits delivery of additional lox-flanked genetic cargo in subsequent rounds of conjugation and antibiotic selection. This system was capable of functioning in rhizobacteria and delivering CRISPR cargo to target host biosynthetic pathways [67, 92]. Recently, CRISPR-transposase technology was used to perform species- and site-specific genome editing within soil bacteria communities [43]. CRISPR-transposases facilitate gRNA-targeted integration of cargo to desired chromosomal loci [9395]. The Doudna and Banfield labs used this platform to perform gene knockout and deliver carbon utilization pathways to community-residing species in a selection-free environment [43]. These next-generation genome engineering tools are poised to enable circuit assembly across new chassis within the rhizosphere, including many unculturable rhizobacteria.

3. Selecting Rhizobacterial Chassis for Genetic Circuits

Circuit functioning within rhizobacteria is underpinned by the ability of these chassis to effectively colonize root tissues. Circuits used to monitor and modulate plant growth would ideally be deployed in strains exhibiting high extents of colonization (i.e., bacterial cells per root). Additionally, the inputs and outputs of rhizobacterial circuits are strongly tied to root colonization patterns. For example, rhizobacterial sensors that preferentially localize to different root compartments (e.g., epiphytes vs. endophytes) and anatomical regions (e.g., root hairs vs. root tips) will be exposed to distinct soil nutrient, exudate, and developmental stimuli [96, 97]. Colonization patterns will similarly affect bacterially augmented nutrient uptake and phytohormone signaling. Furthermore, the ecological stability of rhizobacteria within microbial communities can impact their extent of colonization and the success of implemented circuitry [98, 99]. Thus, in addition to the parts used to build rhizobacterial circuits, colonization capabilities of the host chassis should be considered equally important design parameters.

3.1. Native and Engineered Colonization Activity

Rhizobacterial chassis can be selected based on known colonization and strain characteristics. Strains that localize to distinct root regions, such as elongation zone-colonizing Bacillus [100], could be used to target circuitry to specific developmental stages. Other root-colonizing strains may be chosen due to existing plant growth-promoting phenotypes. For example, Pseudomonas and rhizobial species frequently serve as testbeds for engineered circuitry due to their well-studied root colonization behavior and functioning in biocontrol and nitrogen fixation [101, 102]. While many other bacteria possess advantageous genetic backgrounds (e.g., biosynthetic gene clusters), poorer rhizosphere persistence by these strains motivates genetic optimization of colonization. This can be accomplished by targeting genes that control colonization-relevant traits, such as chemotaxis, root attachment, biofilm formation, and plant immune system evasion [103]. Optimized strains might increase production of biofilm-promoting exopolysaccharides [104, 105] or express immune system-evading flagellin monomer proteases [106]. Alternatively, experimental evolution strategies could be used to select for high-colonizing strains that persist in the rhizosphere over multiple inoculation/selection cycles. This approach has evolved Pseudomonas and Bacillus species with elevated root colonization through mutations in genes encoding global regulators, biofilm development, and motility machinery [107110]. Refining genetic design rules for root colonization should further our ability to spatiotemporally control circuit functioning and transfer colonization phenotypes into potentially beneficial bacteria that do not natively colonize plants.

3.2. Chassis-Microbiome Interactions

Bacterial fitness and interactions within rhizosphere communities are also important design constraints when choosing a circuit-hosting chassis. While an engineered strain may be an effective root colonizer in monoculture, it could be outcompeted by other bacteria in a community setting. This would concomitantly dilute circuit-driven sensing and plant growth-promoting phenotypes. Biocontrol strain chassis (e.g., Pseudomonas protegens and Bacillus velezensis) may mitigate this issue [111, 112], as these strains produce a spectrum of antimicrobial compounds that aid their establishment within the root microbiome. Conversely, rhizobacterial chassis may negatively impact rhizosphere community structure and encourage proliferation of bacteria that cause adverse plant phenotypes. Selection of keystone rhizobacterial species as chassis might stabilize community effects on plant hosts by mediating critical interactions across microbial taxa [113]. Variovorax spp. are a recent example of this, as they broadly suppress root growth inhibition in complex communities through a genus-conserved auxin degradation operon [51]. Likewise, chassis strains should be evaluated for their ability to elicit microbiome-modulating plant disease phenotypes, such as induced systemic resistance or induced systemic susceptibility [114117]. Engineered circuitry might also alter chassis composition within communities through targeted killing of population members [118] or signal-based regulation of population density [119].

4. Sensors for Root Exudates and Rhizosphere Compounds

Rhizobacteria encounter diverse chemical stimuli within roots and in the rhizosphere. Root exudates include many primary (e.g., sugars, organic acids, and amino acids) and secondary metabolites (e.g., phytohormones), whose concentrations continuously vary based on their location of release from the root, plant developmental stage, environmental conditions, and in response to biotic/abiotic stresses [45, 120]. Additionally, the rhizosphere contains many inorganic plant nutrients (e.g., nitrate and phosphate) that also affect root microbiome activities. The intimate relationship between these molecules and plant functioning makes them attractive sensing targets for monitoring plant health and actuating interventions when necessary. Accordingly, rhizobacterial genetic circuits could be leveraged to spatiotemporally perceive and report on the concentration of key rhizosphere compounds.

While many genetic parts can be used to create biosensors, small molecule-responsive transcriptional regulators possess several advantages for rhizobacterial sensor design. Transcriptional sensor circuits typically function by expressing one- or two-component regulator proteins that modulate expression of cognate promoters placed upstream of a regulated gene (e.g., a reporter protein) (Figure 2(a)) [121, 122]. These circuits have well-established design rules and exhibit predictable patterns of activity based on gene expression (e.g., the Hill equation) [14, 123, 124]. Modeled response functions relate inducer molecule concentrations to output activity, which are parameterized by their sensitivity (i.e., threshold inducer concentration) and dynamic range (i.e., ratio of maximal induction to uninduced state). These parameters are engineered by changing the promoter and RBS strength of both regulator and output genes. Protein engineering can also tune response functions by modulating the regulator’s ligand/DNA affinity or kinase/phosphatase activity [22, 125, 126]. Furthermore, outputs of transcriptional sensors can be connected to additional downstream modules, such as multi-input logic gates and actuator elements.

Here, we characterize various transcriptional regulators that can be used to build rhizobacterial sensor circuits for major root exudates and rhizosphere compounds (Table 1). We also discuss transcriptional sensors that can monitor root-induced changes in overall bacterial physiology (e.g., growth rate). Lastly, we identify non-fluorescent sensor outputs that can potentially monitor rhizobacteria activity in complex plant-soil systems.


Sensor targetSensor gene(s)-output promoteraDetection rangeBacterial chassis-tested plant/environmentReference

SucrosescrR - PscrY10 μM to 1000 μMErwinia herbicola - wild oat (Avena barbata)[130, 134]
Fructosen/a - PfruB1.67 μM to 167 μMErwinia herbicola - common bean (Phaseolus vulgaris)[135]
Glucosen/a - PgluA7500 μM to 10 mMEscherichia coli - n/a[139]
Glucose-6-phosphateuhpABC - PuhpT0.5 μM to 4 μMEscherichia coli - n/a[138]
Nitratefnr and narXL - PnarG0.1 μM to 10 mMEnterobacter cloacae - wild oat (Avena fatua)[144]
NitratenarX and narL/ydfI - PydfJ1155.62 μM to 562 μMBacillus subtilis - fertilized soil[126]
L-Tryptophann/a - Paatl0.1 μM to 100 μMErwinia herbicola - wild oat (Avena barbata)[130]
L-Phenylalaninen/a - PphhA10 μM to 5000 μMRhizobium leguminosarum - pea (Pisum sativum)[136]
L-ProlineputR - PputA0.1 μM to 500 μMRhizobium leguminosarum - pea (Pisum sativum)[156]
L-LysinelysG - PlysE5 mM to 25 mMCorynebacterium glutamicum - n/a[157]
L-Methioninelrp - PbrnF0.2 mM to 23.5 mMCorynebacterium glutamicum - n/a[158]
L-GlutamateaauSR - PrpoN20 mMbPseudomonas aeruginosa - n/a[159]
NaringeninfdeR - PfdeA2 μM to 100 μMPseudomonas protegens - n/a[61, 161]
QuercetinqdoR - PqdoI10 μM to 100 μMEscherichia coli - n/a[161]
LuteolinfdeR/nodD1 - PfdeA(R)7 μM to 56 μMHerbaspirillum seropedicae - n/a[162]
ApigeninfdeR - PfdeA(R)7 μM to 60 μMHerbaspirillum seropedicae - n/a[162]
Salicylic acidnahR - Psal1 μM to 100 μMAzotobacter caulinodans - n/a[61]
Indole-3-acetic acidiacR - PiacH1 μM to 200 μMEnterobacter soli - n/a[180]
2-Phenylacetic acidpaaXK - PpaaA10 μM to 200 μMEscherichia coli - n/a[185]
OctopineoccR - Pocc1 μM to 10 μMAzotobacter caulinodans - n/a[61]
NopalinenocR - Pnoc100 μM to 1000 μMAzotobacter caulinodans - n/a[61]
Scyllo-inosaminemocR - PmocB1 μM to 1000 μMRhizobium leguminosarum - alfalfa (Medicago sativa), barrelclover (Medicago truncatula), and barley[167]
Intracellular ribosomal biosynthesisn/a - PEcrrnB0.17 h-1 to 0.28 h-1Pseudomonas putida - barley[192]
Intracellular NADH/NAD+ ratiorex(Bs) - PropIP0.38 to 0.88cEscherichia coli - n/a[198]

a. Sensor genes and promoters were either used directly in the cited reference or inferred. b. Only a single L-glutamate concentration was tested for sensor induction relative to its absence. c. Sensor NADH/NAD+ range was inferred from biochemically measured values.
4.1. Sugars

Sugars, such as sucrose, glucose, and fructose, are primary components of soluble root exudates (up to 65% by mass) [127, 128]. Root-exuded sugars serve as carbon sources for rhizobacteria and can be reabsorbed by roots to fulfill plant biomass and energy requirements. Sucrose is particularly noteworthy, as it is the primary carbon fixation product of photosynthesis and serves as currency in the plant’s carbon economy [129]. Based on source-sink limitations within plant tissues, sucrose is differentially transported to non-photosynthetic organs (e.g., roots) to support their growth. Concentrations of root-exuded sucrose have shown to spatially increase towards growing root tips and are significantly higher during the seedling stage of plant development [130132]. Given this ability to spatiotemporally map root development and potentially serve as a biomarker for various plant stresses (e.g., pathogen infection), sucrose and its associated sugars appear as valuable inputs for building rhizobacterial genetic circuits (Figure 2(b)).

Sugars are common circuit inducers across bacterial synthetic biology with two of the most popular inducers being the plant-released sugars, xylose and arabinose [20, 133]. However, only a few studies from the early 2000s report constructing sucrose-inducible circuits. These sucrose sensors were engineered in the plant epiphyte, Erwinia herbicola, using the regulator-promoter pair from Salmonella typhimurium, ScrR-PscrY, and outputting reporter genes encoding either GFP, LacZ, or InaZ [130, 134]. The sucrose sensor’s detection range of 10 to 1000 μM enabled detection of sucrose exuded along soil-grown Avena barbata roots, with a measured concentration of ca. 100 μM near the root tip. Circuits can also be used to detect the release of sucrose catabolic products, glucose and fructose [135]. Pini et al. analyzed the transcriptomics profile of Rhizobium leguminosarum to pinpoint native promoters that upregulate genes during sucrose and fructose supplementation [136]. Biosensors were built by transforming R. leguminosarum with plasmids carrying identified promoters placed upstream of the luxCDABE operon. Quantification of lux-driven luminescence revealed the importance of rhizobia nitrogen fixation for high plant exudation of sucrose and fructose in pea root nodules. Modern synthetic biology optimizations could potentially upgrade these rhizobacterial circuits to increase linearity in their detection range [35] and portability to strain backgrounds lacking sensor-interfering sugar catabolism [137]. Circuits not previously tested in rhizobacteria might also be useful for sugar detection, such as other one-component/two-component circuits or those that monitor glucose-6-phosphate levels [138, 139].

4.2. Nitrogen Compounds

Nitrogen is a growth-limiting plant nutrient that is taken up from soils by roots and transported to aboveground organs [140]. Consequently, nitrogen compounds within the rhizosphere and those released as root exudates are closely tied to plant and microbiome functioning [120]. While it is not an appreciable root exudate, nitrate is a major plant nitrogen source and is exogenously supplemented through chemical fertilization of soils. Relative to other forms of nitrogen, root exudation of sugars appears to be heavily downregulated by nitrate supplementation [141]. This suggests strong ties between nitrate levels and the feeding of resident bacteria growing on roots [132]. In addition to its plant and microbial assimilation, nitrate serves as a dissimilatory respiratory source for denitrifying bacteria under anaerobic conditions [142]. Nitrate uptake processes are also linked to changes in root developmental signaling, with lower rhizosphere nitrate levels triggering a foraging response of auxin-promoted lateral root growth [143]. Taken together, construction of rhizobacterial nitrate sensors could aid precision agriculture-guided fertilization, improve understanding of nitrogen effects on rhizosphere communities, and potentially forecast changes in root development (Figure 2(c)).

Nitrate-sensing circuits can be built that function in the soil environment. DeAngelis et al. developed a plasmid-based sensor using the promoter of the Escherichia coli nitrate reductase gene, narG, placed upstream of either the gfp or inaZ reporter genes [144]. Under anaerobic conditions, PnarG is induced by nitrate and the global regulator protein FNR. Since FNR is inactive under aerobic conditions, they also co-expressed an oxygen-insensitive mutant of FNR (FNR-L28H) [145], which permitted aerobic nitrate inducibility in the range of 0.1 μM to 10 mM. When testing soil-grown grass Avena fatua colonized by Enterobacter cloacae carrying this sensor, it was observed that reporter activity was highest in nitrate-amended bulk soils and substantially lower around root tissue [144]. This was interpreted as rapid assimilation of nitrate by roots, with reporter-based rhizosphere concentration estimates at 1 μM. A limitation of this sensor was that it presumably relied upon the chassis’ native expression of the sensor histidine kinase-response regulator pair, NarX-NarL, to modulate PnarG activity [146, 147]. In contrast, the Tabor Lab built a nitrate sensor circuit by directly inserting Escherichia coli narX-narL into the genome of Bacillus subtilis [126]. To mitigate heterologous expression issues with the E. coli nar promoter, the C-terminal of NarL was replaced with an orthologous DNA binding domain from B. subtilis YdfI, which enabled nitrate-inducible gfp expression from the Bacillus functional PydfJ115 promoter. Protein engineering to decrease NarX phosphatase activity increased sensor sensitivity and dynamic range, relative to circuits with the wild type NarX. Using their optimized sensor, they demonstrated nitrate detection in chemically fertilized soils over a range of 5.62 to 562 μM. While their biosensor was not used in a plant root environment, a recent report testing a similar NarX-NarL sensor in mouse-colonizing E. coli Nissle 1917 suggests promise for in situ sensing of nitrate in microbiomes [148].

Amino acids are another important form of rhizosphere nitrogen. These small molecules are naturally abundant in soils and can serve as organic plant nitrogen sources, particularly during inorganic nitrogen deficiency [149, 150]. Like nitrate, amino acids strongly interact with plant signaling pathways that modulate root architecture [151]. In contrast to inorganic forms of nitrogen, amino acids constitute a large fraction of root exudates. Amino acids readily nourish root microbiota and are significant chemoattractants for rhizobacteria [152, 153]. Plants dynamically alter their amino acid exudation during growth and under various environmental contexts, including nutrient deficiencies [128], elevated atmospheric CO2 levels [154], and exposure to microbial products [155]. Given these roles, amino acids may be useful inducers for rhizobacterial circuits (Figure 2(c)).

The diversity of proteinogenic amino acids gives rise to numerous genetic parts for their quantitation in the rhizosphere. Jaeger et al. built a tryptophan biosensor by fusing the inaZ reporter gene to the tryptophan aminotransferase gene, aatl, within the genome of epiphyte E. herbicola [130]. The aatl promoter is induced in the presence of tryptophan, which enabled the biosensor strain to detect higher levels of the amino acid around emerging lateral roots of soil-grown Avena barbata. Similarly, Pini et al. used the native phenylalanine-inducible promoter of phenylalanine-4-hydroxylase from R. leguminosarum to trigger luxCDABE-based luminescence within this bacterium [136]. Plasmid-based expression of the biosensor revealed temporal dynamics of phenylalanine exudation within the rhizosphere and root nodules of pea. The same group built another genetic sensor in R. leguminosarum for proline—a particularly abundant root exudate—using the native putR regulator gene and a promoter region proximal to a proline catabolism gene, putA [156]. Previously characterized genetic parts from non-rhizobacteria might also be co-opted to build rhizobacterial sensors for exuded amino acids, such as lysine, methionine, and glutamate [157159].

4.3. Secondary Metabolite and Phytohormones

In addition to primary metabolites, plants exude several secondary metabolites and hormones that shape plant development, rhizosphere chemistry, and microbial activities. For many of these specialized compounds, genetic parts can be leveraged to build bacterial sensor circuits (Figure 2(d)). One example of this is with flavonoids: a class of plant-released compounds that regulate various plant-microbe symbioses [160]. Sensor circuits were built in bacterial chassis, including rhizobacteria, to detect the flavonoids naringenin, quercetin, luteolin, and apigenin [61, 161, 162]. Del Valle et al. showed that flavonoid biosensors can be used to analyze flavonoid-containing soil, which enabled the researchers to study how soil organic matter affects naringenin bioavailability and subsequent root nodulation frequency with Medicago sativa [163]. Given the role of flavonoids in diverse health and consumer products, flavonoid-sensing bacterial circuits might be used to aid plant metabolic engineering efforts [164].

Engineered exudation of secondary metabolites can facilitate designer interactions between plants and rhizobacteria. Towards this goal, transgenic plants were created that express biosynthetic pathways for non-native signaling molecules, opines and rhizopines [165167]. These compounds are naturally produced by Agrobacterium-infected plants and root nodulating rhizobia, respectively, and serve as determinants of bacterial colonization and catabolism [168]. Plants with engineered opine exudation exhibited increased colonization by opine-catabolizing bacteria [165, 166] and can potentially be paired with biosensors for octopine and nopaline [61]. The Poole Lab demonstrated that root exudation of the orthogonal rhizopine, scyllo-inosamine, could mitigate bacterial catabolism and enable specific transkingdom signaling by bacteria carrying optimized rhizopine sensors [167]. Development of further orthogonal signaling circuitry will be critical for multiplexing communication to individual rhizobacteria and engineering communities whose members actuate distinct tasks [169].

Bacterial circuits can also be built to sense plant hormones. For example, salicylic acid is a pathogen defense-related hormone and has been the target for sensor circuits built in rhizobacterial chassis [61, 136, 170]. Auxins are particularly important hormones, due to their significant effects on plant growth [171] and their biosynthesis/catabolism by rhizobacteria [172, 173]. While genetic methods for auxin detection are frequently used in eukaryotic systems [174176], there are surprisingly few examples of auxin-inducible circuits engineered in bacteria. Indole-3-acetic acid (IAA) is one of the primary auxins within plants [177], but there appears to be no bacterial biosensor circuits explicitly built for detecting this molecule. Nonetheless, studies on bacterial IAA degradation pathways have identified IAA-responsive transcriptional regulators [178180]. Although their use in circuits has not been tested, these parts are promising candidates for IAA sensor design. Additionally, Wang et al. created biosensors for non-IAA indole compounds and proposed their engineered regulator could be adapted for IAA specificity [181]. Bacterial circuits have also been built to sense the less understood auxin, 2-phenylacetic acid (PAA). PAA similarly affects plant growth and rhizobacterial activities [182, 183], albeit at higher concentrations than IAA. Bacterial circuits were optimized to output the fluorescent reporters, GFP and RFP, in response to PAA and its related compounds, 4-hydroxyphenylacetic acid, and 2-phenylethylamine [184, 185]. Future developments in hormone sensing circuitry might enable design of rhizobacterial strains that rewire plant signaling to influence plant growth and immunity [186].

4.4. Rhizobacterial Physiology

Root exudates affect general physiological processes of rhizobacteria, such as cell growth rate and intracellular metabolite levels. As root exudate composition varies during plant growth, rhizobacteria adjust their metabolism to feed on different sets of encountered nutrients [132]. Sensor circuits could be leveraged to spatiotemporally interrogate these generalized exudation effects and program downstream circuit responses by rhizobacteria (Figure 2(e)).

Bacterial growth rates have long been correlated with cellular ribosomal RNA (rRNA) levels and activity from rRNA promoters [187189]. These phenomena are explained by translation-based growth limitations—bacteria need more ribosomes to grow faster. Thus, reporter genes placed downstream of rRNA promoters can potentially act as cell growth rate and metabolic activity sensors [190, 191]. Using this approach, the activity of Pseudomonas on barley roots was studied by chromosomally integrating a rrnB ribosomal promoter from Escherichia coli upstream of an unstable gfp variant [192]. Cells from growth rate-controlled chemostats exhibited ribosomal content and GFP fluorescence that were linearly related to growth rates in the range of 0.17 to 0.28 h-1 (i.e., doubling times of ca. 4 to 2.5 h). When analyzing this strain’s growth on barley seedling root tips, epifluorescence microscopy revealed highest GFP levels at the edges of microcolonies formed on border cells. This contrasted another barley-colonizing P. putida strain that constitutively expressed GFP via the lac promoter, where broad cellular fluorescence was observed across the entire root tip. In a separate study, rRNA-driven expression of unstable GFP in Pseudomonas fluorescens similarly showed higher fluorescence at the root tips of colonized alfalfa [193]. Strains that expressed more stable GFP variants yielded fluorescence throughout the entire root system. These results support increased exudation rates at growing root tips and suggest that rRNA promoters can be used to map spatial gradients of bacterial activity on roots. Although rRNA-bacterial activity correlations may break down under certain growth rates and environmental conditions [194], these genetic elements provide an immediately obvious part to test for building growth-responsive rhizobacterial circuits. Understanding of rRNA promoter functioning within rhizobacterial hosts may be further improved through circuit-physiology modeling [195].

Intracellular bacterial energetics is also tied to root exudation. The intracellular ratio of global energy carriers, such as NAD+/NADH, changes based on carbon source availability and has been tied to rhizobacterial colonization [196]. While not tested in rhizobacteria, the Rex regulator can be used to build transcriptional circuits that detect NAD+/NADH ratios [197, 198]. Liu et al. showed that the Rex regulator from B. subtilis, B-Rex, can induce fluorescent reporter expression under conditions of high intracellular NADH (i.e., anaerobic growth) by derepressing a promoter that contains a Rex operator. Porting this energetics sensor into rhizobacteria could potentially improve understanding of carbon use efficiency with root exudates [199] and complement other sensors that interrogate influential rhizosphere parameters (e.g., oxygen levels) [200, 201].

4.5. Reporters for Monitoring Plant-Bacteria Interactions in Soil

Fluorescent reporters have been powerful tools for unraveling plant-rhizobacteria interactions but are challenging to use in soil settings. Imaging of rhizobacterial fluorescence typically requires disruptive preparation of root samples [202], which can potentially perturb colonization patterns of soil-grown plants. Although live fluorescent imaging can be accomplished for plants grown in optically transparent soil [203, 204], differences in chemical and physical properties of these synthetic systems may alter rhizobacterial colonization and plant growth when compared to natural soils. To address these limitations and build genetic circuits that report on rhizosphere dynamics in situ, reporters that function within soil environments are needed.

Luminescence has been used as an output for many rhizobacterial genetic circuits, and in some situations can be used in soil environments. Many of these circuits leverage the lux operon, which biosynthesizes enzymes and metabolite substrates required for bacterial luminescence [205]. Rellán-Álvarez et al. built a rhizotron imaging system capable of phenotyping luminescent plants and rhizobacteria in soil, termed Growth and Luminescence Observatory for Roots (GLO-Roots) [206]. The thin dimensions of their rhizotrons enabled cameras to image the entire root system of transgenic Arabidopsis thaliana, Brachypodium distachyon, and Setaria viridis that expressed luciferase genes and were watered with luciferin substrates [207]. They also showed that this system could image a root-colonizing P. fluorescens strain that constitutively expressed lux. Considering recent robotics and automation upgrades to their system (GLO-Bot) [208], this rhizotron platform could potentially be used to prototype sensor circuits in a soil environment.

Non-optical reporters have been used to build genetically encoded sensors for soil bacteria processes. The Silberg Lab developed a soil-functional reporter system using sensor circuits that biosynthesized indicator gaseous compounds, methyl bromide and ethylene [163, 209211]. In their initial circuits, a ratiometric gas signal was generated by inducer-regulated methyl halide transferase (MHT) expression/methyl bromide production and constitutive ethylene forming enzyme (EFE) expression/ethylene production. Sensor strains were mixed into soil within sealed containers, and headspace concentrations of the output gasses were quantified by mass spectrometry. These sensors generated typical Hill function responses and could detect quorum sensing molecules within soil matrices modulated by rhizobacteria, Bacillus thuringiensis and R. leguminosarum. Although non-ethylene ratiometric gasses would be needed to mitigate interference with native plant signaling, generation of methyl halide alone can permit functional sensor activity [163, 211]. Additionally, their gas reporter approach appears compatible with existing infrastructure used to study plant release/uptake of gaseous compounds [212]. Ultrasound technology for imaging soil-grown plant roots [213] might similarly be adapted to detect acoustic reporters (i.e., gas-filled protein nanostructures) produced by rhizosphere bacteria [214]. While functioning of these reporters has yet to be demonstrated in rhizobacterial species, they have shown promise for in situ reporting within the mammalian microbiome [215].

5. Rhizobacterial Actuators

Actuator elements of engineered rhizobacteria drive agriculture- and sustainability-relevant phenotypes in colonized plants. In rhizobacterial circuits, these genes traditionally encode individual proteins or biosynthetic pathways that control nutrient acquisition, biotic stress resilience, and plant growth promotion (Table 2). More recently, rhizobacterial actuators have been proposed to address effects of climate change by improving plant resilience to abiotic stresses and augmenting carbon sequestration into soil. To maximize rhizobacterial effectiveness in these applications, circuit designers would benefit from knowing what actuators can be used, how they can be optimized, and how they interface with broader ecological and environmental processes (Figure 3(a)). In this section, we will highlight recent progress in these areas and emerging genes/pathways that can be leveraged for actuator engineering.


Actuation(s)Biosynthesized compounds and actuator gene(s)Regulation and notesBacterial chassis-host plantReference

Nitrogen fixationRefactored nif cluster from Klebsiella oxytocaIPTG- and aTc-inducible T7 RNAP controller regulated subdivided nif operonsKlebsiella oxytoca - n/a[60]
Nitrogen fixation62 member library of combinatorially assembled nif clusters from Klebsiella oxytocaVariable strength RBSs for each nif gene and an IPTG-inducible T7 RNAP controller for subdivided nif operonsKlebsiella oxytoca, Escherichia coli - n/a[220]
Nitrogen fixation12 native and engineered nif clusters from diverse bacterial orders (Enterobacterales, Pseudomondales, Rhizobiales, Rhodobacterales, Rhodospirillales, Bacillales, and Oscillatoriales)Native regulation; variable strength RBSs for each nif gene and an IPTG-inducible T7 RNAP that controlled expression of the nif master regulator, NifAAzorhizobium caulinodans, Rhizobium sp., Pseudomonas protegens - n/a[61]
Nitrogen fixationNative nif cluster with engineered postranslational regulation that increases ammonia secretionEngineered unidirectional adenyltransferase (uAT) inhibits glutamine synthetaseAzospirillum brasilense - Setaria viridis[221]
Nitrogen fixationNative nif cluster with engineered postranslational regulation that increases ammonia secretionMultiple copies of engineered uAT expressed to increase genetic redundancy and prolong BNF activityAzospirillum brasilense - Setaria viridis, maize (Zea mays)[222]
Phosphate/iron solubilizationCitric acid produced by expressing citrate synthase (gltA) and citrate transporter (citC)Constitutive lac promoter drove genome-integrated citrate operonPseudomonas fluorescens, Pseudomonas protegens - n/a[228]
Phosphate/iron solubilization2-Ketogluconic acid produced by expressing of gluconate dehydrogenase operon (gad)IPTG-inducible tac promoter and native gad operon promoterEnterobacter asburiae - n/a[230]
Phosphate/iron solubilizationGluconic acid and 2-ketogluconic acid produced by expressing PQQ cofactor synthesis gene cluster (pqq) and gad operonConstitutive lac promoterHerbaspirillum seropedicae - rice (Oryza sativa)[229]
Phosphate solubilization82 phytases from diverse bacteria were refactored for Proteobacteria expression and tested in 3 species to create 185-phytase expressing strainsIPTG-induciblePseudomonas putida, Pseudomonas simiae, Ralstonia sp. - Arabidopsis thaliana[233]
Biocontrol2,4-DAPG produced by heterologously expressing phlACBDE biosynthetic locusNative regulationPseudomonas fluorescens - wheat, tomato[253]
Biocontrol2,4-DAPG is overproduced by altering transcription of native phlACBD operonGenomic deletion of Pseudomonas sigma regulator gene (psrA) relieves phlACBD transcriptional repressionPseudomonas fluorescens - n/a[254]
Biocontrol2,4-DAPG is produced by heterologously expressing phlDACB gene clusterConstitutive lac promoterPseudomonas sp. - rice, sorghum, wheat[255]
BiocontrolIturin A lipopeptide overproduced by engineering promoter of native itu operonConstitutive strong promoter (C2up) place upstream of itu operonBacillus amyloliquefaciens - n/a[256]
Phosphate/iron solubilization, biocontrol, abiotic stress resiliencePhenazines overproduced by engineering the 5’ UTR of the native phz operonDeletion of a 90 bp region in the 5’ UTR of phz operon relieved transcriptional and translational repressionPseudomonas chlororaphis - wheat[258, 271]
Phosphate/iron solubilization, biocontrol, abiotic stress resiliencePhenazine-1-carboxamide (PCN) overproduced by engineering promoters of native phz2 gene cluster and glutamine aminotransferase (phzH)Native promoters of phz2 and phzH gene clusters replaced with strong quorum sensing- and thermo-regulated PrhlIPseudomonas aeruginosa - n/a[259]
Phosphate/iron solubilization, biocontrol, abiotic stress resiliencePhenazine N-oxides overproduced by heterologously expressing phenazine N-monooxygenase (naphzNO1) and monooxygenases (phzS or phzO)Native Pphz, PphzS, and PphzO promoters used to drive naphZNO1-phz, phzS, and phzO gene clusters, respectivelyPseudomonas chlororaphis - n/a[261]
Phosphate/iron solubilization, biocontrol, abiotic stress resiliencePCN overproduced by replacing chromsomal phzO gene with phzHphzH regulated by native promoter of phzO; several negative regulators of phz biosynthesis machinery and metabolite pool were deleted from genome (lon, rsmE, psrA, rpeA, parS, pykF)Pseudomonas chlororaphis - n/a[260]
Phosphate/iron solubilization, biocontrol, abiotic stress resiliencePhenazine-1,6-dicarboxylic acid (PDC) overproduced in 5 chassis hosts using 4 refactored PDC gene clusters that contain phzABG or homologsIPTG-inducible T7 RNAP controlled PDC gene cluster expressionPseudomonas simiae, Aeromonas salmonicida, Escherichia coli, Dickeya solani, Xenorhabdus doucetiae - n/a[268]
BiocontrolBacillus thuringiensis Cry toxin evolved to bind cabbage looper (Trichoplusia ni) cadherin-like receptor not bound by wild-type toxinProtein evolved using phage-assisted continuous evolution (PACE) over more than 500 generationsEscherichia coli - n/a[265]
Abiotic stress resilience/soil carbon sequestrationElevated trehalose production by overexpressing trehalose-6-phosphate synthase (otsA)Constitutive lac promoterRhizobium etli - common bean (Phaseolus vulgaris)[276]
Abiotic stress resilience/soil carbon sequestrationElevated trehalose production by heterologously expressing ReotsA or chimeric fusion of trehalose-6-phosphate synthase/trehalose-6-phosphate phosphatease (BIF) from Saccharomyces cerevisiaeConstitutive lac promoterAzospirillum brasilense - maize (Zea mays)[277]
Abiotic stress resilience/soil carbon sequestrationElevated trehalose production by heterologously expressing otsA and trehalose-6-phosphate phosphatase (otsB)Constitutive lac promoterPseudomonas putida - green pepper (Capsicum annum)[278]
Abiotic stress resilience/soil carbon sequestrationElevated trehalose production by overexpressing maltooligosyltrehalose synthase (treY) and maltooligosyltrehalose trehalohydrolase (treZ)IPTG-inducible tac promoterCorynebacterium glutamicum - n/a[311]
Abiotic stress resiliencePlant ethylene levels lowered by heterologously expressing a surface displayed ACC deaminase (inaK-N/acdS)Strong constitutive promoter from Enterobacter cloacae (fragment 132a/HQ834306)Enterobacter sp., Kosakonia sp. - rice[289]
Abiotic stress resilienceEthylene biosynthesis by heterologously ethylene forming enzyme (efe)Strong constitutive promoter (P1)Escherichia coli, Shewanella oneidensis, Bacillus thuringiensis - n/a[209]
Soil carbon sequestrationPoly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P(3HB)) produced by expressing (P(3HB)) synthesis operon (phaCAB) and succinate degradation pathway (orfZ, 4hbD, sucD)Native promoters used for phaCAB and orfZ, strong constitutive promoter (Ppdc) used for 4hbD-sucDEscherichia coli - n/a[306]
Soil carbon sequestrationGlycogen biosynthesis from native glg operonNative regulation, induced by raffinose/trehalose and repressed by glucoseLactobacillus acidophilus - n/a[310]
Soil carbon sequestrationIndole-3-acetic acid biosynthesis by expressing 2-tryptophan monooxygenase (iaaM) and indole-3-acetamide hydrolase (iaaH)Quorum sensing autoinducer production (luxI) regulates iaaMH placed downstream of PluxICupriavidus pinatubonensis - Arabidopsis thaliana[318]

5.1. Nitrogen Acquisition

Rhizobacteria can aid root acquisition of growth-limiting nutrients and potentially eliminate agricultural requirements for chemical fertilizers. Owing to the high energy and economic costs associated with synthetic nitrogen fertilizer [216], biological nitrogen fixation (BNF) has been a primary actuation targeted by rhizobacterial engineers [217]. BNF pathways are natively found in rhizobia that mutualistically colonize legume root nodules [218]. Although these pathways enable substantial delivery of fixed nitrogen (e.g., ammonium and amino acids) to legumes, rhizobial BNF activity is heavily restricted to within legume root nodules. This has motivated circuit designers to transfer nitrogen fixation (nif) pathways into bacteria that reside outside root nodules (e.g., free-living bacteria), as these engineered strains could deliver nitrogen to nodule-lacking crops, such as cereal grasses (Figure 3(b)) [219].

BNF circuit design has proven challenging due to limited BNF activity in different host backgrounds, heavy pathway regulation by environmental factors (e.g., NH3+ and O2 inhibition), and the large multigene footprint of nif pathways (11 kbp to 64 kbp). To decouple native nif regulation, key regulator genes (e.g., nifA) have been placed under the control of inducible orthogonal regulators, such as T7 RNAP. These synthetic controllers, coupled with entire pathway refactoring (i.e., reordering genetic elements to remove native regulation and enable designer control over individual gene expression), have optimized transcription and translation of nif pathways for new host chassis [60, 61, 220]. Recently, Schnabel and Sattely improved bacterial BNF activity by engineering a posttranslational step that regulates ammonia release from diazotrophic Azospirillum brasilense [221]. Under typical BNF conditions, glutamine synthetase (GS) siphons away generated ammonia to produce bioassimiliated glutamine. Since GS activity is regulated by a bidirectional adenyltransferase (AT), the group engineered a unidirectional adenyltransferase (uAT) that keeps GS inhibited and could elevate bacterial ammonia delivery to Setaria viridis. This system was further optimized by expressing multiple copies of uAT, which buffered against evolutionary instability from ammonia overproduction [222]. Genetic redundancy might also be applied with nif pathway components to prolong BNF activity in new hosts, as metabolic burden by the pathway (nitrogenase can account for 20% of cell mass) increases likelihood of circuit breakage.

5.2. Phosphate and Iron Solubilization

Rhizobacterial actuators can also assist plants in extracting key nutrients from soil. While phosphate and iron are terrestrially abundant, they are typically found within mineral precipitates or highly adsorbed to mineral surfaces [223, 224]. This limits their soil solubility and accessibility by plants, which necessitates exogenous application in agriculture. To decrease reliance on synthetic fertilizer and mitigate its environmental runoff effects, nutrient-solubilizing rhizobacteria have been used to promote phosphate and iron mobilization around roots (Figure 3(b)).

Many rhizobacterial chassis natively produce high amounts of organic acids and siderophores, which facilitate release of mineral-bound nutrients for plant uptake [225227]. Metabolic engineering in rhizobacteria can increase nutrient solubilization through expression of pathways that elevate secretion of citric acid, gluconic acid, and 2-keto-D-gluconic acid [228230]. Bacterial siderophore biosynthesis can also be genetically optimized [231, 232], but this has yet to be demonstrated in a rhizobacterial context. In a separate approach, Shulse et al. showed that circuits expressing 82 diverse phytases in P. putida, P. simiae, and Ralstonia sp. can enzymatically liberate Pi from organic phosphate (phytate) and increase its uptake by Arabidopsis [233]. Antibiotic metabolites produced by Pseudomonas spp., termed phenazines, can also increase Fe uptake by plants through reductive dissolution of minerals [234, 235]. Recently, McRose and Newman demonstrated that phenazines are capable of liberating mineral-bound phosphate within natural soils [236]. Given their proposed role as “keystone metabolites” of soil community structure [237, 238] and amenability to metabolic engineering [239], phenazine-producing circuits could be a promising means to improve iron/phosphate uptake by plants and augment existing phenazine pools in agricultural soils [240242].

5.3. Biotic Stress Resilience

Biocontrol is an important tool in agricultural pest/disease management and a major focus of rhizobacterial engineering [46, 243]. Biocontrol strains protect plants from insect herbivory and fungal- and bacterial-borne diseases, which minimizes the requirement for costly and environmentally impactful chemical pesticides. This is primarily accomplished through bacterial biosynthesis of biocontrol compounds, such as toxic proteins and secondary metabolites [244]. To optimize biocontrol activity and increase the host range of protected plants, biocontrol circuits can be engineered in native biocontrol strains or new rhizobacterial chassis (Figure 3(c)).

Genome mining in rhizobacteria has pinpointed many genetic determinants of biocontrol activity, with early progress focusing on model biocontrol strains from the Pseudomonas and Bacillus genera. Pseudomonas protegens Pf-5 was found to protect diverse plants (e.g., cotton, wheat, cucumber, and tomatoes) against soil-borne fungal/bacterial pathogens by possessing biosynthetic gene clusters for iron-chelating siderophores (e.g., pyoverdine and pyochelin), antifungals (e.g., 2,4-diacetylphloroglucinol/DAPG), and hydrogen cyanide [111, 245248]. Similarly, Bacillus velezensis FZB42 was shown to protect potato, wheat, and lettuce by encoding several antimicrobial polyketides (e.g., bacillaene and macrolactin) and antifungal lipopeptides (e.g., fengycin and bacillomycin D) [249252]. Identification of these pathways has enabled metabolic engineering approaches to overproduce target compounds within rhizobacteria [253256].

Antibiotic phenazines from Pseudomonas spp. have also served as biocontrol circuit outputs. These compounds are broadly produced by rhizosphere-dwelling pseudomonads and are thought to actuate microbial killing through ROS generation [257]. Increasing Pseudomonas phenazine titers is possible by engineering promoter and 5 UTR elements of phz biosynthetic pathways [258, 259]. Alternatively, combining phz components from different Pseudomonas spp. can tailor production towards phenazine derivatives with higher antibacterial activity, such as phenazine-1-carboxamide and phenazine N-oxide [260, 261].

Resistance to biocontrol agents is a mounting agricultural problem and has motivated identification of new insecticidal, antifungal, and antibiotic actuators. An example of this is with Bacillus thuringiensis (Bt): an important biocontrol agent that kills insect larvae by Cry/Cyt protein toxins [262]. These toxins bind to insect receptor proteins, which leads to formation of lethal pores in their cell membranes. Although Bt toxins are widely used in pest management, their field effectiveness has waned as insects acquire Bt resistance [263, 264]. Since Bt toxins are proteins, their insecticidal activity and susceptibility to resistance can potentially be optimized through protein engineering. To test this hypothesis, Badran et al. used a continuous evolution strategy to engineer Cry variants that bind a cabbage looper (Trichoplusia ni) receptor protein untargeted by the wild-type toxin [265]. Evolved toxins were 335-fold more potent against wild-type resistant insects and could possibly be expressed in new Bt strains. Advancements in high throughput sequencing, genetic engineering, and metabolite analysis methods have similarly increased the rate of discovery for novel biocontrol actuators [266, 267]. Wang et al. powerfully demonstrated this by using CRAGE to activate nine gene clusters from insect pathogens, Photorhabdus and Xenorhabdus, when screening for insecticidal metabolites [42]. Rapid genome engineering of 25 diverse Proteobacteria hosts, including a few rhizobacteria, allowed them to identify pathways that produced previously elusive metabolites. This same group used CRAGE to rapidly assay phenazine biosynthetic gene clusters and identify those that facilitate high titers of the bioactive phenazine derivative, phenazine-1,6-dicarboxylic acid [268]. While it is likely inevitable that individual biocontrol actuators will become obsolete as their targets acquire resistance, the modularity of rhizobacterial chassis and biosynthetic circuitry should enable replacement actuations to be rapidly deployed.

5.4. Abiotic Stress Resilience

Climate change-induced stresses, such as drought and high salinity, are becoming increasingly problematic within agricultural regions. These stresses devastate crop health and yields and are predicted to impact 50% of all arable land by 2050 [269]. In addition to other plant growth-promoting effects, rhizobacteria have demonstrated significant potential for improving plant resiliency to abiotic stresses. Bacteria can ameliorate stress through a combination of biosynthesized protective compounds, biofilm formation within the rhizosphere, and stress-priming modulation of root chemistry [270]. Despite the complex polygenic nature of stress protection traits, key genetic contributors have been identified in rhizobacterial species. As few genetic circuits have been built for the express purpose of abiotic stress protection, understanding these factors can clue us into useful genetic parts and circuit design rules (Figure 3(d)).

Adding to their multipurpose functionality, phenazines from Pseudomonas spp. have demonstrated an ability to improve plant drought and salinity tolerance. Mahmoudi et al. found that wheat colonized by phenazine-producing Pseudomonas chlororaphis 30-84 strains exhibited higher relative water content and improved survivability after periods of water deficit, relative to wheat colonized by a phenazine-null mutant and uninoculated controls [271]. Improved drought tolerance might have resulted from adapted root development, as phenazine production stimulated belowground growth and increased total root length, root surface area, and the number of root tips. The same group used these P. chlororaphis strains to assay phenazine effects on salinity stress with wheat and similarly observed that phenazine production generally lowered salt-induced ROS accumulation [272]. Although the exact mechanism for phenazine-based protection remains unclear, it is proposed that they directly impact plant functioning (e.g., inducing stress-response pathways), indirectly assist plants by modifying the rhizosphere environment (e.g., increasing bacterial abundance/biofilm formation), or both. These results suggest that existing phenazine biosynthesis circuits could be repurposed as actuators for drought and salinity tolerance within rhizobacterial chassis [259261].

Trehalose biosynthesis is another rhizobacterial actuation that confers colonized plants with drought and osmotic stress tolerance. Trehalose is a disaccharide composed of two glucose monomers that naturally accumulates under osmotic stress conditions in many bacteria, animals, and plants [273]. In addition to its protective capabilities, trehalose and its derivatives are important signaling molecules that regulate plant tissue sugar levels [274, 275]. Although rhizobacteria natively produce trehalose, its overaccumulation can be engineered to improve plant stress protection. Suarez et al. demonstrated that increasing trehalose biosynthesis in Rhizobium etli substantially improves the growth of common bean (Phaseolus vulgaris) during water deficit [276]. Constitutive plasmid-based expression of the R. etli trehalose-6-phosphate synthase (TPS) gene, otsA, increased plant survival and yields by over 50% relative to those inoculated with wild-type strains. Similarly, A. brasilense engineered to express a chimeric fusion of trehalose-6-phosphate synthase and trehalose-6-phosphate phosphatase (TPP) from Saccharomyces cerevisiae was able to increase survivability and root length of maize subjected to drought [277]. In another study, trehalose overaccumulation in P. putida was engineered by expressing otsA (TPS) and otsB (TPP) from desiccation-tolerant strain Microbacterium sp. 3J1 [278]. Under 14 days of no watering, this strain increased the dry weight and relative water content of green pepper up to 1.2-fold relative to wild-type inoculated plants. Like other biosynthesized protectant compounds, it remains unclear whether trehalose directly provides osmoprotection to plant cells or indirectly confers these benefits [279]. For example, rhizobacteria might be triggering trehalose-linked adaptive stress response pathways within plants [280].

Rhizobacterial circuits that improve biofilm formation might physiochemically buffer roots from abiotic stresses. Biofilm components, such as exopolysaccharides, can directly hydrate the rhizosphere and reduce movement of harmful Na+ ions from soils to roots [281283]. Drought tolerance of A. thaliana colonized by Bacillus amyloliquefaciens was shown to be dependent on biofilm formation genes, such as epsC and tasA [105, 284]. Biofilm-deficient mutants yielded plants with substantially lower survivability, biomass, and root development. Since many of these strains also exhibited impaired root colonization, biofilms could also indirectly influence cell density-dependent interactions with Arabidopsis, such as bacterial modulation of stress signaling pathways [105]. For example, many rhizobacteria promote plant growth under drought and salinity stress by disrupting ethylene signaling [285]. Ethylene is an important phytohormone that generally inhibits root growth and has shown to accumulate in plant tissues during abiotic stress [286]. Rhizobacteria that natively or heterologously express ACC deaminase, an enzyme that degrades the precursor to ethylene (ACC), can lower plant ethylene levels and lessen ethylene-mediated inhibition of growth [287, 288]. To improve activity of the typically intracellularly localized ACC deaminase, Liu et al. engineered a surface displayed variant of the acdS gene by translationally fusing it to the InaK membrane anchoring motif [289]. Expressing this variant in endophytic Enterobacter and Kosakonia increased germination rates up to 3-fold for colonized rice under high salinity (2.5% NaCl), relative to wild-type strains. Since ethylene can also positively impact plant salinity responses [290, 291], rhizobacterial stress protection circuits might leverage combinations of ethylene production [209] and ACC degradation to tune ethylene levels depending on plant species and environmental conditions.

5.5. Soil Carbon Sequestration

Increasing plant-mediated transfer of atmospheric carbon into soils can combat anthropogenic CO2 emissions and reverse the effects of climate change [292]. Soils are the largest terrestrial reservoir of carbon (ca. 3-fold more carbon in soils than the atmosphere) and receive photosynthetic inputs from root growth and exudation [293]. Root-derived carbon enters soil through the rhizosphere, where rhizobacterial communities play a significant role in stabilizing soil organic matter (SOM) pools [294, 295]. Rhizobacteria either assimilate carbon inputs into soil-stabilized biomass or respire these compounds as CO2 that is released back into the atmosphere. Elevating SOM levels is generally accomplished by increasing the formation of stable root and rhizobacterial biomass. This might occur through higher root growth/exudation and formation of carbon-rich root/bacterial biopolymers. Since rhizobacteria can actuate both these processes with biosynthetic pathways, rhizobacterial circuit design could be employed to optimize soil carbon sequestration (Figure 3(e)).

Despite significant variability in carbon use efficiencies and CO2 losses across microbial species [296, 297], microbe-derived biomass constitutes an enormous portion of soil carbon pools (up to 50% is composed of dead microbial cells) [298, 299]. Rhizobacterial circuits might further augment this balance by increasing the conversion of root exudates into biomass components with high soil residence times. For instance, many soil bacteria naturally generate long-lived storage molecules that serve as mobilizable energy reservoirs and protectants against environmental stresses [300]. As these storage compounds can make up to ca. 25% of cell dry mass, overexpressing relevant biosynthetic pathways in rhizobacterial chassis could enable creation of high SOM-forming strains. One target for biosynthesis may be polyhydroxyalkanoates (PHA), which are energy storage polyesters of many soil bacteria [301]. PHA exhibits a wide range of molecular structures and can improve starvation endurance of nitrogen-fixing root colonizers, such as A. brasilense and Sinorhizobium meliloti [302305]. Primary metabolism intermediates (e.g., acetyl-CoA) are converted into PHA polymers by expressing the pha or phb gene clusters, with PHA synthase (phaC) exerting high control over the specificity of polymerized PHA monomers [306308]. Glycogen is another high energy polymer that is catabolized by rhizobacteria under stress conditions [309]. Given the abundance of glycogen monomers in root exudates (i.e., glucose) [127], rhizobacteria may be ideal chassis for its hyperaccumulation by overexpressing the glg biosynthetic pathway [310]. While trehalose is a relatively small polymer (2-mer), it also serves as a bacterial storage molecule that could be produced at high titers by engineered circuitry [311]. Since biosynthesis of any storage molecule will likely impact rhizobacterial growth and root colonization capabilities, it will be critical to evaluate if engineered strains actuate a net increase of SOM in field settings.

Plant-based sequestration efforts have largely focused on optimizing crop root characteristics that deposit more carbon belowground [1, 2]. Carbon-storing ideotypes include crops with elevated root system biomass and deeper/steeper root architectures [312]. Modeling analyses predict that even a 2-fold increase in these traits on 90% of United States cropland could offset up to 60% of the country’s transportation emissions [313]. As a complement to plant engineering approaches, engineered rhizobacteria could actuate desired root phenotypes through targeted biosynthesis of phytohormones, such as auxin. Within root cells, the concentration of auxin/IAA regulates expression of genes that control cell division, differentiation, and elongation [314, 315]. Rhizobacteria that locally modulate these concentrations can alter overall features of root development [316, 317]. For example, Zúñiga et al. showed that a quorum sensing-based positive feedback circuit within Cupriavidus pinatubonensis could induce biosynthesis of IAA and increase primary root length and lateral root number of colonized A. thaliana [318]. Future circuits that target IAA biosynthesis to specific root regions might allow bacteria to more precisely control the design of root architectures [186].

Phytohormone biosynthesizing rhizobacteria could also be used to tailor root chemical compositions towards higher levels of long-lasting biomolecules. Lignin and suberin are proposed carbon sequestration targets [319], as these biopolymer components of root cell walls and nutrient diffusion barriers exhibit high recalcitrance to microbial/abiotic degradation [320, 321]. Despite their biotic stability in soils, their abundance within roots is substantially affected by rhizobacteria [322, 323]. Salas-González et al. demonstrated that, relative to axenic plant growth, bacterial colonization generally decreases endodermal suberization of A. thaliana roots by inhibiting endodermal abscisic acid (ABA) signaling. This suggests that rhizobacteria engineered to biosynthesize ABA could reverse signaling inhibition and increase suberin deposition within root tissues. While ABA biosynthesis has been observed by rhizobacterial isolates [324326], further understanding on the genetic basis of this actuation is required to design ABA-producing circuitry.

6. Challenges to Deploying Rhizobacterial Genetic Circuits

Although rhizobacterial genetic circuits have the potential to augment growth and functioning of agriculturally relevant plants, key technical, regulatory, and ethical challenges will need to be considered to apply circuit-containing bacteria outside the laboratory. As with any synthetic biology endeavor, rhizobacterial circuits will require genetic optimization for the specific bacterial strain and range of environmental conditions in which they are deployed. Sensory and biosynthetic pathways (e.g., nitrogen fixation) can perform poorly in non-native bacteria due to pathway refactoring-based loss of cryptic, yet important, regulation [60, 61]. Additionally, findings from engineered rhizobacteria prototyped in monoassociation with axenically grown plants may not directly translate to field experiments; circuit-carrying strains applied to environmental soil-grown plants will likely encounter diverse microbial communities that may alter their colonization dynamics and/or perform similar/competing functions (e.g., nutrient mobilization and phytohormone modulation) [327, 328]. These challenges underscore the need to build robust genetic toolkits in non-model rhizobacteria to facilitate learn-by-design optimization of individual pathways and strains. Improving genetic tractability across chassis with varying colonization patterns can expand the ability of circuit designers to direct pathway activity to developmentally and physiologically relevant regions of the root system, such as meristematic or high nutrient flux zones [329, 330]. Since maintaining bioinoculant colonization in the complex and competitive microbiome of plants remains a major hurdle [331], access to new chassis might also increase the persistence of deployed strains. This could occur by porting genetic circuitry into dominant bacterial taxa of target rhizospheres [48, 49, 332]. While all these approaches will require rigorous assessment in field conditions, they might enable engineered rhizobacteria to outcompete native microbiota and perform beneficial plant growth-modulating activity beyond what is naturally occurring.

Perhaps the foremost issue facing application of rhizobacterial circuitry is the limited public and political acceptance of deploying transgenic bacteria in the field, which has led to strict regulation over their release [333, 334]. While field implementation of transgenic plants is similarly constrained [335], genetically modified bacteria have elicited greater fears of strain escape from deployment sites (i.e., target crops) and horizontal gene transfer to environmental microbes. This has translated to protracted and expensive regulatory barriers, which many in the scientific community view as excessive given the posed risks [336, 337]. As transgenes are an inevitable necessity for building next-generation sensor-actuator rhizobacterial circuitry, it will be important to integrate input from government regulators, bioethicists, and ecologists to study the economic/environmental benefits and risks of engineered microbes and advise on the safe, responsible, and efficacious implementation of well-characterized rhizobacterial technologies [338, 339]. Public unease regarding horizontal gene transfer/strain escape might be ameliorated by engineering strains with biocontainment circuitry [340, 341] and modeling the environmental impact of strain/transgene deployment [99, 342, 343]. Biocontainment of transgenic strains might involve designing auxotrophies that require plant-produced metabolites for bacterial growth [344, 345] or utilizing environmentally triggered kill switches [346348]. Alternatively, strains with recoded genomes/nonstandard amino acid usage could render escaped transgenes non-functional outside the engineered host context [349351].

Despite these formidable challenges, the ability of bacterial circuits to perform value-added biochemistries has led to increased commercialization of rhizobacterial synthetic biology for agriculture [352]. Many companies have been founded with aims to engineer rhizobacteria for plant nutrient acquisition and stress tolerance [353]. For example, Pivot Bio recently reported a free-living nitrogen-fixing Klebsiella strain that can increase maize yields in field trials up to 3%, relative to untreated fields [354]. Although genome editing optimized nitrogen fixation by this strain, non-transgenic status was retained through deletion of native regulatory components that decouple nif expression from exogenous nitrogen presence/absence. As more sophisticated engineering endeavors continue to navigate regulatory landscapes, rhizobacterial circuitry can still find utility within industrial research settings to uncover commercial opportunities afforded by bacterial manipulation of root chemistry and growth. Incorporating genetic engineering design-build-test-learn cycles into biofertilizer product development can pinpoint desirable traits of non-modified bacteria that could be bioprospected from the environment and further tested in pre-commercialization phases (i.e., field trials) [355].

7. Conclusions

The last two decades have led to substantial progress in bacterial synthetic biology and our understanding of plant-bacteria interactions [329, 356]. Although advances in each field were generally independent of the other, tools for optimizing genetic circuit design and domesticating non-model chassis are now positioned to accelerate engineering of rhizosphere processes. Many of the separate sensor and actuator parts we describe could be connected to dynamically report and respond to changes in plant physiology. Modern rhizobacterial circuits might look like the recent attempts to engineer nitrogen-fixing nif pathways that are modularly induced by plant-released compounds, such as (rhizo)pines and salicylic acid [61, 357]. One could envision similar circuits that use root exudates (e.g., sucrose and phytohormones) to trigger actuations based on plant developmental stage and surrounding community composition [132]. To connect these sensing and actuation functions, it will be necessary to port previously built transcriptional logic gate machinery into new rhizobacterial chassis [358].

As advances are made in other bacterial fields, it is tempting to speculate how these innovations might translate into new rhizobacterial sensors and actuators. The intersection of synthetic biology with materials science is one burgeoning area, where functionalized living materials are fabricated by engineered bacteria [359, 360]. Circuit-biosynthesized materials, such as amyloid-based hydrogels and cellulosic biomass [361, 362], might provide a unique means to functionalize colonized root tissues and buffer plants against environmental stresses, such as drought. Materials formed by CO2-fixing rhizobacteria could similarly progress climate change mitigation by augmenting carbon capture into soils [25, 363]. Additionally, rhizobacterial circuitry could be co-opted to interact with pollutants and deleterious materials that plants encounter in the environment [364]. Bacterial pathways that degrade plastics and detoxify hazardous metals could be anchored within soil by root systems as robust remediation schemes [365368].

The next wave of synthetic biology is primed to utilize plant-associating hosts for genetic circuit design. While this review focused on designing circuits that function within rhizobacteria, rhizosphere-colonizing fungi (i.e., mycorrhizae) could also be genetically engineered to perform similar biological functions [369]. Their high prevalence in the rhizosphere [370] and mediation of key plant-environment interactions (e.g., nutrient acquisition and soil carbon storage) [371, 372] make mycorrhizae attractive engineering targets and should motivate increased genetic toolkit development. Alternatively, circuit design strategies could be adopted to engineer bacteria that colonize aboveground plant tissue compartments, known as the phyllosphere [373, 374]. Phyllosphere sensor-actuator circuits present their own unique opportunities for optimizing plant health and plant-environment interactions, such as enabling airborne pollutant remediation (i.e., phylloremediation) [375, 376]. Furthermore, plants themselves could be engineered to manipulate rhizobacterial circuits and functioning. For example, plant-produced transkingdom signaling molecules (e.g., rhizopines) were shown to trigger rhizobacterial sensor circuits and actuations [167, 357]. Alternatively, root exudates could be engineered to nutritionally tailor microbiota composition [377]. While it remains to be tested, root production of exotic carbon sources (e.g., algal polysaccharides) might give certain bacteria a growth advantage if these strains express carbohydrate-active enzymes necessary for metabolization [378]. This approach enabled engineered Bacteroides engraftment within the mouse gut microbiome [379] and could similarly be applied to improve strain persistence within the rhizosphere. These and other highlighted strategies demonstrate how genetic circuits can improve plant-bacteria interactions for agricultural and environmental sustainability. As engineered rhizobacteria move from the wet bench to the field, it will be exciting to see how plant-monitoring and -controlling circuits perform at scale.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding publication of this article.

Authors’ Contributions

CMD drafted the manuscript. CMD and JRD both planned and edited the manuscript.

Acknowledgments

CMD is supported by a postdoctoral fellowship from the TomKat Center for Sustainable Energy at Stanford University. The authors thank members of the Dinneny Lab for helpful comments during preparation of the manuscript. All figures were created with BioRender.com.

References

  1. D. B. Kell, “Large-scale sequestration of atmospheric carbon via plant roots in natural and agricultural ecosystems: why and how,” Philosophical Transactions of the Royal Society, B: Biological Sciences, vol. 367, no. 1595, pp. 1589–1597, 2012. View at: Publisher Site | Google Scholar
  2. X. Yang, D. Liu, H. Lu et al., “Biological parts for plant biodesign to enhance land-based carbon dioxide removal,” BioDesign Research, vol. 2021, article 9798714, pp. 1–22, 2021. View at: Publisher Site | Google Scholar
  3. D. J. Moellenbeck, M. L. Peters, J. W. Bing et al., “Insecticidal proteins from Bacillus thuringiensis protect corn from corn rootworms,” Nature Biotechnology, vol. 19, no. 7, pp. 668–672, 2001. View at: Publisher Site | Google Scholar
  4. R. Munns, R. A. James, B. Xu et al., “Wheat grain yield on saline soils is improved by an ancestral Na+ transporter gene,” Nature Biotechnology, vol. 30, no. 4, pp. 360–364, 2012. View at: Publisher Site | Google Scholar
  5. Y. Uga, K. Sugimoto, S. Ogawa et al., “Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions,” Nature Genetics, vol. 45, no. 9, pp. 1097–1102, 2013. View at: Publisher Site | Google Scholar
  6. M. A. Khan, D. C. Gemenet, and A. Villordon, “Root system architecture and abiotic stress tolerance: current knowledge in root and tuber crops,” Frontiers in Plant Science, vol. 7, p. 1584, 2016. View at: Publisher Site | Google Scholar
  7. J. P. Lynch, “Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems,” Annals of Botany, vol. 112, no. 2, pp. 347–357, 2013. View at: Publisher Site | Google Scholar
  8. J. A. N. Brophy, T. LaRue, and J. R. Dinneny, “Understanding and engineering plant form,” Seminars in Cell and Developmental Biology, vol. 79, pp. 68–77, 2018. View at: Publisher Site | Google Scholar
  9. O. de Lange, O. de Lange, E. Klavins, and J. Nemhauser, “Synthetic genetic circuits in crop plants,” Current Opinion in Biotechnology, vol. 49, pp. 16–22, 2018. View at: Publisher Site | Google Scholar
  10. M. S. Antunes, K. J. Morey, J. J. Smith et al., “Programmable ligand detection system in plants through a synthetic signal transduction pathway,” PLoS One, vol. 6, no. 1, article e16292, 2011. View at: Publisher Site | Google Scholar
  11. K. A. Schaumberg, M. S. Antunes, T. K. Kassaw et al., “Quantitative characterization of genetic parts and circuits for plant synthetic biology,” Nature Methods, vol. 13, no. 1, pp. 94–100, 2016. View at: Publisher Site | Google Scholar
  12. J. A. N. Brophy, K. J. Magallon, L. Duan et al., “Synthetic genetic circuits as a means of reprogramming plant roots,” Science, vol. 377, no. 6607, pp. 747–751, 2022. View at: Google Scholar
  13. J. P. B. Lloyd, F. Ly, P. Gong et al., “Synthetic memory circuits for stable cell reprogramming in plants,” Nature Biotechnology, 2022. View at: Publisher Site | Google Scholar
  14. J. A. N. Brophy and C. A. Voigt, “Principles of genetic circuit design,” Nature Methods, vol. 11, no. 5, pp. 508–520, 2014. View at: Publisher Site | Google Scholar
  15. H. Alper, C. Fischer, E. Nevoigt, and G. Stephanopoulos, “Tuning genetic control through promoter engineering,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 36, pp. 12678–12683, 2005. View at: Publisher Site | Google Scholar
  16. H. M. Salis, E. A. Mirsky, and C. A. Voigt, “Automated design of synthetic ribosome binding sites to control protein expression,” Nature Biotechnology, vol. 27, no. 10, pp. 946–950, 2009. View at: Publisher Site | Google Scholar
  17. J. R. Kelly, A. J. Rubin, J. H. Davis et al., “Measuring the activity of BioBrick promoters using an in vivo reference standard,” Journal of Biological Engineering, vol. 3, no. 1, p. 4, 2009. View at: Publisher Site | Google Scholar
  18. V. K. Mutalik, J. C. Guimaraes, G. Cambray et al., “Precise and reliable gene expression via standard transcription and translation initiation elements,” Nature Methods, vol. 10, no. 4, pp. 354–360, 2013. View at: Publisher Site | Google Scholar
  19. L. S. Qi, M. H. Larson, L. A. Gilbert et al., “Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression,” Cell, vol. 152, no. 5, pp. 1173–1183, 2013. View at: Publisher Site | Google Scholar
  20. R. Lutz and H. Bujard, “Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements,” Nucleic Acids Research, vol. 25, no. 6, pp. 1203–1210, 1997. View at: Publisher Site | Google Scholar
  21. J. J. Tabor, A. Levskaya, and C. A. Voigt, “Multichromatic Control of Gene Expression in Escherichia coli,” Journal of Molecular Biology, vol. 405, no. 2, pp. 315–324, 2011. View at: Publisher Site | Google Scholar
  22. S. R. Schmidl, F. Ekness, K. Sofjan et al., “Rewiring bacterial two-component systems by modular DNA-binding domain swapping,” Nature Chemical Biology, vol. 15, no. 7, pp. 690–698, 2019. View at: Publisher Site | Google Scholar
  23. E. J. Steen, Y. Kang, G. Bokinsky et al., “Microbial production of fatty-acid-derived fuels and chemicals from plant biomass,” Nature, vol. 463, no. 7280, pp. 559–562, 2010. View at: Publisher Site | Google Scholar
  24. H. M. Jensen, A. E. Albers, K. R. Malley et al., “Engineering of a synthetic electron conduit in living cells,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 45, pp. 19213–19218, 2010. View at: Publisher Site | Google Scholar
  25. S. Gleizer, R. Ben-Nissan, Y. M. Bar-On et al., “Conversion of Escherichia coli to Generate All Biomass Carbon from CO2,” Cell, vol. 179, no. 6, pp. 1255–1263.e12, 2019. View at: Publisher Site | Google Scholar
  26. Y. Yokobayashi, R. Weiss, and F. H. Arnold, “Directed evolution of a genetic circuit,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 26, pp. 16587–16591, 2002. View at: Publisher Site | Google Scholar
  27. A. Tamsir, J. J. Tabor, and C. A. Voigt, “Robust multicellular computing using genetically encoded NOR gates and chemical 'wires',” Nature, vol. 469, no. 7329, pp. 212–215, 2011. View at: Publisher Site | Google Scholar
  28. B. Wang, R. I. Kitney, N. Joly, and M. Buck, “Engineering modular and orthogonal genetic logic gates for robust digital- like synthetic biology,” Nature Communications, vol. 2, no. 1, p. 508, 2011. View at: Publisher Site | Google Scholar
  29. T. S. Moon, C. Lou, A. Tamsir, B. C. Stanton, and C. A. Voigt, “Genetic programs constructed from layered logic gates in single cells,” Nature, vol. 491, no. 7423, pp. 249–253, 2012. View at: Publisher Site | Google Scholar
  30. J. Bonnet, P. Yin, M. E. Ortiz, P. Subsoontorn, and D. Endy, “Amplifying genetic logic gates,” Science, vol. 340, no. 6132, pp. 599–603, 2013. View at: Publisher Site | Google Scholar
  31. B. C. Stanton, A. A. K. Nielsen, A. Tamsir, K. Clancy, T. Peterson, and C. A. Voigt, “Genomic mining of prokaryotic repressors for orthogonal logic gates,” Nature Chemical Biology, vol. 10, no. 2, pp. 99–105, 2014. View at: Publisher Site | Google Scholar
  32. A. A. K. Nielsen, B. S. Der, J. Shin et al., “Genetic circuit design automation,” Science, vol. 352, no. 6281, p. aac7341, 2016. View at: Publisher Site | Google Scholar
  33. T. S. Gardner, C. R. Cantor, and J. J. Collins, “Construction of a genetic toggle switch in Escherichia coli,” Nature, vol. 403, no. 6767, pp. 339–342, 2000. View at: Publisher Site | Google Scholar
  34. P. Siuti, J. Yazbek, and T. K. Lu, “Synthetic circuits integrating logic and memory in living cells,” Nature Biotechnology, vol. 31, no. 5, pp. 448–452, 2013. View at: Publisher Site | Google Scholar
  35. R. Daniel, J. R. Rubens, R. Sarpeshkar, and T. K. Lu, “Synthetic analog computation in living cells,” Nature, vol. 497, no. 7451, pp. 619–623, 2013. View at: Publisher Site | Google Scholar
  36. M. B. Elowitz and S. Leibler, “A synthetic oscillatory network of transcriptional regulators,” Nature, vol. 403, no. 6767, pp. 335–338, 2000. View at: Publisher Site | Google Scholar
  37. S. Basu, Y. Gerchman, C. H. Collins, F. H. Arnold, and R. Weiss, “A synthetic multicellular system for programmed pattern formation,” Nature, vol. 434, no. 7037, pp. 1130–1134, 2005. View at: Publisher Site | Google Scholar
  38. J. Stricker, S. Cookson, M. R. Bennett, W. H. Mather, L. S. Tsimring, and J. Hasty, “A fast, robust and tunable synthetic gene oscillator,” Nature, vol. 456, no. 7221, pp. 516–519, 2008. View at: Publisher Site | Google Scholar
  39. I. Kolinko, A. Lohße, S. Borg et al., “Biosynthesis of magnetic nanostructures in a foreign organism by transfer of bacterial magnetosome gene clusters,” Nature Nanotechnology, vol. 9, no. 3, pp. 193–197, 2014. View at: Publisher Site | Google Scholar
  40. E. Kalyoncu, R. E. Ahan, C. E. Ozcelik, and U. O. S. Seker, “Genetic logic gates enable patterning of amyloid nanofibers,” Advanced Materials, vol. 31, no. 39, article e1902888, 2019. View at: Publisher Site | Google Scholar
  41. J. A. N. Brophy, A. J. Triassi, B. L. Adams et al., “Engineered integrative and conjugative elements for efficient and inducible DNA transfer to undomesticated bacteria,” Nature Microbiology, vol. 3, no. 9, pp. 1043–1053, 2018. View at: Publisher Site | Google Scholar
  42. G. Wang, Z. Zhao, J. Ke et al., “CRAGE enables rapid activation of biosynthetic gene clusters in undomesticated bacteria,” Nature Microbiology, vol. 4, no. 12, pp. 2498–2510, 2019. View at: Publisher Site | Google Scholar
  43. B. E. Rubin, S. Diamond, B. F. Cress et al., “Species- and site-specific genome editing in complex bacterial communities,” Nature Microbiology, vol. 7, pp. 34–47, 2022. View at: Publisher Site | Google Scholar
  44. B. Lugtenberg and F. Kamilova, “Plant-growth-promoting rhizobacteria,” Annual Review of Microbiology, vol. 63, no. 1, pp. 541–556, 2009. View at: Publisher Site | Google Scholar
  45. J. Sasse, E. Martinoia, and T. Northen, “Feed your friends: do plant exudates shape the root microbiome?” Trends in Plant Science, vol. 23, no. 1, pp. 25–41, 2018. View at: Publisher Site | Google Scholar
  46. G. V. Bloemberg and B. J. Lugtenberg, “Molecular basis of plant growth promotion and biocontrol by rhizobacteria,” Current Opinion in Plant Biology, vol. 4, no. 4, pp. 343–350, 2001. View at: Publisher Site | Google Scholar
  47. J. K. Vessey, “Plant growth promoting rhizobacteria as biofertilizers,” Plant and Soil, vol. 255, no. 2, pp. 571–586, 2003. View at: Publisher Site | Google Scholar
  48. D. S. Lundberg, S. L. Lebeis, S. H. Paredes et al., “Defining the core Arabidopsis thaliana root microbiome,” Nature, vol. 488, no. 7409, pp. 86–90, 2012. View at: Publisher Site | Google Scholar
  49. D. Bulgarelli, M. Rott, K. Schlaeppi et al., “Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota,” Nature, vol. 488, no. 7409, pp. 91–95, 2012. View at: Publisher Site | Google Scholar
  50. G. Castrillo, P. J. P. L. Teixeira, S. H. Paredes et al., “Root microbiota drive direct integration of phosphate stress and immunity,” Nature, vol. 543, no. 7646, pp. 513–518, 2017. View at: Publisher Site | Google Scholar
  51. O. M. Finkel, I. Salas-González, G. Castrillo et al., “A single bacterial genus maintains root growth in a complex microbiome,” Nature, vol. 587, no. 7832, pp. 103–108, 2020. View at: Publisher Site | Google Scholar
  52. R. S. C. de Souza, J. S. L. Armanhi, and P. Arruda, “From microbiome to traits: designing synthetic microbial communities for improved crop resiliency,” Frontiers in Plant Science, vol. 11, p. 1179, 2020. View at: Publisher Site | Google Scholar
  53. J. Ke, B. Wang, and Y. Yoshikuni, “Microbiome engineering: synthetic biology of plant-associated microbiomes in sustainable agriculture,” Trends in Biotechnology, vol. 39, no. 3, pp. 244–261, 2021. View at: Publisher Site | Google Scholar
  54. T. L. Haskett, A. Tkacz, and P. S. Poole, “Engineering rhizobacteria for sustainable agriculture,” The ISME Journal, vol. 15, pp. 949–964, 2021. View at: Publisher Site | Google Scholar
  55. C. Jansson, C. Faiola, A. Wingler et al., “Crops for carbon farming,” Frontiers in Plant Science, vol. 12, article 636709, 2021. View at: Publisher Site | Google Scholar
  56. S. Cardinale and A. P. Arkin, “Contextualizing context for synthetic biology - identifying causes of failure of synthetic biological systems,” Biotechnology Journal, vol. 7, no. 7, pp. 856–866, 2012. View at: Publisher Site | Google Scholar
  57. H. Tas, L. Grozinger, R. Stoof, V. de Lorenzo, and Á. Goñi-Moreno, “Contextual dependencies expand the re-usability of genetic inverters,” Nature Communications, vol. 12, no. 1, pp. 1–9, 2021. View at: Publisher Site | Google Scholar
  58. A. V. Bryksin and I. Matsumura, “Rational design of a plasmid origin that replicates efficiently in both gram-positive and gram-negative bacteria,” PLoS One, vol. 5, no. 10, article e13244, 2010. View at: Publisher Site | Google Scholar
  59. I. Farasat, M. Kushwaha, J. Collens, M. Easterbrook, M. Guido, and H. M. Salis, “Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria,” Molecular Systems Biology, vol. 10, no. 6, p. 731, 2014. View at: Publisher Site | Google Scholar
  60. K. Temme, D. Zhao, and C. A. Voigt, “Refactoring the nitrogen fixation gene cluster from Klebsiella oxytoca,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 18, pp. 7085–7090, 2012. View at: Publisher Site | Google Scholar
  61. M.-H. Ryu, J. Zhang, T. Toth et al., “Control of nitrogen fixation in bacteria that associate with cereals,” Nature Microbiology, vol. 5, no. 2, pp. 314–330, 2020. View at: Publisher Site | Google Scholar
  62. Y. Liao, B. Wang, Y. Ye, and L. Pan, “Determination and optimization of a strong promoter element from Bacillus amyloliquefaciens by using a promoter probe vector,” Biotechnology Letters, vol. 40, no. 1, pp. 119–126, 2018. View at: Publisher Site | Google Scholar
  63. R. Münch, K. Hiller, A. Grote et al., “Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes,” Bioinformatics, vol. 21, no. 22, pp. 4187–4189, 2005. View at: Publisher Site | Google Scholar
  64. V. S. A. Salamov and A. Solovyevand, “Automatic annotation of microbial genomes and metagenomic sequences,” in Metagenomics and Its Applications in Agriculture, Biomedicine and Environmental Studies, R. W. Li, Ed., Nova Science Publishers, Inc., 2011. View at: Google Scholar
  65. T. La Fleur, A. Hossain, and H. M. Salis, Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria, bioRxiv, 2021.
  66. C. C. Liu, M. C. Jewett, J. W. Chin, and C. A. Voigt, “Toward an orthogonal central dogma,” Nature Chemical Biology, vol. 14, no. 2, pp. 103–106, 2018. View at: Publisher Site | Google Scholar
  67. B. Wang, Z. Zhao, L. K. Jabusch et al., “CRAGE-Duet facilitates modular assembly of biological systems for studying plant–microbe interactions,” ACS Synthetic Biology, vol. 9, no. 9, pp. 2610–2615, 2020. View at: Publisher Site | Google Scholar
  68. T. H. Segall-Shapiro, A. J. Meyer, A. D. Ellington, E. D. Sontag, and C. A. Voigt, “A “resource allocator” for transcription based on a highly fragmented T7 RNA polymerase,” Molecular Systems Biology, vol. 10, no. 7, p. 742, 2014. View at: Publisher Site | Google Scholar
  69. A. J. Meyer, J. W. Ellefson, and A. D. Ellington, “Directed evolution of a panel of orthogonal T7 RNA polymerase variants forin Vivoorin VitroSynthetic circuitry,” ACS Synthetic Biology, vol. 4, no. 10, pp. 1070–1076, 2015. View at: Publisher Site | Google Scholar
  70. M. Kushwaha and H. M. Salis, “A portable expression resource for engineering cross-species genetic circuits and pathways,” Nature Communications, vol. 6, no. 1, p. 7832, 2015. View at: Publisher Site | Google Scholar
  71. S. Kar and A. D. Ellington, “Construction of synthetic T7 RNA polymerase expression systems,” Methods, vol. 143, pp. 110–120, 2018. View at: Publisher Site | Google Scholar
  72. A. Hui and H. A. de Boer, “Specialized ribosome system: preferential translation of a single mRNA species by a subpopulation of mutated ribosomes in Escherichia coli,” Proceedings of the National Academy of Sciences of the United States of America, vol. 84, no. 14, pp. 4762–4766, 1987. View at: Publisher Site | Google Scholar
  73. O. Rackham and J. W. Chin, “A network of orthogonal ribosome mRNA pairs,” Nature Chemical Biology, vol. 1, no. 3, pp. 159–166, 2005. View at: Publisher Site | Google Scholar
  74. E. D. Carlson, A. E. d’Aquino, D. S. Kim et al., “Engineered ribosomes with tethered subunits for expanding biological function,” Nature Communications, vol. 10, no. 1, p. 3920, 2019. View at: Publisher Site | Google Scholar
  75. F. Radford, S. D. Elliott, A. Schepartz, and F. J. Isaacs, “Targeted editing and evolution of engineered ribosomes in vivo by filtered editing,” Nature Communications, vol. 13, no. 1, pp. 1–13, 2022. View at: Publisher Site | Google Scholar
  76. E. Martínez-García, I. Benedetti, A. Hueso, and V. De Lorenzo, “Mining environmental plasmids for synthetic biology parts and devices,” Microbiology Spectrum, vol. 3, no. 1, 2015. View at: Publisher Site | Google Scholar
  77. D. K. Summers, “The kinetics of plasmid loss,” Trends in Biotechnology, vol. 9, no. 1, pp. 273–278, 1991. View at: Publisher Site | Google Scholar
  78. S. P. Leonard, J. Perutka, J. E. Powell et al., “Genetic engineering of bee gut microbiome bacteria with a toolkit for modular assembly of broad-host-range plasmids,” ACS Synthetic Biology, vol. 7, no. 5, pp. 1279–1290, 2018. View at: Publisher Site | Google Scholar
  79. B. A. Geddes, M. A. Mendoza-Suárez, and P. S. Poole, “A bacterial expression vector archive (BEVA) for flexible modular assembly of golden gate-compatible vectors,” Frontiers in Microbiology, vol. 9, p. 3345, 2019. View at: Publisher Site | Google Scholar
  80. R. Silva-Rocha, E. Martínez-García, B. Calles et al., “The Standard European Vector Architecture (SEVA): a coherent platform for the analysis and deployment of complex prokaryotic phenotypes,” Nucleic Acids Research, vol. 41, no. D1, pp. D666–D675, 2013. View at: Publisher Site | Google Scholar
  81. E. Martínez-García, T. Aparicio, A. Goñi-Moreno, S. Fraile, and V. de Lorenzo, “SEVA 2.0: an update of the Standard European Vector Architecture for de-/re-construction of bacterial functionalities,” Nucleic Acids Research, vol. 43, pp. D1183–D1189, 2015. View at: Publisher Site | Google Scholar
  82. E. Martínez-García, A. Goñi-Moreno, B. Bartley et al., “SEVA 3.0: an update of the Standard European Vector Architecture for enabling portability of genetic constructs among diverse bacterial hosts,” Nucleic Acids Research, vol. 48, pp. D1164–D1170, 2020. View at: Publisher Site | Google Scholar
  83. R. Meyer, “Replication and conjugative mobilization of broad host-range IncQ plasmids,” Plasmid, vol. 62, no. 2, pp. 57–70, 2009. View at: Publisher Site | Google Scholar
  84. A. Jain and P. Srivastava, “Broad host range plasmids,” FEMS Microbiology Letters, vol. 348, no. 2, pp. 87–96, 2013. View at: Publisher Site | Google Scholar
  85. R. C. Roberts and D. R. Helinski, “Definition of a minimal plasmid stabilization system from the broad-host-range plasmid RK2,” Journal of Bacteriology, vol. 174, no. 24, pp. 8119–8132, 1992. View at: Publisher Site | Google Scholar
  86. M. Weinstein, R. C. Roberts, and D. R. Helinski, “A region of the broad-host-range plasmid RK2 causes stable in planta inheritance of plasmids in Rhizobium meliloti cells isolated from alfalfa root nodules,” Journal of Bacteriology, vol. 174, no. 22, pp. 7486–7489, 1992. View at: Publisher Site | Google Scholar
  87. C. L. Easter, H. Schwab, and D. R. Helinski, “Role of the parCBA operon of the broad-host-range plasmid RK2 in stable plasmid maintenance,” Journal of Bacteriology, vol. 180, no. 22, pp. 6023–6030, 1998. View at: Publisher Site | Google Scholar
  88. F. Hayes, “Toxins-antitoxins: plasmid maintenance, programmed cell death, and cell cycle arrest,” Science, vol. 301, no. 5639, pp. 1496–1499, 2003. View at: Publisher Site | Google Scholar
  89. B. Shao, J. Rammohan, D. A. Anderson, N. Alperovich, D. Ross, and C. A. Voigt, “Single-cell measurement of plasmid copy number and promoter activity,” Nature Communications, vol. 12, no. 1, pp. 1–9, 2021. View at: Publisher Site | Google Scholar
  90. A. J. Link, D. Phillips, and G. M. Church, “Methods for generating precise deletions and insertions in the genome of wild-type Escherichia coli: application to open reading frame characterization,” Journal of Bacteriology, vol. 179, no. 20, pp. 6228–6237, 1997. View at: Publisher Site | Google Scholar
  91. K.-H. Choi, J. B. Gaynor, K. G. White et al., “A Tn 7-based broad-range bacterial cloning and expression system,” Nature Methods, vol. 2, no. 6, pp. 443–448, 2005. View at: Publisher Site | Google Scholar
  92. J. Ke, D. Robinson, Z.-Y. Wu et al., “CRAGE-CRISPR facilitates rapid activation of secondary metabolite biosynthetic gene clusters in bacteria,” Cell Chemical Biology, vol. 29, no. 4, pp. 696–710.e4, 2022. View at: Publisher Site | Google Scholar
  93. S. E. Klompe, P. L. H. Vo, T. S. Halpin-Healy, and S. H. Sternberg, “Transposon-encoded CRISPR-Cas systems direct RNA-guided DNA integration,” Nature, vol. 571, no. 7764, pp. 219–225, 2019. View at: Publisher Site | Google Scholar
  94. J. Strecker, A. Ladha, Z. Gardner et al., “RNA-guided DNA insertion with CRISPR-associated transposases,” Science, vol. 365, no. 6448, pp. 48–53, 2019. View at: Publisher Site | Google Scholar
  95. P. L. H. Vo, C. Ronda, S. E. Klompe et al., “CRISPR RNA-guided integrases for high-efficiency, multiplexed bacterial genome engineering,” Nature Biotechnology, vol. 39, no. 4, pp. 480–489, 2021. View at: Publisher Site | Google Scholar
  96. P. Yu and F. Hochholdinger, “The role of host genetic signatures on root–microbe interactions in the rhizosphere and endosphere,” Frontiers in Plant Science, vol. 9, 2018. View at: Publisher Site | Google Scholar
  97. T. Galindo-Castañeda, J. P. Lynch, J. Six, and M. Hartmann, “Improving soil resource uptake by plants through capitalizing on synergies between root architecture and anatomy and root-associated microorganisms,” Frontiers in Plant Science, vol. 13, article 827369, 2022. View at: Publisher Site | Google Scholar
  98. Z. Yan, M. S. Reddy, and J. W. Kloepper, “Survival and colonization of rhizobacteria in a tomato transplant system,” Canadian Journal of Microbiology, vol. 49, no. 6, pp. 383–389, 2003. View at: Publisher Site | Google Scholar
  99. N. S. Strigul and L. V. Kravchenko, “Mathematical modeling of PGPR inoculation into the rhizosphere,” Environmental Modelling & Software, vol. 21, pp. 1158–1171, 2006. View at: Publisher Site | Google Scholar
  100. H. Massalha, E. Korenblum, S. Malitsky, O. H. Shapiro, and A. Aharoni, “Live imaging of root-bacteria interactions in a microfluidics setup,” Proceedings of the National Academy of Sciences of the United States of America, vol. 114, pp. 4549–4554, 2017. View at: Publisher Site | Google Scholar
  101. B. A. Geddes, M.-H. Ryu, F. Mus et al., “Use of plant colonizing bacteria as chassis for transfer of N2-fixation to cereals,” Current Opinion in Biotechnology, vol. 32, pp. 216–222, 2015. View at: Publisher Site | Google Scholar
  102. C. M. J. Pieterse, R. L. Berendsen, R. de Jonge et al., “Pseudomonas simiae WCS417: star track of a model beneficial rhizobacterium,” Plant and Soil, vol. 461, no. 1-2, pp. 245–263, 2021. View at: Publisher Site | Google Scholar
  103. B. J. Cole, M. E. Feltcher, R. J. Waters et al., “Genome-wide identification of bacterial plant colonization genes,” PLoS Biology, vol. 15, no. 9, article e2002860, 2017. View at: Publisher Site | Google Scholar
  104. C. H. S. G. Meneses, L. F. M. Rouws, J. L. Simoes-Araujo, M. S. Vidal, and J. I. Baldani, “Exopolysaccharide production is required for biofilm formation and plant colonization by the nitrogen-fixing endophyte Gluconacetobacter diazotrophicus,” Molecular Plant-Microbe Interactions, vol. 24, no. 12, pp. 1448–1458, 2011. View at: Publisher Site | Google Scholar
  105. X. Lu, S.-F. Liu, L. Yue et al., “Epsc involved in the encoding of exopolysaccharides produced by Bacillus amyloliquefaciens FZB42 act to boost the drought tolerance of Arabidopsis thaliana,” International Journal of Molecular Sciences, vol. 19, no. 12, article 3795, 2018. View at: Publisher Site | Google Scholar
  106. M. J. C. Pel, A. J. H. van Dijken, B. W. Bardoel et al., “Pseudomonas syringae evades host immunity by degrading flagellin monomers with alkaline protease AprA,” Molecular Plant-Microbe Interactions, vol. 27, no. 7, pp. 603–610, 2014. View at: Publisher Site | Google Scholar
  107. E. Li, H. Zhang, H. Jiang et al., “Experimental-evolution-driven identification of Arabidopsis Rhizosphere competence genes in Pseudomonas protegens,” MBio, vol. 12, no. 3, article e0092721, 2021. View at: Publisher Site | Google Scholar
  108. E. Li, R. de Jonge, C. Liu et al., “Rapid evolution of bacterial mutualism in the plant rhizosphere,” Nature Communications, vol. 12, no. 1, p. 3829, 2021. View at: Publisher Site | Google Scholar
  109. Y. Lin, M. Alstrup, J. K. Y. Pang et al., “Adaptation of Bacillus thuringiensis to plant colonization affects differentiation and toxicity,” mSystems, vol. 6, no. 5, article e0086421, 2021. View at: Publisher Site | Google Scholar
  110. C. Blake, M. Nordgaard, G. Maróti, and Á. T. Kovács, “Diversification of Bacillus subtilis during experimental evolution on Arabidopsis thaliana and the complementarity in root colonization of evolved subpopulations,” Environmental Microbiology, vol. 23, no. 10, pp. 6122–6136, 2021. View at: Publisher Site | Google Scholar
  111. J. E. Loper, D. Y. Kobayashi, and I. T. Paulsen, “The genomic sequence of Pseudomonas fluorescens Pf-5: insights into biological control,” Phytopathology, vol. 97, no. 2, pp. 233–238, 2007. View at: Publisher Site | Google Scholar
  112. B. Fan, C. Wang, X. Song et al., “Bacillus velezensis FZB42 in 2018: The Gram-positive model strain for plant growth promotion and biocontrol,” Frontiers in Microbiology, vol. 9, p. 2491, 2018. View at: Publisher Site | Google Scholar
  113. S. Banerjee, K. Schlaeppi, and M. G. A. van der Heijden, “Keystone taxa as drivers of microbiome structure and functioning,” Nature Reviews Microbiology, vol. 16, no. 9, pp. 567–576, 2018. View at: Publisher Site | Google Scholar
  114. J. W. Kloepper, C.-M. Ryu, and S. Zhang, “Induced systemic resistance and promotion of plant growth byBacillusspp,” Phytopathology, vol. 94, no. 11, pp. 1259–1266, 2004. View at: Publisher Site | Google Scholar
  115. D. M. Weller, D. V. Mavrodi, J. A. van Pelt, C. M. J. Pieterse, L. C. van Loon, and P. A. H. M. Bakker, “Induced systemic resistance in Arabidopsis thaliana against Pseudomonas syringae pv. tomato by 2,4-diacetylphloroglucinol-producing Pseudomonas fluorescens,” Phytopathology, vol. 102, no. 4, pp. 403–412, 2012. View at: Publisher Site | Google Scholar
  116. C. H. Haney, B. S. Samuel, J. Bush, and F. M. Ausubel, “Associations with rhizosphere bacteria can confer an adaptive advantage to plants,” Nature Plants, vol. 1, no. 6, pp. 1–9, 2015. View at: Publisher Site | Google Scholar
  117. P. Beskrovnaya, R. A. Melnyk, Z. Liu et al., “Comparative genomics identified a genetic locus in plant-associated Pseudomonas spp. that is necessary for induced systemic susceptibility,” MBio, vol. 11, no. 3, 2020. View at: Publisher Site | Google Scholar
  118. M. J. Liao, M. O. Din, L. Tsimring, and J. Hasty, “Rock-paper-scissors: engineered population dynamics increase genetic stability,” Science, vol. 365, no. 6457, pp. 1045–1049, 2019. View at: Publisher Site | Google Scholar
  119. W. Jiang, X. Yang, F. Gu et al., “Construction of synthetic microbial ecosystems and the regulation of population proportion,” ACS Synthetic Biology, vol. 11, no. 2, pp. 538–546, 2022. View at: Publisher Site | Google Scholar
  120. A. Canarini, C. Kaiser, A. Merchant, A. Richter, and W. Wanek, “Root exudation of primary metabolites: mechanisms and their roles in plant responses to environmental stimuli,” Frontiers in Plant Science, vol. 10, p. 157, 2019. View at: Publisher Site | Google Scholar
  121. A. J. Meyer, T. H. Segall-Shapiro, E. Glassey, J. Zhang, and C. A. Voigt, “Escherichia coli "Marionette" strains with 12 highly optimized small- molecule sensors,” Nature Chemical Biology, vol. 15, no. 2, pp. 196–204, 2019. View at: Publisher Site | Google Scholar
  122. J. T. Lazar and J. J. Tabor, “Bacterial two-component systems as sensors for synthetic biology applications,” Current Opinion in Systems Biology, vol. 28, 2021. View at: Publisher Site | Google Scholar
  123. J. Ang, E. Harris, B. J. Hussey, R. Kil, and D. R. McMillen, “Tuning response curves for synthetic biology,” ACS Synthetic Biology, vol. 2, no. 10, pp. 547–567, 2013. View at: Publisher Site | Google Scholar
  124. A. A. Mannan, D. Liu, F. Zhang, and D. A. Oyarzún, “Fundamental design principles for transcription-factor-based metabolite biosensors,” ACS Synthetic Biology, vol. 6, no. 10, pp. 1851–1859, 2017. View at: Publisher Site | Google Scholar
  125. R. E. Rondon and C. J. Wilson, “Engineering a new class of anti-LacI transcription factors with alternate DNA recognition,” ACS Synthetic Biology, vol. 8, no. 2, pp. 307–317, 2019. View at: Publisher Site | Google Scholar
  126. B. P. Landry, R. Palanki, N. Dyulgyarov, L. A. Hartsough, and J. J. Tabor, “Phosphatase activity tunes two-component system sensor detection threshold,” Nature Communications, vol. 9, no. 1, p. 1433, 2018. View at: Publisher Site | Google Scholar
  127. I. Kraffczyk, G. Trolldenier, and H. Beringer, “Soluble root exudates of maize: influence of potassium supply and rhizosphere microorganisms,” Soil Biology and Biochemistry, vol. 16, no. 4, pp. 315–322, 1984. View at: Publisher Site | Google Scholar
  128. L. C. Carvalhais, P. G. Dennis, D. Fedoseyenko, M.-R. Hajirezaei, R. Borriss, and N. von Wirén, “Root exudation of sugars, amino acids, and organic acids by maize as affected by nitrogen, phosphorus, potassium, and iron deficiency,” Journal of Plant Nutrition and Soil Science, vol. 174, no. 1, pp. 3–11, 2011. View at: Publisher Site | Google Scholar
  129. Y.-L. Ruan, “Sucrose metabolism: gateway to diverse carbon use and sugar signaling,” Annual Review of Plant Biology, vol. 65, no. 1, pp. 33–67, 2014. View at: Publisher Site | Google Scholar
  130. C. H. Jaeger, S. E. Lindow, W. Miller, E. Clark, and M. K. Firestone, “Mapping of sugar and amino acid availability in soil around roots with bacterial sensors of sucrose and tryptophan,” Applied and Environmental Microbiology, vol. 65, no. 6, pp. 2685–2690, 1999. View at: Publisher Site | Google Scholar
  131. J. M. Chaparro, D. V. Badri, M. G. Bakker, A. Sugiyama, D. K. Manter, and J. M. Vivanco, “Root exudation of phytochemicals in Arabidopsis follows specific patterns that are developmentally programmed and correlate with soil microbial functions,” PLoS One, vol. 8, no. 2, article e55731, 2013. View at: Publisher Site | Google Scholar
  132. K. Zhalnina, K. B. Louie, Z. Hao et al., “Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly,” Nature Microbiology, vol. 3, no. 4, pp. 470–480, 2018. View at: Publisher Site | Google Scholar
  133. L. Kim, A. Mogk, and W. Schumann, “A xylose-inducible Bacillus subtilis integration vector and its application,” Gene, vol. 181, no. 1-2, pp. 71–76, 1996. View at: Publisher Site | Google Scholar
  134. W. G. Miller, M. T. Brandl, B. Quiñones, and S. E. Lindow, “Biological sensor for sucrose availability: relative sensitivities of various reporter genes,” Applied and Environmental Microbiology, vol. 67, no. 3, pp. 1308–1317, 2001. View at: Publisher Site | Google Scholar
  135. J. H. Leveau and S. E. Lindow, “Appetite of an epiphyte: quantitative monitoring of bacterial sugar consumption in the phyllosphere,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 6, pp. 3446–3453, 2001. View at: Publisher Site | Google Scholar
  136. F. Pini, A. K. East, C. Appia-Ayme et al., “Bacterial biosensors for in vivo spatiotemporal mapping of root secretion,” Plant Physiology, vol. 174, no. 3, pp. 1289–1306, 2017. View at: Publisher Site | Google Scholar
  137. H. Löwe, P. Sinner, A. Kremling, and K. Pflüger-Grau, “Engineering sucrose metabolism in Pseudomonas putida highlights the importance of porins,” Microbial Biotechnology, vol. 13, no. 1, pp. 97–106, 2020. View at: Publisher Site | Google Scholar
  138. D. T. Verhamme, P. W. Postma, W. Crielaard, and K. J. Hellingwerf, “Cooperativity in signal transfer through the Uhp system of Escherichia coli,” Journal of Bacteriology, vol. 184, no. 15, pp. 4205–4210, 2002. View at: Publisher Site | Google Scholar
  139. F. Moser, A. Espah Borujeni, A. N. Ghodasara, E. Cameron, Y. Park, and C. A. Voigt, “Dynamic control of endogenous metabolism with combinatorial logic circuits,” Molecular Systems Biology, vol. 14, article e8605, 2018. View at: Google Scholar
  140. Y.-Y. Wang, Y.-H. Cheng, K.-E. Chen, and Y.-F. Tsay, “Nitrate transport, signaling, and use efficiency,” Annual Review of Plant Biology, vol. 69, no. 1, pp. 85–122, 2018. View at: Publisher Site | Google Scholar
  141. T. Mahmood, M. Woitke, H. Gimmler, and W. M. Kaiser, “Sugar exudation by roots of kallar grass [Leptochloa fusca (L.) Kunth] is strongly affected by the nitrogen source,” Planta, vol. 214, no. 6, pp. 887–894, 2002. View at: Publisher Site | Google Scholar
  142. S. Henry, S. Texier, S. Hallet et al., “Disentangling the rhizosphere effect on nitrate reducers and denitrifiers: insight into the role of root exudates,” Environmental Microbiology, vol. 10, no. 11, pp. 3082–3092, 2008. View at: Publisher Site | Google Scholar
  143. C.-H. Sun, J.-Q. Yu, and D.-G. Hu, “Nitrate: a crucial signal during lateral roots development,” Frontiers in Plant Science, vol. 8, p. 485, 2017. View at: Publisher Site | Google Scholar
  144. K. M. DeAngelis, P. Ji, M. K. Firestone, and S. E. Lindow, “Two novel bacterial biosensors for detection of nitrate availability in the rhizosphere,” Applied and Environmental Microbiology, vol. 71, no. 12, pp. 8537–8547, 2005. View at: Publisher Site | Google Scholar
  145. P. J. Kiley and W. S. Reznikoff, “Fnr mutants that activate gene expression in the presence of oxygen,” Journal of Bacteriology, vol. 173, no. 1, pp. 16–22, 1991. View at: Publisher Site | Google Scholar
  146. T. Nohno, S. Noji, S. Taniguchi, and T. Saito, “The narX and narL genes encoding the nitrate-sensing regulators of Escherichia coli are homologous to a family of prokaryotic two-component regulatory genes,” Nucleic Acids Research, vol. 17, no. 8, pp. 2947–2957, 1989. View at: Publisher Site | Google Scholar
  147. I. Schröder, S. Darie, and R. P. Gunsalus, “Activation of the Escherichia coli nitrate reductase (narGHJI) operon by NarL and Fnr requires integration host factor,” Journal of Biological Chemistry, vol. 268, no. 2, pp. 771–774, 1993. View at: Publisher Site | Google Scholar
  148. S.-G. Woo, S.-J. Moon, S. K. Kim et al., “A designed whole-cell biosensor for live diagnosis of gut inflammation through nitrate sensing,” Biosensors and Bioelectronics, vol. 168, article 112523, 2020. View at: Publisher Site | Google Scholar
  149. F. Paynel, P. J. Murray, and J. Bernard Cliquet, “Root exudates: a pathway for short-term N transfer from clover and ryegrass,” Plant and Soil, vol. 229, no. 2, pp. 235–243, 2001. View at: Publisher Site | Google Scholar
  150. L. A. Moe, “Amino acids in the rhizosphere: from plants to microbes,” American Journal of Botany, vol. 100, no. 9, pp. 1692–1705, 2013. View at: Publisher Site | Google Scholar
  151. B. G. Forde, “Glutamate signalling in roots,” Journal of Experimental Botany, vol. 65, no. 3, pp. 779–787, 2014. View at: Publisher Site | Google Scholar
  152. H. Feng, N. Zhang, W. Du et al., “Identification of chemotaxis compounds in root exudates and their sensing chemoreceptors in plant-growth-promoting rhizobacteria Bacillus amyloliquefaciens SQR9,” Molecular Plant-Microbe Interactions, vol. 31, no. 10, pp. 995–1005, 2018. View at: Publisher Site | Google Scholar
  153. H. Feng, R. Fu, X. Hou et al., “Chemotaxis of beneficial rhizobacteria to root exudates: the first step towards root–microbe rhizosphere interactions,” International Journal of Molecular Sciences, vol. 22, no. 13, p. 6655, 2021. View at: Publisher Site | Google Scholar
  154. D. A. Phillips, T. C. Fox, and J. Six, “Root exudation (net efflux of amino acids) may increase rhizodeposition under elevated CO2,” Global Change Biology, vol. 12, no. 3, pp. 561–567, 2006. View at: Publisher Site | Google Scholar
  155. D. A. Phillips, T. C. Fox, M. D. King, T. V. Bhuvaneswari, and L. R. Teuber, “Microbial products trigger amino acid exudation from plant roots,” Plant Physiology, vol. 136, no. 1, pp. 2887–2894, 2004. View at: Publisher Site | Google Scholar
  156. M. I. Rubia, V. K. Ramachandran, C. Arrese-Igor, E. Larrainzar, and P. S. Poole, “A novel biosensor to monitor proline in pea root exudates and nodules under osmotic stress and recovery,” Plant and Soil, vol. 452, no. 1-2, pp. 413–422, 2020. View at: Publisher Site | Google Scholar
  157. S. Binder, G. Schendzielorz, N. Stäbler et al., “A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level,” Genome Biology, vol. 13, no. 5, p. R40, 2012. View at: Publisher Site | Google Scholar
  158. N. Mustafi, A. Grünberger, D. Kohlheyer, M. Bott, and J. Frunzke, “The development and application of a single-cell biosensor for the detection of l-methionine and branched-chain amino acids,” Metabolic Engineering, vol. 14, no. 4, pp. 449–457, 2012. View at: Publisher Site | Google Scholar
  159. B. R. Lundgren, J. M. Shoytush, R. A. Scheel, S. Sain, Z. Sarwar, and C. T. Nomura, “Utilization of L-glutamate as a preferred or sole nutrient in Pseudomonas aeruginosa PAO1 depends on genes encoding for the enhancer-binding protein AauR, the sigma factor RpoN and the transporter complex AatJQMP,” BMC Microbiology, vol. 21, no. 1, p. 83, 2021. View at: Publisher Site | Google Scholar
  160. L. J. Shaw, P. Morris, and J. E. Hooker, “Perception and modification of plant flavonoid signals by rhizosphere microorganisms,” Environmental Microbiology, vol. 8, no. 11, pp. 1867–1880, 2006. View at: Publisher Site | Google Scholar
  161. S. Siedler, S. G. Stahlhut, S. Malla, J. Maury, and A. R. Neves, “Novel biosensors based on flavonoid-responsive transcriptional regulators introduced into Escherichia coli,” Metabolic Engineering, vol. 21, pp. 2–8, 2014. View at: Publisher Site | Google Scholar
  162. B. De Paepe, J. Maertens, B. Vanholme, and M. De Mey, “Chimeric LysR-type transcriptional biosensors for customizing ligand specificity profiles toward flavonoids,” ACS Synthetic Biology, vol. 8, no. 2, pp. 318–331, 2019. View at: Publisher Site | Google Scholar
  163. I. Del Valle, T. M. Webster, H.-Y. Cheng et al., “Soil organic matter attenuates the efficacy of flavonoid-based plant-microbe communication,” Science Advances, vol. 6, no. 5, p. eaax8254, 2020. View at: Publisher Site | Google Scholar
  164. R. A. Dixon and C. L. Steele, “Flavonoids and isoflavonoids – a gold mine for metabolic engineering,” Trends in Plant Science, vol. 4, no. 10, pp. 394–400, 1999. View at: Publisher Site | Google Scholar
  165. P. Oger, A. Petit, and Y. Dessaux, “Genetically engineered plants producing opines alter their biological environment,” Nature Biotechnology, vol. 15, no. 4, pp. 369–372, 1997. View at: Publisher Site | Google Scholar
  166. S. Mondy, A. Lenglet, A. Beury-Cirou et al., “An increasing opine carbon bias in artificial exudation systems and genetically modified plant rhizospheres leads to an increasing reshaping of bacterial populations,” Molecular Ecology, vol. 23, no. 19, pp. 4846–4861, 2014. View at: Publisher Site | Google Scholar
  167. B. A. Geddes, P. Paramasivan, A. Joffrin et al., “Engineering transkingdom signalling in plants to control gene expression in rhizosphere bacteria,” Nature Communications, vol. 10, no. 1, p. 3430, 2019. View at: Publisher Site | Google Scholar
  168. S. Subramoni, N. Nathoo, E. Klimov, and Z.-C. Yuan, “Agrobacterium tumefaciens responses to plant-derived signaling molecules,” Frontiers in Plant Science, vol. 5, p. 322, 2014. View at: Google Scholar
  169. J. T. Sexton and J. J. Tabor, “Multiplexing cell-cell communication,” Molecular Systems Biology, vol. 16, article e9618, 2020. View at: Publisher Site | Google Scholar
  170. W. E. Huang, H. Wang, H. Zheng et al., “Chromosomally located gene fusions constructed in Acinetobacter sp. ADP1 for the detection of salicylate,” Environmental Microbiology, vol. 7, no. 9, pp. 1339–1348, 2005. View at: Publisher Site | Google Scholar
  171. Y. Zhao, “Auxin biosynthesis and its role in plant development,” Annual Review of Plant Biology, vol. 61, no. 1, pp. 49–64, 2010. View at: Publisher Site | Google Scholar
  172. S. Spaepen and J. Vanderleyden, “Auxin and plant-microbe interactions,” Cold Spring Harbor Perspectives in Biology, vol. 3, 2011. View at: Publisher Site | Google Scholar
  173. M. Lambrecht, Y. Okon, A. V. Broek, and J. Vanderleyden, “Indole-3-acetic acid: a reciprocal signalling molecule in bacteria-plant interactions,” Trends in Microbiology, vol. 8, no. 7, pp. 298–300, 2000. View at: Publisher Site | Google Scholar
  174. T. Ulmasov, J. Murfett, G. Hagen, and T. J. Guilfoyle, “Aux/IAA proteins repress expression of reporter genes containing natural and highly active synthetic auxin response elements,” The Plant Cell, vol. 9, no. 11, pp. 1963–1971, 1997. View at: Publisher Site | Google Scholar
  175. O. Herud-Sikimić, A. C. Stiel, M. Kolb et al., “A biosensor for the direct visualization of auxin,” Nature, vol. 592, no. 7856, pp. 768–772, 2021. View at: Publisher Site | Google Scholar
  176. C. Zhao, A. Yaschenko, J. M. Alonso, and A. N. Stepanova, “Leveraging synthetic biology approaches in plant hormone research,” Current Opinion in Plant Biology, vol. 60, article 101998, 2021. View at: Publisher Site | Google Scholar
  177. S. Simon and J. Petrášek, “Why plants need more than one type of auxin,” Plant Science, vol. 180, no. 3, pp. 454–460, 2011. View at: Publisher Site | Google Scholar
  178. H.-Y. Shu, L.-C. Lin, T.-K. Lin et al., “Transcriptional regulation of the iac locus from Acinetobacter baumannii by the phytohormone indole-3-acetic acid,” Antonie Van Leeuwenhoek, vol. 107, no. 5, pp. 1237–1247, 2015. View at: Publisher Site | Google Scholar
  179. R. Donoso, P. Leiva-Novoa, A. Zúñiga, T. Timmermann, G. Recabarren-Gajardo, and B. González, “Biochemical and genetic bases of indole-3-acetic acid (auxin phytohormone) degradation by the plant-growth-promoting Rhizobacterium Paraburkholderia phytofirmans PsJN,” Applied and Environmental Microbiology, vol. 83, no. 1, 2017. View at: Publisher Site | Google Scholar
  180. I. V. Greenhut, B. L. Slezak, and J. H. J. Leveau, “iac Gene expression in the indole-3-acetic acid-degrading soil bacterium Enterobacter soli LF7,” Applied and Environmental Microbiology, vol. 84, no. 19, 2018. View at: Publisher Site | Google Scholar
  181. J. Wang, C. Zhang, and W. S. Childers, “A biosensor for detection of indole metabolites,” ACS Synthetic Biology, vol. 10, no. 7, pp. 1605–1614, 2021. View at: Publisher Site | Google Scholar
  182. S. Sugawara, K. Mashiguchi, K. Tanaka et al., “Distinct characteristics of indole-3-acetic acid and phenylacetic acid, two common auxins in plants,” Plant and Cell Physiology, vol. 56, no. 8, pp. 1641–1654, 2015. View at: Publisher Site | Google Scholar
  183. S. D. Cook, “An historical review of phenylacetic acid,” Plant and Cell Physiology, vol. 60, no. 2, pp. 243–254, 2019. View at: Publisher Site | Google Scholar
  184. S. Dierckx, S. Van Puyvelde, L. Venken, W. Eberle, and J. Vanderleyden, “Design and construction of a whole cell bacterial 4-hydroxyphenylacetic acid and 2-phenylacetic acid bioassay,” Frontiers in Bioengineering and Biotechnology, vol. 3, p. 88, 2015. View at: Publisher Site | Google Scholar
  185. K.-H. Guo, K.-H. Lu, and Y.-C. Yeh, “Cell-based biosensor with dual signal outputs for simultaneous quantification of phenylacetic acid and phenylethylamine,” ACS Synthetic Biology, vol. 7, no. 12, pp. 2790–2795, 2018. View at: Publisher Site | Google Scholar
  186. J. A. N. Brophy, “Toward synthetic plant development,” Plant Physiology, vol. 188, no. 2, pp. 738–748, 2022. View at: Publisher Site | Google Scholar
  187. M. Schaechter, O. Maaloe, and N. O. Kjeldgaard, “Dependency on medium and temperature of cell size and chemical composition during balanced grown of Salmonella typhimurium,” Journal of General Microbiology, vol. 19, no. 3, pp. 592–606, 1958. View at: Publisher Site | Google Scholar
  188. J. Forchhammer and L. Lindahl, “Growth rate of polypeptide chains as a function of the cell growth rate in a mutant of Escherichia coli 15,” Journal of Molecular Biology, vol. 55, no. 3, pp. 563–568, 1971. View at: Publisher Site | Google Scholar
  189. M. Scott, C. W. Gunderson, E. M. Mateescu, Z. Zhang, and T. Hwa, “Interdependence of cell growth and gene expression: origins and consequences,” Science, vol. 330, no. 6007, pp. 1099–1102, 2010. View at: Publisher Site | Google Scholar
  190. M. S. Bartlett and R. L. Gourse, “Growth rate-dependent control of the rrnB P1 core promoter in Escherichia coli,” Journal of Bacteriology, vol. 176, no. 17, pp. 5560–5564, 1994. View at: Publisher Site | Google Scholar
  191. M. V. Brennerova and D. E. Crowley, “Direct detection of rhizosphere-colonizing Pseudomonas sp. using an Escherichia coli rRNA promoter in a Tn7-lux system,” FEMS Microbiology Ecology, vol. 14, no. 4, pp. 319–330, 1994. View at: Publisher Site | Google Scholar
  192. C. Ramos, L. Mølbak, and S. Molin, “Bacterial activity in the rhizosphere analyzed at the single-cell level by monitoring ribosome contents and synthesis rates,” Applied and Environmental Microbiology, vol. 66, no. 2, pp. 801–809, 2000. View at: Publisher Site | Google Scholar
  193. T. S. Boldt, J. Sørensen, U. Karlson, S. Molin, and C. Ramos, “Combined use of different Gfp reporters for monitoring single-cell activities of a genetically modified PCB degrader in the rhizosphere of alfalfa,” FEMS Microbiology Ecology, vol. 48, no. 2, pp. 139–148, 2004. View at: Publisher Site | Google Scholar
  194. S. J. Blazewicz, R. L. Barnard, R. A. Daly, and M. K. Firestone, “Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses,” The ISME Journal, vol. 7, no. 11, pp. 2061–2068, 2013. View at: Publisher Site | Google Scholar
  195. C. Liao, A. E. Blanchard, and T. Lu, “An integrative circuit-host modelling framework for predicting synthetic gene network behaviours,” Nature Microbiology, vol. 2, no. 12, pp. 1658–1666, 2017. View at: Publisher Site | Google Scholar
  196. L. C. Dekkers, A. J. van der Bij, I. H. Mulders et al., “Role of the O-antigen of lipopolysaccharide, and possible roles of growth rate and of NADH:ubiquinone oxidoreductase (nuo) in competitive tomato root-tip colonization by Pseudomonas fluorescens WCS365,” Molecular Plant-Microbe Interactions, vol. 11, no. 8, pp. 763–771, 1998. View at: Publisher Site | Google Scholar
  197. D. Brekasis and M. S. B. Paget, “A novel sensor of NADH/NAD+ redox poise in Streptomyces coelicolor A3(2),” The EMBO Journal, vol. 22, no. 18, pp. 4856–4865, 2003. View at: Publisher Site | Google Scholar
  198. Y. Liu, R. Landick, and S. Raman, “A regulatory NADH/NAD+ redox biosensor for bacteria,” ACS Synthetic Biology, vol. 8, no. 2, pp. 264–273, 2019. View at: Publisher Site | Google Scholar
  199. T. Franzino, H. Boubakri, T. Cernava et al., “Implications of carbon catabolite repression for plant-microbe interactions,” Plant Communications, vol. 3, no. 2, p. 100272, 2022. View at: Publisher Site | Google Scholar
  200. P. de Philip, J. Batut, and P. Boistard, “Rhizobium meliloti Fix L is an oxygen sensor and regulates R. meliloti nifA and fixK genes differently in Escherichia coli,” Journal of Bacteriology, vol. 172, no. 8, pp. 4255–4262, 1990. View at: Publisher Site | Google Scholar
  201. O. Højberg, U. Schnider, H. V. Winteler, J. Sørensen, and D. Haas, “Oxygen-sensing reporter strain of Pseudomonas fluorescens for monitoring the distribution of low-oxygen habitats in soil,” Applied and Environmental Microbiology, vol. 65, no. 9, pp. 4085–4093, 1999. View at: Publisher Site | Google Scholar
  202. G. V. Bloemberg, A. H. M. Wijfjes, G. E. M. Lamers, N. Stuurman, and B. J. J. Lugtenberg, “Simultaneous imaging of Pseudomonas fluorescens WCS365 populations expressing three different autofluorescent proteins in the rhizosphere: new perspectives for studying microbial communities,” Molecular Plant-Microbe Interactions, vol. 13, no. 11, pp. 1170–1176, 2000. View at: Publisher Site | Google Scholar
  203. Y. Liu, D. Patko, and I. Engelhardt, “Plant–environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere,” Proceedings of the National Academy of Sciences, vol. 118, no. 48, 2021. View at: Publisher Site | Google Scholar
  204. C. Y. Jones, I. Engelhardt, D. Patko, L. Dupuy, N. Holden, and W. G. T. Willats, “High-resolution 3D mapping of rhizosphere glycan patterning using molecular probes in a transparent soil system,” The Cell Surface, vol. 7, article 100059, 2021. View at: Publisher Site | Google Scholar
  205. J. I. Prosser, K. Killham, L. A. Glover, and E. A. S. Rattray, “Luminescence-based systems for detection of bacteria in the environment,” Critical Reviews in Biotechnology, vol. 16, no. 2, pp. 157–183, 1996. View at: Publisher Site | Google Scholar
  206. R. Rellán-Álvarez, G. Lobet, H. Lindner et al., “GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems,” eLife, vol. 4, 2015. View at: Publisher Site | Google Scholar
  207. J. Sebastian, M.-C. Yee, W. Goudinho Viana et al., “Grasses suppress shoot-borne roots to conserve water during drought,” Proceedings of the National Academy of Sciences of the United States United, vol. 113, no. 31, pp. 8861–8866, 2016. View at: Publisher Site | Google Scholar
  208. T. LaRue, H. Lindner, A. Srinivas, M. Exposito-Alonso, G. Lobet, and J. R. Dinneny, “Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform,” bioRxiv, 2021. View at: Publisher Site | Google Scholar
  209. H.-Y. Cheng, C. A. Masiello, I. Del Valle, X. Gao, G. N. Bennett, and J. J. Silberg, “Ratiometric gas reporting: a nondisruptive approach to monitor gene expression in soils,” ACS Synthetic Biology, vol. 7, no. 3, pp. 903–911, 2018. View at: Publisher Site | Google Scholar
  210. E. M. Fulk, D. Huh, J. T. Atkinson, M. Lie, C. A. Masiello, and J. J. Silberg, “A split methyl halide transferase AND gate that reports by synthesizing an indicator gas,” ACS Synthetic Biology, vol. 9, no. 11, pp. 3104–3113, 2020. View at: Publisher Site | Google Scholar
  211. E. M. Fulk, X. Gao, L. C. Lu, K. R. Redeker, C. A. Masiello, and J. J. Silberg, “Nondestructive chemical sensing within bulk soil using 1000 biosensors per gram of matrix,” ACS Synthetic Biology, vol. 11, no. 7, pp. 2372–2383, 2022. View at: Publisher Site | Google Scholar
  212. S. Hunt, “Measurements of photosynthesis and respiration in plants,” Physiologia Plantarum, vol. 117, no. 3, pp. 314–325, 2003. View at: Publisher Site | Google Scholar
  213. A. Singhvi, A. Fitzpatrick, J. D. Scharwies, J. R. Dinneny, and A. Arbabian, “A thermoacoustic imaging system for non-invasive and non-destructive root phenotyping,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 5, pp. 2493–2497, 2022. View at: Publisher Site | Google Scholar
  214. R. W. Bourdeau, A. Lee-Gosselin, A. Lakshmanan et al., “Acoustic reporter genes for noninvasive imaging of microorganisms in mammalian hosts,” Nature, vol. 553, no. 7686, pp. 86–90, 2018. View at: Publisher Site | Google Scholar
  215. D. P. Sawyer, A. Bar-Zion, A. Farhadi et al., “Ultrasensitive ultrasound imaging of gene expression with signal unmixing,” Nature Methods, vol. 18, no. 8, pp. 945–952, 2021. View at: Publisher Site | Google Scholar
  216. J. Woods, A. Williams, J. K. Hughes, M. Black, and R. Murphy, “Energy and the food system,” Philosophical Transactions of the Royal Society, B: Biological Sciences, vol. 365, no. 1554, pp. 2991–3006, 2010. View at: Publisher Site | Google Scholar
  217. D. F. Herridge, M. B. Peoples, and R. M. Boddey, “Global inputs of biological nitrogen fixation in agricultural systems,” Plant and Soil, vol. 311, no. 1-2, pp. 1–18, 2008. View at: Publisher Site | Google Scholar
  218. R. Dixon and D. Kahn, “Genetic regulation of biological nitrogen fixation,” Nature Reviews Microbiology, vol. 2, no. 8, pp. 621–631, 2004. View at: Publisher Site | Google Scholar
  219. F. Mus, M. B. Crook, K. Garcia et al., “Symbiotic nitrogen fixation and the challenges to its extension to nonlegumes,” Applied and Environmental Microbiology, vol. 82, pp. 3698–3710, 2016. View at: Publisher Site | Google Scholar
  220. M. J. Smanski, S. Bhatia, D. Zhao et al., “Functional optimization of gene clusters by combinatorial design and assembly,” Nature Biotechnology, vol. 32, no. 12, pp. 1241–1249, 2014. View at: Publisher Site | Google Scholar
  221. T. Schnabel and E. Sattely, “Engineering posttranslational regulation of glutamine synthetase for controllable ammonia production in the plant symbiont Azospirillum brasilense,” Applied and Environmental Microbiology, vol. 87, no. 14, article e0058221, 2021. View at: Publisher Site | Google Scholar
  222. T. Schnabel and E. Sattely, “Improved stability of engineered ammonia production in the plant-symbiont Azospirillum brasilense,” ACS Synthetic Biology, vol. 10, pp. 2982–2996, 2021. View at: Publisher Site | Google Scholar
  223. W. L. Lindsay and A. P. Schwab, “The chemistry of iron in soils and its availability to plants,” Journal of Plant Nutrition, vol. 5, pp. 821–840, 1982. View at: Publisher Site | Google Scholar
  224. P. Hinsinger, “Bioavailability of soil inorganic P in the rhizosphere as affected by root-induced chemical changes: a review,” Plant and Soil, vol. 237, no. 2, pp. 173–195, 2001. View at: Publisher Site | Google Scholar
  225. R. M. N. Kucey, H. H. Janzen, and M. E. Leggett, Advances in Agronomy, N. C. Brady, Ed., vol. 42, (Academic Press, 1989.
  226. J. W. Kloepper, J. Leong, M. Teintze, and M. N. Schroth, “Enhanced plant growth by siderophores produced by plant growth-promoting rhizobacteria,” Nature, vol. 286, no. 5776, pp. 885-886, 1980. View at: Publisher Site | Google Scholar
  227. P. Trapet, L. Avoscan, A. Klinguer et al., “The Pseudomonas fluorescens siderophore pyoverdine weakens Arabidopsis thaliana defense in favor of growth in iron-deficient conditions,” Plant Physiology, vol. 171, no. 1, pp. 675–693, 2016. View at: Publisher Site | Google Scholar
  228. H. Adhikary, P. B. Sanghavi, S. R. Macwan, G. Archana, and G. Naresh Kumar, “Artificial citrate operon confers mineral phosphate solubilization ability to diverse fluorescent pseudomonads,” PLoS One, vol. 9, no. 9, article e107554, 2014. View at: Publisher Site | Google Scholar
  229. J. Wagh, K. Chanchal, S. Sonal, B. Praveena, G. Archana, and G. N. Kumar, “Inoculation of genetically modified endophytic Herbaspirillum seropedicae Z67 endowed with gluconic and 2-ketogluconic acid secretion, confers beneficial effects on rice (Oriza sativa) plants,” Plant and Soil, vol. 409, no. 1-2, pp. 51–64, 2016. View at: Publisher Site | Google Scholar
  230. C. Kumar, K. Yadav, G. Archana, and G. Naresh Kumar, “2-ketogluconic acid secretion by incorporation of Pseudomonas putida KT 2440 gluconate dehydrogenase (gad) operon in Enterobacter asburiae PSI3 improves mineral phosphate solubilization,” Current Microbiology, vol. 67, no. 3, pp. 388–394, 2013. View at: Publisher Site | Google Scholar
  231. G. Swayambhu, N. Moscatello, G. E. Atilla-Gokcumen, and B. A. Pfeifer, “Flux balance analysis for media optimization and genetic targets to improve heterologous siderophore production,” iScience, vol. 23, no. 4, article 101016, 2020. View at: Publisher Site | Google Scholar
  232. R. Qi, G. Swayambhu, M. Bruno, G. Zhang, and B. A. Pfeifer, “Consolidated plasmid Design for Stabilized Heterologous Production of the complex natural product Siderophore Yersiniabactin,” Biotechnology Progress, vol. 37, no. 2, article e3103, 2021. View at: Publisher Site | Google Scholar
  233. C. N. Shulse, M. Chovatia, C. Agosto et al., “Engineered root bacteria release plant-available phosphate from phytate,” Applied and Environmental Microbiology, vol. 85, no. 18, 2019. View at: Publisher Site | Google Scholar
  234. M. E. Hernandez, A. Kappler, and D. K. Newman, “Phenazines and other redox-active antibiotics promote microbial mineral reduction,” Applied and Environmental Microbiology, vol. 70, no. 2, pp. 921–928, 2004. View at: Publisher Site | Google Scholar
  235. M. K. LeTourneau, M. J. Marshall, M. Grant et al., “Phenazine-1-carboxylic acid-producing bacteria enhance the reactivity of iron minerals in dryland and irrigated wheat rhizospheres,” Environmental Science & Technology, vol. 53, no. 24, pp. 14273–14284, 2019. View at: Publisher Site | Google Scholar
  236. D. L. McRose and D. K. Newman, “Redox-active antibiotics enhance phosphorus bioavailability,” Science, vol. 371, no. 6533, pp. 1033–1037, 2021. View at: Publisher Site | Google Scholar
  237. K. M. Dahlstrom, D. L. McRose, and D. K. Newman, “Keystone metabolites of crop rhizosphere microbiomes,” Current Biology, vol. 30, no. 19, pp. R1131–R1137, 2020. View at: Publisher Site | Google Scholar
  238. E. K. Perry and D. K. Newman, “Prevalence and correlates of phenazine resistance in culturable bacteria from a dryland wheat field,” Applied and Environmental Microbiology, vol. 88, no. 6, article e0232021, 2022. View at: Publisher Site | Google Scholar
  239. M. Bilal, S. Guo, H. M. N. Iqbal, H. Hu, W. Wang, and X. Zhang, “Engineering Pseudomonas for phenazine biosynthesis, regulation, and biotechnological applications: a review,” World Journal of Microbiology and Biotechnology, vol. 33, no. 10, p. 191, 2017. View at: Publisher Site | Google Scholar
  240. L. S. Thomashow, D. M. Weller, R. F. Bonsall, and L. S. Pierson, “Production of the antibiotic phenazine-1-carboxylic acid by fluorescent pseudomonas species in the rhizosphere of wheat,” Applied and Environmental Microbiology, vol. 56, no. 4, pp. 908–912, 1990. View at: Publisher Site | Google Scholar
  241. M. Bergsma-Vlami, M. E. Prins, and J. M. Raaijmakers, “Influence of plant species on population dynamics, genotypic diversity and antibiotic production in the rhizosphere by indigenous Pseudomonas spp,” FEMS Microbiology Ecology, vol. 52, no. 1, pp. 59–69, 2005. View at: Publisher Site | Google Scholar
  242. D. V. Mavrodi, O. V. Mavrodi, J. A. Parejko et al., “Accumulation of the antibiotic phenazine-1-carboxylic acid in the rhizosphere of dryland cereals,” Applied and Environmental Microbiology, vol. 78, pp. 804–812, 2012. View at: Publisher Site | Google Scholar
  243. X. Jiao, Y. Takishita, G. Zhou, and D. L. Smith, “Plant associated rhizobacteria for biocontrol and plant growth enhancement,” Frontiers in Plant Science, vol. 12, article 634796, 2021. View at: Publisher Site | Google Scholar
  244. J. M. Raaijmakers, M. Vlami, and J. T. de Souza, “Antibiotic production by bacterial biocontrol agents,” Antonie Van Leeuwenhoek, vol. 81, pp. 537–547, 2002. View at: Google Scholar
  245. C. R. Howell and R. D. Stipanovic, “Suppression of Pythium ultimum-induced damping-off of cotton seedlings by Pseudomonas fluorescens and its antibiotic, pyoluteorin,” Phytopathology, vol. 70, no. 8, pp. 712–715, 1980. View at: Publisher Site | Google Scholar
  246. J. Kraus and J. E. Loper, “Lack of evidence for a role of antifungal metabolite production byPseudomonas fluorescensPf-5 in biological control of Pythium damping-off of cucumber,” Phytopathology, vol. 82, no. 3, pp. 264–271, 1992. View at: Publisher Site | Google Scholar
  247. W. F. Pfender, J. Kraus, and J. E. Loper, “A genomic region fromPseudomonas fluorescensPf-5 required for pyrrolnitrin production and inhibition ofPyrenophora tritici-repentisin wheat straw,” Phytopathology, vol. 83, no. 11, pp. 1223–1228, 1993. View at: Publisher Site | Google Scholar
  248. I. T. Paulsen, C. M. Press, J. Ravel et al., “Complete genome sequence of the plant commensal Pseudomonas fluorescens Pf-5,” Nature Biotechnology, vol. 23, no. 7, pp. 873–878, 2005. View at: Publisher Site | Google Scholar
  249. B. Krebs, B. Höding, S. Kübart et al., “Use of Bacillus subtilis as biocontrol agent. I. Activities and characterization of Bacillus subtilis strains/Anwendung von Bacillus subtilis als Mittel für den biologischen Pflanzenschutz. I. Aktivitäten und Charakterisierung von Bacillus sibtills-Stämmen,” Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz/Journal of Plant Diseases and Protection, vol. 105, pp. 181–197, 1998. View at: Google Scholar
  250. X. H. Chen, A. Koumoutsi, R. Scholz et al., “Comparative analysis of the complete genome sequence of the plant growth–promoting bacterium Bacillus amyloliquefaciens FZB42,” Nature Biotechnology, vol. 25, pp. 1007–1014, 2007. View at: Publisher Site | Google Scholar
  251. S. P. Chowdhury, K. Dietel, M. Rändler et al., “Effects of Bacillus amyloliquefaciens FZB42 on lettuce growth and health under pathogen pressure and its impact on the rhizosphere bacterial community,” PLoS One, vol. 8, article e68818, 2013. View at: Publisher Site | Google Scholar
  252. P. J. Talboys, D. W. Owen, J. R. Healey, P. J. A. Withers, and D. L. Jones, “Auxin secretion by Bacillus amyloliquefaciens FZB42 both stimulates root exudation and limits phosphorus uptake in Triticum aestivium,” BMC Plant Biology, vol. 14, p. 51, 2014. View at: Publisher Site | Google Scholar
  253. H. Zhou, H. Wei, X. Liu, Y. Wang, L. Zhang, and W. Tang, “Improving biocontrol activity ofPseudomonas fluorescens through chromosomal integration of 2,4-diacetylphloroglucinol biosynthesis genes,” Chinese Science Bulletin, vol. 50, pp. 775–781, 2005. View at: Publisher Site | Google Scholar
  254. X. Wu, J. Liu, W. Zhang, and L. Zhang, “Multiple-level regulation of 2,4-diacetylphloroglucinol production by the sigma regulator PsrA in Pseudomonas fluorescens 2P24,” PLoS One, vol. 7, no. 11, article e50149, 2012. View at: Publisher Site | Google Scholar
  255. J. K. Patel and G. Archana, “Engineered production of 2,4-diacetylphloroglucinol in the diazotrophic endophytic bacterium Pseudomonas sp. WS5 and its beneficial effect in multiple plant-pathogen systems,” Applied Soil Ecology, vol. 124, pp. 34–44, 2018. View at: Publisher Site | Google Scholar
  256. Y. Dang, F. Zhao, X. Liu et al., “Enhanced production of antifungal lipopeptide iturin A by Bacillus amyloliquefaciens LL3 through metabolic engineering and culture conditions optimization,” Microbial Cell Factories, vol. 18, no. 1, p. 68, 2019. View at: Publisher Site | Google Scholar
  257. A. Price-Whelan, L. E. P. Dietrich, and D. K. Newman, “Rethinking 'secondary' metabolism: physiological roles for phenazine antibiotics,” Nature Chemical Biology, vol. 2, no. 2, pp. 71–78, 2006. View at: Publisher Site | Google Scholar
  258. J. M. Yu, D. Wang, T. R. Ries, L. S. Pierson, and E. A. Pierson, “An upstream sequence modulates phenazine production at the level of transcription and translation in the biological control strain Pseudomonas chlororaphis 30-84,” PLoS One, vol. 13, article e0193063, 2018. View at: Publisher Site | Google Scholar
  259. Z.-J. Jin, L. Zhou, S. Sun et al., “Identification of a strong quorum sensing- and Thermo-regulated promoter for the biosynthesis of a new metabolite pesticide phenazine-1-carboxamide in Pseudomonas strain PA1201,” ACS Synthetic Biology, vol. 9, pp. 1802–1812, 2020. View at: Publisher Site | Google Scholar
  260. L. Li, Z. Li, W. Yao et al., “Metabolic engineering of Pseudomonas chlororaphis Qlu-1 for the enhanced production of phenazine-1-carboxamide,” Journal of Agricultural and Food Chemistry, vol. 68, pp. 14832–14840, 2020. View at: Publisher Site | Google Scholar
  261. S. Guo, R. Liu, W. Wang, H. Hu, Z. Li, and X. Zhang, “Designing an artificial pathway for the biosynthesis of a novel phenazine N-oxide in Pseudomonas chlororaphis HT66,” ACS Synthetic Biology, vol. 9, pp. 883–892, 2020. View at: Publisher Site | Google Scholar
  262. A. Bravo, S. Likitvivatanavong, S. S. Gill, and M. Soberón, “Bacillus thuringiensis: A story of a successful bioinsecticide,” Insect Biochemistry and Molecular Biology, vol. 41, no. 7, pp. 423–431, 2011. View at: Publisher Site | Google Scholar
  263. B. E. Tabashnik, “Evolution of resistance to Bacillus thuringiensis,” Annual Review of Entomology, vol. 39, no. 1, pp. 47–79, 1994. View at: Publisher Site | Google Scholar
  264. B. E. Tabashnik, T. Brévault, and Y. Carrière, “Insect resistance to Bt crops: lessons from the first billion acres,” Nature Biotechnology, vol. 31, pp. 510–521, 2013. View at: Publisher Site | Google Scholar
  265. A. H. Badran, V. M. Guzov, Q. Huai et al., “Continuous evolution of Bacillus thuringiensis toxins overcomes insect resistance,” Nature, vol. 533, pp. 58–63, 2016. View at: Publisher Site | Google Scholar
  266. A. J. Mullins, J. A. H. Murray, M. J. Bull et al., “Genome mining identifies cepacin as a plant-protective metabolite of the biopesticidal bacterium Burkholderia ambifaria,” Nature Microbiology, vol. 4, no. 6, pp. 996–1005, 2019. View at: Publisher Site | Google Scholar
  267. K. Murata, M. Suenaga, and K. Kai, “Genome mining discovery of protegenins A–D, bacterial polyynes involved in the antioomycete and biocontrol activities of Pseudomonas protegens,” ACS Chemical Biology, 2021. View at: Publisher Site | Google Scholar
  268. J. Ke, Z. Zhao, C. R. Coates et al., “Development of platforms for functional characterization and production of phenazines using a multi-chassis approach via CRAGE,” Metabolic Engineering, vol. 69, pp. 188–197, 2022. View at: Publisher Site | Google Scholar
  269. W. Wang, B. Vinocur, and A. Altman, “Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance,” Planta, vol. 218, no. 1, pp. 1–14, 2003. View at: Publisher Site | Google Scholar
  270. F. T. de Vries, R. I. Griffiths, C. G. Knight, O. Nicolitch, and A. Williams, “Harnessing rhizosphere microbiomes for drought-resilient crop production,” Science, vol. 368, pp. 270–274, 2020. View at: Publisher Site | Google Scholar
  271. T. R. Mahmoudi, J. M. Yu, S. Liu, L. S. Pierson, and E. A. Pierson, “Drought-stress tolerance in wheat seedlings conferred by phenazine-producing rhizobacteria,” Frontiers in Microbiology, vol. 10, p. 1590, 2019. View at: Publisher Site | Google Scholar
  272. P. Yuan, H. Pan, E. N. Boak, L. S. Pierson, and E. A. Pierson, “Phenazine-producing rhizobacteria promote plant growth and reduce redox and osmotic stress in wheat seedlings under saline conditions,” Frontiers in Plant Science, vol. 11, article 575314, 2020. View at: Publisher Site | Google Scholar
  273. M. Iordachescu and R. Imai, “Trehalose biosynthesis in response to abiotic stresses,” Journal of Integrative Plant Biology, vol. 50, no. 10, pp. 1223–1229, 2008. View at: Publisher Site | Google Scholar
  274. J. E. Lunn, I. Delorge, C. M. Figueroa, P. Van Dijck, and M. Stitt, “Trehalose metabolism in plants,” The Plant Journal, vol. 79, no. 4, pp. 544–567, 2014. View at: Publisher Site | Google Scholar
  275. F. Fichtner and J. E. Lunn, “The role of trehalose 6-phosphate (Tre6P) in plant metabolism and development,” Annual Review of Plant Biology, vol. 72, pp. 737–760, 2021. View at: Publisher Site | Google Scholar
  276. R. Suárez, A. Wong, M. Ramírez et al., “Improvement of drought tolerance and grain yield in common bean by overexpressing trehalose-6-phosphate synthase in rhizobia,” Molecular Plant-Microbe Interactions, vol. 21, pp. 958–966, 2008. View at: Publisher Site | Google Scholar
  277. J. Rodríguez-Salazar, R. Suárez, J. Caballero-Mellado, and G. Iturriaga, “Trehalose accumulation in Azospirillum brasilense improves drought tolerance and biomass in maize plants,” FEMS Microbiology Letters, vol. 296, pp. 52–59, 2009. View at: Publisher Site | Google Scholar
  278. J. I. Vílchez, C. García-Fontana, D. Román-Naranjo, J. González-López, and M. Manzanera, “Plant drought tolerance enhancement by trehalose production of desiccation-tolerant microorganisms,” Frontiers in Microbiology, vol. 7, p. 1577, 2016. View at: Publisher Site | Google Scholar
  279. O. Fernandez, L. Béthencourt, A. Quero, R. S. Sangwan, and C. Clément, “Trehalose and plant stress responses: friend or foe?” Trends in Plant Science, vol. 15, pp. 409–417, 2010. View at: Publisher Site | Google Scholar
  280. B. Vishal, P. Krishnamurthy, R. Ramamoorthy, and P. P. Kumar, “OsTPS8 controls yield-related traits and confers salt stress tolerance in rice by enhancing suberin deposition,” New Phytologist, vol. 221, pp. 1369–1386, 2019. View at: Publisher Site | Google Scholar
  281. P. G. Hartel and M. Alexander, “Role of extracellular polysaccharide production and clays in the desiccation tolerance of Cowpea bradyrhizobia,” Soil Science Society of America Journal, vol. 50, pp. 1193–1198, 1986. View at: Publisher Site | Google Scholar
  282. E. B. Roberson and M. K. Firestone, “Relationship between desiccation and exopolysaccharide production in a soil Pseudomonas sp,” Applied and Environmental Microbiology, vol. 58, pp. 1284–1291, 1992. View at: Publisher Site | Google Scholar
  283. M. Ashraf, S. Hasnain, O. Berge, and T. Mahmood, “Inoculating wheat seedlings with exopolysaccharide-producing bacteria restricts sodium uptake and stimulates plant growth under salt stress,” Biology and Fertility of Soils, vol. 40, no. 3, 2004. View at: Publisher Site | Google Scholar
  284. D.-C. Wang, C.-H. Jiang, L.-N. Zhang, L. Chen, X.-Y. Zhang, and J.-H. Guo, “Biofilms positively contribute to Bacillus amyloliquefaciens 54-induced drought tolerance in tomato plants,” International Journal of Molecular Sciences, vol. 20, no. 24, p. 6271, 2019. View at: Publisher Site | Google Scholar
  285. E. Gamalero and B. R. Glick, “Bacterial modulation of plant ethylene levels,” Plant Physiology, vol. 169, no. 1, pp. 13–22, 2015. View at: Publisher Site | Google Scholar
  286. J.-J. Tao, H.-W. Chen, B. Ma, W.-K. Zhang, S.-Y. Chen, and J.-S. Zhang, “The role of ethylene in plants under salinity stress,” Frontiers in Plant Science, vol. 6, p. 1059, 2015. View at: Publisher Site | Google Scholar
  287. S. Mayak, T. Tirosh, and B. R. Glick, “Plant growth-promoting bacteria that confer resistance to water stress in tomatoes and peppers,” Plant Science, vol. 166, no. 2, pp. 525–530, 2004. View at: Publisher Site | Google Scholar
  288. M. Saleem, M. Arshad, S. Hussain, and A. S. Bhatti, “Perspective of plant growth promoting rhizobacteria (PGPR) containing ACC deaminase in stress agriculture,” Journal of Industrial Microbiology & Biotechnology, vol. 34, no. 10, pp. 635–648, 2007. View at: Publisher Site | Google Scholar
  289. Y. Liu, L. Cao, H. Tan, and R. Zhang, “Surface display of ACC deaminase on endophytic Enterobacteriaceae strains to increase saline resistance of host rice sprouts by regulating plant ethylene synthesis,” Microbial Cell Factories, vol. 16, no. 1, p. 214, 2017. View at: Publisher Site | Google Scholar
  290. X. Shen, Z. Wang, X. Song et al., “Transcriptomic profiling revealed an important role of cell wall remodeling and ethylene signaling pathway during salt acclimation in Arabidopsis,” Plant Molecular Biology, vol. 86, pp. 303–317, 2014. View at: Publisher Site | Google Scholar
  291. J. Peng, Z. Li, X. Wen et al., “Salt-induced stabilization of EIN3/EIL1 confers salinity tolerance by deterring ROS accumulation in Arabidopsis,” PLoS Genetics, vol. 10, article e1004664, 2014. View at: Publisher Site | Google Scholar
  292. K. Paustian, E. Larson, J. Kent, E. Marx, and A. Swan, “Soil C sequestration as a biological negative emission strategy,” Frontiers in Climate, vol. 1, 2019. View at: Publisher Site | Google Scholar
  293. N. H. Batjes, “Total carbon and nitrogen in the soils of the world,” European Journal of Soil Science, vol. 47, no. 2, pp. 151–163, 1996. View at: Publisher Site | Google Scholar
  294. N. W. Sokol, J. Sanderman, and M. A. Bradford, “Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry,” Global Change Biology, vol. 25, no. 1, pp. 12–24, 2019. View at: Publisher Site | Google Scholar
  295. N. W. Sokol, E. Slessarev, G. L. Marschmann et al., “Life and death in the soil microbiome: how ecological processes influence biogeochemistry,” Nature Reviews Microbiology, vol. 20, no. 7, pp. 415–430, 2022. View at: Publisher Site | Google Scholar
  296. K. M. Geyer, E. Kyker-Snowman, A. S. Grandy, and S. D. Frey, “Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter,” Biogeochemistry, vol. 127, no. 2-3, pp. 173–188, 2016. View at: Publisher Site | Google Scholar
  297. M. Saifuddin, J. M. Bhatnagar, D. Segrè, and A. C. Finzi, “Microbial carbon use efficiency predicted from genome-scale metabolic models,” Nature Communications, vol. 10, no. 1, p. 3568, 2019. View at: Publisher Site | Google Scholar
  298. M. Ludwig, J. Achtenhagen, A. Miltner et al., “Microbial contribution to SOM quantity and quality in density fractions of temperate arable soils,” Soil Biology and Biochemistry, vol. 81, pp. 311–322, 2015. View at: Publisher Site | Google Scholar
  299. C. Liang, W. Amelung, J. Lehmann, and M. Kästner, “Quantitative assessment of microbial necromass contribution to soil organic matter,” Global Change Biology, vol. 25, no. 11, pp. 3578–3590, 2019. View at: Publisher Site | Google Scholar
  300. K. Mason-Jones, S. L. Robinson, G. F. C. Veen, S. Manzoni, and W. H. van der Putten, “Microbial storage and its implications for soil ecology,” The ISME Journal, vol. 16, no. 3, pp. 617–629, 2022. View at: Publisher Site | Google Scholar
  301. D. Kadouri, E. Jurkevitch, Y. Okon, and S. Castro-Sowinski, “Ecological and agricultural significance of bacterial polyhydroxyalkanoates,” Critical Reviews in Microbiology, vol. 31, no. 2, pp. 55–67, 2005. View at: Publisher Site | Google Scholar
  302. D. Kadouri, S. Burdman, E. Jurkevitch, and Y. Okon, “Identification and isolation of genes involved in poly(β-hydroxybutyrate) biosynthesis in Azospirillum brasilense and characterization of a phbC mutant,” Applied and Environmental Microbiology, vol. 68, pp. 2943–2949, 2002. View at: Publisher Site | Google Scholar
  303. D. Kadouri, E. Jurkevitch, and Y. Okon, “Involvement of the reserve material poly-beta-hydroxybutyrate in Azospirillum brasilense stress endurance and root colonization,” Applied and Environmental Microbiology, vol. 69, no. 6, pp. 3244–3250, 2003. View at: Publisher Site | Google Scholar
  304. W. C. Ratcliff and R. F. Denison, “Individual-Level Bet Hedging in the Bacterium Sinorhizobium meliloti,” Current Biology, vol. 20, no. 19, pp. 1740–1744, 2010. View at: Publisher Site | Google Scholar
  305. J. A. Ramírez-Trujillo, M. F. Dunn, R. Suárez-Rodríguez, and I. Hernández-Lucas, “The Sinorhizobium meliloti glyoxylate cycle enzyme isocitrate lyase (AceA) is required for the utilization of poly-β-hydroxybutyrate during carbon starvation,” Annals of Microbiology, vol. 66, no. 2, pp. 921–924, 2016. View at: Publisher Site | Google Scholar
  306. Z.-J. Li, Z.-Y. Shi, J. Jian, Y.-Y. Guo, Q. Wu, and G.-Q. Chen, “Production of poly(3-hydroxybutyrate- co-4-hydroxybutyrate) from unrelated carbon sources by metabolically engineered Escherichia coli,” Metabolic Engineering, vol. 12, no. 4, pp. 352–359, 2010. View at: Publisher Site | Google Scholar
  307. D.-C. Meng and G.-Q. Chen, “Synthetic biology of polyhydroxyalkanoates (PHA),” in Synthetic Biology – Metabolic Engineering, Springer, 2017. View at: Publisher Site | Google Scholar
  308. M. P. Mezzina, M. T. Manoli, M. A. Prieto, and P. I. Nikel, “Engineering native and synthetic pathways in Pseudomonas putida for the production of tailored polyhydroxyalkanoates,” Biotechnology Journal, vol. 16, article e2000165, 2021. View at: Publisher Site | Google Scholar
  309. A. Lerner, S. Castro-Sowinski, H. Lerner, Y. Okon, and S. Burdman, “Glycogen phosphorylase is involved in stress endurance and biofilm formation in Azospirillum brasilense Sp7,” FEMS Microbiology Letters, vol. 300, pp. 75–82, 2009. View at: Publisher Site | Google Scholar
  310. Y. J. Goh and T. R. Klaenhammer, “A functional glycogen biosynthesis pathway in Lactobacillus acidophilus: expression and analysis of the glg operon,” Molecular Microbiology, vol. 89, no. 6, pp. 1187–1200, 2013. View at: Publisher Site | Google Scholar
  311. J. Carpinelli, R. Krämer, and E. Agosin, “Metabolic engineering of Corynebacterium glutamicum for trehalose overproduction: role of the TreYZ trehalose biosynthetic pathway,” Applied and Environmental Microbiology, vol. 72, no. 3, pp. 1949–1955, 2006. View at: Publisher Site | Google Scholar
  312. V. Poirier, C. Roumet, and A. D. Munson, “The root of the matter: linking root traits and soil organic matter stabilization processes,” Soil Biology and Biochemistry, vol. 120, pp. 246–259, 2018. View at: Publisher Site | Google Scholar
  313. K. Paustian, N. Campbell, C. Dorich, E. Marx, and A. Swan, Assessment of Potential Greenhouse Gas Mitigation from Changes to Crop Root Mass and Architecture, Booz Allen Hamiltion Inc., McLean, VA (United States), 2016, https://www.osti.gov/biblio/1339423.
  314. P. Overvoorde, H. Fukaki, and T. Beeckman, “Auxin control of root development,” Cold Spring Harbor Perspectives in Biology, vol. 2, article a001537, 2010. View at: Google Scholar
  315. K. Kazan, “Auxin and the integration of environmental signals into plant root development,” Annals of Botany, vol. 112, no. 9, pp. 1655–1665, 2013. View at: Publisher Site | Google Scholar
  316. M. Sarwar and R. J. Kremer, “Enhanced suppression of plant growth through production of L-tryptophan-derived compounds by deleterious rhizobacteria,” Plant and Soil, vol. 172, no. 2, pp. 261–269, 1995. View at: Publisher Site | Google Scholar
  317. H. Xie, J. J. Pasternak, and B. R. Glick, “Isolation and characterization of mutants of the plant growth-promoting rhizobacterium Pseudomonas putida GR12-2 that overproduce indoleacetic acid,” Current Microbiology, vol. 32, no. 2, pp. 67–71, 1996. View at: Publisher Site | Google Scholar
  318. A. Zúñiga, F. de la Fuente, F. Federici et al., “An engineered device for indoleacetic acid production under quorum sensing signals Enables Cupriavidus pinatubonensis JMP134 to stimulate plant growth,” ACS Synthetic Biology, vol. 7, no. 6, pp. 1519–1527, 2018. View at: Publisher Site | Google Scholar
  319. A. E. Harman-Ware, S. Sparks, B. Addison, and U. C. Kalluri, “Importance of suberin biopolymer in plant function, contributions to soil organic carbon and in the production of bio-derived energy and materials,” Biotechnology for Biofuels, vol. 14, no. 1, p. 75, 2021. View at: Publisher Site | Google Scholar
  320. L. Schreiber, K. Hartmann, M. Skrabs, and J. Zeier, “Apoplastic barriers in roots: chemical composition of endodermal and hypodermal cell walls,” Journal of Experimental Botany, vol. 50, no. 337, pp. 1267–1280, 1999. View at: Publisher Site | Google Scholar
  321. K. Lorenz, R. Lal, C. M. Preston, and K. G. J. Nierop, “Strengthening the soil organic carbon pool by increasing contributions from recalcitrant aliphatic bio(macro)molecules,” Geoderma, vol. 142, no. 1-2, pp. 1–10, 2007. View at: Publisher Site | Google Scholar
  322. I. Salas-González, G. Reyt, P. Flis et al., “Coordination between microbiota and root endodermis supports plant mineral nutrient homeostasis,” Science, vol. 371, no. 6525, 2021. View at: Publisher Site | Google Scholar
  323. E. Martynenko, T. Arkhipova, V. Safronova et al., “Effects of phytohormone-producing rhizobacteria on casparian band formation, ion homeostasis and salt tolerance of durum wheat,” Biomolecules, vol. 12, no. 2, p. 230, 2022. View at: Publisher Site | Google Scholar
  324. L. Boiero, D. Perrig, O. Masciarelli, C. Penna, F. Cassán, and V. Luna, “Phytohormone production by three strains of Bradyrhizobium japonicum and possible physiological and technological implications,” Applied Microbiology and Biotechnology, vol. 74, no. 4, pp. 874–880, 2007. View at: Publisher Site | Google Scholar
  325. D. Perrig, M. L. Boiero, O. A. Masciarelli et al., “Plant-growth-promoting compounds produced by two agronomically important strains of Azospirillum brasilense, and implications for inoculant formulation,” Applied Microbiology and Biotechnology, vol. 75, no. 5, pp. 1143–1150, 2007. View at: Publisher Site | Google Scholar
  326. A. C. Cohen, R. Bottini, and P. N. Piccoli, “Azospirillum brasilense Sp 245 produces ABA in chemically-defined culture medium and increases ABA content in arabidopsis plants,” Plant Growth Regulation, vol. 54, no. 2, pp. 97–103, 2008. View at: Publisher Site | Google Scholar
  327. A. Levy, I. Salas Gonzalez, M. Mittelviefhaus et al., “Genomic features of bacterial adaptation to plants,” Nature Genetics, vol. 50, no. 1, pp. 138–150, 2017. View at: Publisher Site | Google Scholar
  328. L. Xu, Z. Dong, D. Chiniquy et al., “Genome-resolved metagenomics reveals role of iron metabolism in drought- induced rhizosphere microbiome dynamics,” Nature Communications, vol. 12, no. 1, p. 3209, 2021. View at: Publisher Site | Google Scholar
  329. H. E. Knights, B. Jorrin, T. L. Haskett, and P. S. Poole, “Deciphering bacterial mechanisms of root colonization,” Environmental Microbiology Reports, vol. 13, no. 4, pp. 428–444, 2021. View at: Publisher Site | Google Scholar
  330. M. K. Hassan, J. A. McInroy, and J. W. Kloepper, “The interactions of rhizodeposits with plant growth-promoting rhizobacteria in the rhizosphere: a review,” Agriculture, vol. 9, p. 142, 2019. View at: Publisher Site | Google Scholar
  331. J. Munoz-Ucros, M. J. Zwetsloot, C. Cuellar-Gempeler, and T. L. Bauerle, “Spatiotemporal patterns of rhizosphere microbiome assembly: from ecological theory to agricultural application,” Journal of Applied Ecology, vol. 58, no. 5, pp. 894–904, 2021. View at: Publisher Site | Google Scholar
  332. J. A. Peiffer, A. Spor, O. Koren et al., “Diversity and heritability of the maize rhizosphere microbiome under field conditions,” Proceedings of the National Academy of Sciences, vol. 110, no. 16, pp. 6548–6553, 2013. View at: Publisher Site | Google Scholar
  333. P. R. Hirsch, “Release of transgenic bacterial inoculants - rhizobia as a case study,” Plant and Soil, vol. 266, no. 1-2, pp. 1–10, 2005. View at: Publisher Site | Google Scholar
  334. C. A. Wozniak, G. McClung, J. Gagliardi, M. Segal, and K. Matthews, Regulation of Agricultural Biotechnology: The United States and Canada, C. A. Wozniak and A. McHughen, Eds., Springer, Netherlands, Dordrecht, 2012.
  335. J. K. Miller and K. J. Bradford, “The regulatory bottleneck for biotech specialty crops,” Nature Biotechnology, vol. 28, no. 10, pp. 1012–1014, 2010. View at: Publisher Site | Google Scholar
  336. G. Conko, D. L. Kershen, H. Miller, and W. A. Parrott, “A risk-based approach to the regulation of genetically engineered organisms,” Nature Biotechnology, vol. 34, no. 5, pp. 493–503, 2016. View at: Publisher Site | Google Scholar
  337. S. H. Strauss and J. K. Sax, “Ending event-based regulation of GMO crops,” Nature Biotechnology, vol. 34, no. 5, pp. 474–477, 2016. View at: Publisher Site | Google Scholar
  338. M. T. Parker and A. M. Kunjapur, “Deployment of engineered microbes: contributions to the bioeconomy and considerations for biosecurity,” Health Security, vol. 18, no. 4, pp. 278–296, 2020. View at: Publisher Site | Google Scholar
  339. C. N. Jack, R. H. Petipas, T. E. Cheeke, J. L. Rowland, and M. L. Friesen, “Microbial inoculants: silver bullet or microbial Jurassic Park?” Trends in Microbiology, vol. 29, no. 4, pp. 299–308, 2021. View at: Publisher Site | Google Scholar
  340. O. Wright, G.-B. Stan, and T. Ellis, “Building-in biosafety for synthetic biology,” Microbiology, vol. 159, Part 7, pp. 1221–1235, 2013. View at: Publisher Site | Google Scholar
  341. J. W. Lee, C. T. Y. Chan, S. Slomovic, and J. J. Collins, “Next-generation biocontainment systems for engineered organisms,” Nature Chemical Biology, vol. 14, no. 6, pp. 530–537, 2018. View at: Publisher Site | Google Scholar
  342. K. M. Gillespie, J. S. Angle, and R. L. Hill, “Runoff losses of Pseudomonas aureofaciens (lacZY) from soil,” FEMS Microbiology Ecology, vol. 17, pp. 239–245, 1995. View at: Publisher Site | Google Scholar
  343. B. Vidiella and R. Solé, “Ecological firewalls for synthetic biology,” iScience, vol. 25, no. 7, article 104658, 2022. View at: Publisher Site | Google Scholar
  344. M. C. Ronchel and J. L. Ramos, “Dual system to reinforce biological containment of recombinant bacteria designed for rhizoremediation,” Applied and Environmental Microbiology, vol. 67, no. 6, pp. 2649–2656, 2001. View at: Publisher Site | Google Scholar
  345. L. Steidler, S. Neirynck, N. Huyghebaert et al., “Biological containment of genetically modified Lactococcus lactis for intestinal delivery of human interleukin 10,” Nature Biotechnology, vol. 21, no. 7, pp. 785–789, 2003. View at: Publisher Site | Google Scholar
  346. L. Molina, C. Ramos, M. C. Ronchel, S. Molin, and J. L. Ramos, “Construction of an efficient biologically Contained Pseudomonas putida Strain and its survival in outdoor assays,” Applied and Environmental Microbiology, vol. 64, no. 6, pp. 2072–2078, 1998. View at: Publisher Site | Google Scholar
  347. C. T. Y. Chan, J. W. Lee, D. E. Cameron, C. J. Bashor, and J. J. Collins, ““Deadman” and “Passcode” microbial kill switches for bacterial containment,” Nature Chemical Biology, vol. 12, pp. 82–86, 2015. View at: Publisher Site | Google Scholar
  348. A. G. Rottinghaus, A. Ferreiro, S. R. S. Fishbein, G. Dantas, and T. S. Moon, “Genetically stable CRISPR-based kill switches for engineered microbes,” Nature Communications, vol. 13, no. 1, p. 672, 2022. View at: Publisher Site | Google Scholar
  349. D. J. Mandell, M. J. Lajoie, M. T. Mee et al., “Biocontainment of genetically modified organisms by synthetic protein design,” Nature, vol. 518, no. 7537, pp. 55–60, 2015. View at: Publisher Site | Google Scholar
  350. N. Ostrov, M. Landon, M. Guell et al., “Design, synthesis, and testing toward a 57-codon genome,” Science, vol. 353, no. 6301, pp. 819–822, 2016. View at: Publisher Site | Google Scholar
  351. H. Zhao, W. Ding, J. Zang et al., “Directed-evolution of translation system for efficient unnatural amino acids incorporation and generalizable synthetic auxotroph construction,” Nature Communications, vol. 12, no. 1, p. 7039, 2021. View at: Publisher Site | Google Scholar
  352. J. M. Jez, C. N. Topp, N. W. Breakfield, D. Collett, and M. E. Frodyma, “Plant growth-promoting microbes—an industry view,” Emerging Topics in Life Sciences, vol. 5, no. 2, pp. 317–324, 2021. View at: Publisher Site | Google Scholar
  353. E. Waltz, “A new crop of microbe startups raises big bucks, takes on the establishment,” Nature Biotechnology, vol. 35, no. 12, pp. 1120–1122, 2017. View at: Publisher Site | Google Scholar
  354. A. Wen, K. L. Havens, S. E. Bloch et al., “Enabling biological nitrogen fixation for cereal crops in fertilized fields,” ACS Synthetic Biology, vol. 10, no. 12, pp. 3264–3277, 2021. View at: Publisher Site | Google Scholar
  355. E. K. Mitter, M. Tosi, D. Obregón, K. E. Dunfield, and J. J. Germida, “Rethinking crop nutrition in times of modern microbiology: innovative biofertilizer technologies,” Frontiers in Sustainable Food Systems, vol. 5, 2021. View at: Publisher Site | Google Scholar
  356. F. Meng and T. Ellis, “The second decade of synthetic biology: 2010–2020,” Nature Communications, vol. 11, pp. 1–4, 2020. View at: Publisher Site | Google Scholar
  357. T. L. Haskett, P. Paramasivan, M. D. Mendes et al., “Engineered plant control of associative nitrogen fixation,” Proceedings of the National Academy of Sciences, vol. 119, no. 16, article e2117465119, 2022. View at: Publisher Site | Google Scholar
  358. T. S. Jones, S. M. D. Oliveira, C. J. Myers, C. A. Voigt, and D. Densmore, “Genetic circuit design automation with Cello 2.0,” Nature Protocols, vol. 17, no. 4, pp. 1097–1113, 2022. View at: Publisher Site | Google Scholar
  359. T.-C. Tang, B. An, Y. Huang et al., “Materials design by synthetic biology,” Nature Reviews Materials, vol. 6, pp. 332–350, 2021. View at: Publisher Site | Google Scholar
  360. A. Rodrigo-Navarro, S. Sankaran, M. J. Dalby, A. del Campo, and M. Salmeron-Sanchez, “Engineered living biomaterials,” Nature Reviews Materials, vol. 6, no. 12, pp. 1175–1190, 2021. View at: Publisher Site | Google Scholar
  361. A. M. Duraj-Thatte, N.-M. D. Courchesne, P. Praveschotinunt et al., “Genetically programmable self-regenerating bacterial hydrogels,” Advanced Materials, vol. 31, no. 40, article e1901826, 2019. View at: Publisher Site | Google Scholar
  362. M. Florea, H. Hagemann, G. Santosa et al., “Engineering control of bacterial cellulose production using a genetic toolkit and a new cellulose-producing strain,” Proceedings of the National Academy of Sciences, vol. 113, no. 24, pp. E3431–E3440, 2016. View at: Publisher Site | Google Scholar
  363. A. I. Flamholz, E. Dugan, C. Blikstad et al., “Functional reconstitution of a bacterial CO2 concentrating mechanism in Escherichia coli,” eLife, vol. 9, 2020. View at: Publisher Site | Google Scholar
  364. T. Remans, S. Thijs, S. Truyens et al., “Understanding the development of roots exposed to contaminants and the potential of plant-associated bacteria for optimization of growth,” Annals of Botany, vol. 110, no. 2, pp. 239–252, 2012. View at: Publisher Site | Google Scholar
  365. V. Dhaka, S. Singh, P. C. Ramamurthy et al., “Biological degradation of polyethylene terephthalate by rhizobacteria,” Environmental Science and Pollution Research, 2022. View at: Publisher Site | Google Scholar
  366. H. Lu, D. J. Diaz, N. J. Czarnecki et al., “Machine learning-aided engineering of hydrolases for PET depolymerization,” Nature, vol. 604, no. 7907, pp. 662–667, 2022. View at: Publisher Site | Google Scholar
  367. W. Lee, T. K. Wood, and W. Chen, “Engineering TCE-degrading rhizobacteria for heavy metal accumulation and enhanced TCE degradation,” Biotechnology and Bioengineering, vol. 95, no. 3, pp. 399–403, 2006. View at: Publisher Site | Google Scholar
  368. C. H. Wu, T. K. Wood, A. Mulchandani, and W. Chen, “Engineering plant-microbe symbiosis for rhizoremediation of heavy metals,” Applied and Environmental Microbiology, vol. 72, no. 2, pp. 1129–1134, 2006. View at: Publisher Site | Google Scholar
  369. K. E. French, “Engineering mycorrhizal symbioses to alter plant metabolism and improve crop health,” Frontiers in Microbiology, vol. 8, p. 1403, 2017. View at: Publisher Site | Google Scholar
  370. T. R. Turner, K. Ramakrishnan, J. Walshaw et al., “Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants,” The ISME Journal, vol. 7, no. 12, pp. 2248–2258, 2013. View at: Publisher Site | Google Scholar
  371. N. Begum, C. Qin, M. A. Ahanger et al., “Role of arbuscular mycorrhizal fungi in plant growth regulation: implications in abiotic stress tolerance,” Frontiers in Plant Science, vol. 10, 2019. View at: Publisher Site | Google Scholar
  372. S. D. Frey, “Mycorrhizal fungi as mediators of soil organic matter dynamics,” Annual Review of Ecology, Evolution, and Systematics, vol. 50, no. 1, pp. 237–259, 2019. View at: Publisher Site | Google Scholar
  373. J. A. Vorholt, “Microbial life in the phyllosphere,” Nature Reviews Microbiology, vol. 10, no. 12, pp. 828–840, 2012. View at: Publisher Site | Google Scholar
  374. R. Perreault and I. Laforest-Lapointe, “Plant-microbe interactions in the phyllosphere: facing challenges of the anthropocene,” The ISME Journal, vol. 16, no. 2, pp. 339–345, 2022. View at: Publisher Site | Google Scholar
  375. L. De Kempeneer, B. Sercu, W. Vanbrabant, H. Van Langenhove, and W. Verstraete, “Bioaugmentation of the phyllosphere for the removal of toluene from indoor air,” Applied Microbiology and Biotechnology, vol. 64, no. 2, pp. 284–288, 2004. View at: Publisher Site | Google Scholar
  376. A. Sandhu, L. J. Halverson, and G. A. Beattie, “Bacterial degradation of airborne phenol in the phyllosphere,” Environmental Microbiology, vol. 9, no. 2, pp. 383–392, 2007. View at: Publisher Site | Google Scholar
  377. D. V. Badri, N. Quintana, E. G. El Kassis et al., “An ABC transporter mutation alters root exudation of phytochemicals that provoke an overhaul of natural soil microbiota,” Plant Physiology, vol. 151, no. 4, pp. 2006–2017, 2009. View at: Publisher Site | Google Scholar
  378. J.-H. Hehemann, G. Correc, T. Barbeyron, W. Helbert, M. Czjzek, and G. Michel, “Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota,” Nature, vol. 464, no. 7290, pp. 908–912, 2010. View at: Publisher Site | Google Scholar
  379. E. S. Shepherd, W. C. DeLoache, K. M. Pruss, W. R. Whitaker, and J. L. Sonnenburg, “An exclusive metabolic niche enables strain engraftment in the gut microbiota,” Nature, vol. 557, no. 7705, pp. 434–438, 2018. View at: Publisher Site | Google Scholar

Copyright © 2022 Christopher M. Dundas and José R. Dinneny. Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).

 PDF Download Citation Citation
Views603
Downloads266
Altmetric Score
Citations