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At least 199 records · Page 11

Cyanobacterial circadian regulation enhances bioproduction under subjective nighttime through rewiring of carbon partitioning dynamics, redox balance orchestration, and cell cycle modulation

Abstract Background The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction. Results After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms. Conclusions Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.

59 BASIC BIOLOGICAL SCIENCES↗

Prediction of non-intuitive metabolic targets with bayesian metabolic control analysis to improve 3-hydroxypropionic acid production in Aspergillus niger

Development of efficient bioconversion processes is limited by the ability to predictably improve metabolic flux. Here we deployed Bayesian Metabolic Control Analysis as a platform to integrate multi-omics data with metabolic modeling and evaluated its ability to predict genetic interventions that improve metabolic flux. Global Metabolomics and proteomics data was collected from 17 Aspergillus niger strains engineered to produce the platform biochemical 3-hydroxypropionic acid from which seven actional genetic interventions were predicted from significant flux control coefficients. Of the suggested genetic interventions, two were present within the intuitively designed strains used for training (malonic semialdehyde dehydrogenase and pyruvate carboxylase) while five predicted targets were present within non-intuitive areas of the metabolic network including 5-formyltetrahydrofolate deformylase and four mitochondrial enzymes, alcohol dehydrogenase, succinyl-CoA ligase, aspartate aminotransferase, and malate dehydrogenase. Six of the targets were validated in the highest performing 3-HP strain used for multi-omics data generation which contained a prior disruption of the highest scoring target malonic semialdehyde dehydrogenase. Predicted directional perturbation of five of the six tested targets significantly improved titer and rate of 3-HP production and two significantly improved yield. The greatest improvements were observed following disruption of the non-intuitive target succinyl-CoA ligase which increased titer by 39% and yield by 29% (to 20.4 g/L 3-HP and 0.31 g 3-HP/g glucose) over the strains used for training. This study demonstrates the utility of Bayesian Metabolic Control Analysis and highlights the ability to predict meaningful genetic targets in unexpected areas of metabolism to improve engineered strains for bioconversion.

3-hydroxypropionic acid↗

Genome reduction improves octanoic acid production in scale down bioreactors

Microorganisms in large-scale bioreactors are exposed to heterogeneous environmental conditions due to physical mixing constraints. Nutritional gradients can lead to transient expression of energetically wasteful stress responses and as a result, can reduce the titres, rates and yields of a bioprocess at larger scales. To what extent these process parameters are impacted is often unknown and therefore bioprocess scale-up comes with major risk. Designing platform strains to account for these intermittent stresses before introducing synthesis pathways is one strategy for de-risking bioprocess development. For example, Escherichia coli strain RM214 is a derivative of wild-type MG1655 that has had several genes and whole operons removed from its genome based on their metabolic cost. In this study, we engineered E. coli strain RM214 (referred to as WG02) to produce octanoic acid from glycerol in batch-flask and fed-batch bioreactor cultivations and compared it to an octanoic acid-producing E. coli MG1655 (WG01). In batch flask cultivations, the two strains performed similarly. However, in carbon limited fed-batch bioreactor cultivations, WG02 provided a greater than 22% boost to biomass compared to WG01 while maintaining similar titres of octanoic acid. Reducing the biomass accumulation of WG02 with nitrogen limited fed-batch cultivation resulted in a 16% improvement in octanoic acid titre over WG01. Finally, in a scale-down system consisting of a stirred tank reactor (representing a well-mixed zone) and plug flow reactor (representing an intermittent carbon starvation zone), WG02 again improved octanoic acid titre by almost 18% while maintaining similar biomass concentrations as WG01.

59 BASIC BIOLOGICAL SCIENCES↗

Data for "Anti-Pdc1p Nanobody as a Genetically Encoded Inhibitor of Ethanol Production Enables Dual Transcriptional and Post-translational Controls of Yeast Fermentations"

Microbial fermentation provides a sustainable method of producing valuable chemicals. Adding dynamic control to fermentations can significantly improve titers, but most systems rely on transcriptional controls of metabolic enzymes, leaving existing intracellular enzymes unregulated. This limits the ability of transcriptional controls to switch off metabolic pathways, especially when metabolic enzymes have long half-lives. We developed a two-layer transcriptional/post-translational control system for yeast fermentations. Specifically, the system uses blue light to transcriptionally activate the major pyruvate decarboxylase PDC1 , required for cell growth and concomitant ethanol production. Switching to darkness transcriptionally inactivates PDC1 and instead activates the anti-Pdc1p nanobody, NbJRI, to act as a genetically encoded inhibitor of Pdc1p accumulated during the growth phase. This dual transcriptional/post-translational control improves the production of 2,3-BDO and citramalate by up to 100 and 92% compared to using transcriptional controls alone in dynamic two-phase fermentations. This study establishes the NbJRI nanobody as an effective genetically encoded inhibitor of Pdc1p that can enhance the production of pyruvate-derived chemicals.

metabolic engineering↗

Biotransformation of Pesticides across Biological Systems: Molecular Mechanisms, Omics Insights, and Biotechnological Advances for Environmental Sustainability

The widespread application of pesticides such as organophosphates, organochlorides, and triazines in modern agriculture has led to their notable presence in soils, water bodies, and food chains, raising concerns about persistence, bioaccumulation, and adverse effects on nontarget organisms. Biotransformation, the enzymatic transformation of xenobiotic compounds by microorganisms, plants, and animals, plays a pivotal role in the degradation and detoxification of these chemicals. This review provides a comprehensive examination of the mechanisms, key enzyme classes (e.g., hydrolases, oxidoreductases, transferases), and environmental factors influencing pesticide biotransformation across different biological systems. Recent advances in omics technologies have revolutionized the understanding of microbial and plant metabolism, while synthetic biology offers opportunities for engineering enhanced degradation capabilities. The environmental fate of transformation products is also discussed, together with a critical analysis of challenges, unresolved questions, and future research directions, offering a holistic perspective on pesticide biotransformation as a key process for mitigating chemical pollution.

Biotransformation↗

Conjugation-based genome engineering enables rapid prototyping and bioproduction in non-model bacteria

Abstract Non-model bacteria offer unique metabolic capabilities for sustainable bioproduction, yet their limited genetic accessibility hinders systematic strain development. Here we present conjugation-based serine recombinase-assisted genome engineering (cSAGE), a broad-host-range platform that enables predictable, iterative genomic integration in transformation-resistant bacteria. cSAGE combines conjugative DNA delivery, standardized low-copy vectors, orthogonal recombinases, and modular genetic parts to support rapid pathway assembly and cross-host benchmarking. Using purple nonsulfur bacteria as a testbed, we integrate promoter engineering, multi-payload genome modification, and genome-scale metabolic modeling to empirically evaluate host-dependent pathway performance. Applying this workflow, we identify strain-specific differences in photosynthetic conversion of lignin-derived p -coumarate to the thermoplastic precursor p -vinylphenol. By enabling genome engineering and functional comparison across diverse bacteria using a single plasmid system, cSAGE provides a general framework for non-model strain prototyping and biotransformation discovery.

Guzman, Michael S. [Department of Chemical Enginee↗

Developing a Hybrid Space Suit Simulator as a Research Tool for Assessing Extravehicular Activity Relevant Workload

Suited testing time is limited and hard to come by, expensive, and requires a whole team to operate and monitor the suit; however, pressurized spacesuits are not always necessary for initial developmental efforts or assessment of informatics. The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a Hybrid Space Suit Simulator (HS3) to support characterization of human performance during planetary surface exploration analogs. The primary goal of HS3 is to create a low cost, modular, and unpressurized suit simulator for use as a research tool which provides relevant physical and cognitive workload approximations with EVA-like immersion during planetary extravehicular activity (EVA) simulations. HS3 consists of a soft suit, communication, thermal control, gloves, boots, helmet, and integrated bioinformatics/sensors. The modular design of HS3 allows it to be modified to support test objectives as needed. HS3 enables H-3PO and NASA to complete critical human health and performance testing to address open risks and knowledge gaps in a timely, repeatable, and controlled manner. Four engineering runs were completed with HS3 as a baseline for capability. General workload was assessed while traversing on a passive treadmill and conducting geology tasks. Liquid cooling temperature, suit temperature, and metabolic rates were collected during one extended three-hour engineering run. For the duration of the engineering run the suit temperature was 26.6 ± 1.58 °C while suit humidity was 53.7 ± 5.11 %. Helmet temperature was higher than suit temperature at 27.8 ± 1.58 °C. Helmet humidity was lower than average suit humidity at 50.12 ± 11.3 °C. The liquid cooling temperature remained stable at 22.4 ± 1.62 °C. Resting metabolic rate was 597 ± 88 BTU/hr while metabolic rate increased during traverse tasks at 1500 ± 319 BTU/hr and geology tasks at 921 ± 129 BTU/hr. Following the engineering runs, a characterization study will be conducted. The subjects will perform EVA-like scenarios in both a shirtsleeve only and donned HS3, consisting of EVA task circuit focused on traversing, geological rock sampling, and task board manipulation. The characterization study will provide data capability for implementing HS3 in analog EVA testing environments.

Monica Hew↗

Design of Plant Gas Exchange Experiments in a Variable Pressure Growth Chamber

Sustainable human presence in extreme environments such as lunar and martian bases will require bioregenerative components to human life support systems where plants are used for generation of oxygen, food, and water. Reduced atmospheric pressures will be used to minimize mass and engineering requirements. Few studies have assessed the metabolic and developmental responses of plants to reduced pressure and varied oxygen atmospheres. The first tests of hypobaric pressures on plant gas exchange and biomass production at the Johnson Space Center will be initiated in January 1996 in the Variable Pressure Growth Chamber (VPGC), a large, closed plant growth chamber rated for 10.2 psi. Experiments were designed and protocols detailed for two complete growouts each of lettuce and wheat to generate a general database for human life support requirements and to answer questions about plant growth processes in reduced pressure and varied oxygen environments. The central objective of crop growth studies in the VPGC is to determine the influence of reduced pressure and reduced oxygen on the rates of photosynthesis, dark respiration, evapotranspiration and biomass production of lettuce and wheat. Due to the constraint of one experimental unit, internal controls, called pressure transients, will be used to evaluate rates of CO2 uptake, O2 evolution, and H2O generation. Pressure transients will give interpretive power to the results of repeated growouts at both reduced and ambient pressures. Other experiments involve the generation of response functions to partial pressures of O2 and CO2 and to light intensity. Protocol for determining and calculating rates of gas exchange have been detailed. In order to build these databases and implement the necessary treatment combinations in short time periods, specific requirements for gas injections and removals have been defined. A set of system capability checks will include determination of leakage rates conducted prior to the actual crop growouts. Schedules of experimental events for lettuce and wheat are outlined and include replications in time of diurnal routines, pressure transients, variable pO2, pO2/pCO2 ratio, and light intensity responses.

Corey, Kenneth A.↗

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗

Reexamination of METMAN, Recommendations on Enhancement of LCVG, and Development of New Concepts for EMU Heat Sink

METMAN is a 41-node transient metabolic computer code developed in 1970 and revised in 1989 by Lockheed Engineering and Sciences, Inc. This program relies on a mathematical model to predict the transient temperature distribution in a body influenced by metabolic heat generation and thermal interaction with the environment. A more complex 315-node model is also available that not only simulates the thermal response of a body exposed to a warm environment, but is also capable of describing the thermal response resulting from exposure to a cold environment. It is important to compare the two models for the prediction of the body's thermal response to metabolic heat generation and exposure to various environmental conditions. Discrepancies between the twi models may warrant an investigation of METMAN to ensure its validity for describing the body's thermal response in space environment. The Liquid Cooling and Ventilation Garment is a subsystem of the Extravehicular Mobility Unit (EMU). This garment, worn under the pressure suit, contains the liquid cooling tubing and gas ventilation manifolds; its purpose is to alleviate or reduce thermal stress resulting from metabolic heat generation. There is renewed interest in modifying this garment through identification of the locus of maximum heat transfer at body-liquid cooled tubing interface. The sublimator is a vital component of the Primary Life Support System (PLSS) in the EMU. It acts as a heat sink to remove heat and humidity from the gas ventilating circuit and the liquid cooling loop of the LCVG. The deficiency of the sublimator is that the ice, used as the heat sink, sublimates into space. There is an effort to minimize water losses in the feedwater circuit of the EMU. This requires developing new concepts to design an alternative heat sink system. Efforts are directed to review and verify the heat transfer formulation of the analytical model employed by METMAN. A conceptual investigation of regenerative non-venting heat-sink subsystem for the EMU is recommended.

Karimi, Amir↗

Considerations for Microbial Production of Oleochemicals

Fatty acids and their derivatives have a wide variety of uses in industrial and consumer products. While these compounds are traditionally derived from petrochemicals or natural lipids, microbes offer an alternate route for production. This chapter provides an overview of the highly conserved fatty acid biosynthetic pathway. Though highly conserved, key differences among organisms in how the pathway is realized and how it interacts within metabolism are also addressed. Finally, this chapter details engineering interventions aimed at increasing production of and upgrading fatty acids.

Hubbard, Susan C↗

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗

Building on success in systems design of high yielding low‐input energycanes for marginal lands

Energycane and Miscanthus are highly sustainable and productive perennial grasses that produce a large annual crop of shoot biomass. Theoretical analysis of metabolism has suggested that these crops could be engineered to accumulate 20% of their harvestable biomass as oil. Given their much higher biomass yields than soybean and canola, this would yield several times more oil per acre. This could be converted to biodiesel or sustainable aviation fuel (SAF) by well‐established technologies. Energycane is well suited to the southeast quarter of the contiguous 48 states and Miscanthus to the northeast quarter. The objective of this award was to provide the foundation for converting these crops to oilcanes from DNA constructs and transformation to field trials and pilot plant development of oil extraction. Genetic engineering focused on development and implementation of improved constructs to increase oil levels, and increased photosynthesis to support the additional energy needed for oil production. The specific progress in each of our subtasks follows together with the products arising.

09 BIOMASS FUELS↗

Development of emerging model microorganisms: Megasphaera elsdenii for biomass and organic acid upgrading to fuels and chemicals

The metabolic diversity of microorganisms in nature represents a largely untapped source of valuable compounds that are difficult or impossible to produce in the limited number of available model systems. Efforts to produce longer-chain alcohols, such as hexanol, in organisms like Escherichia coli have met with limited success; production of C6 and larger products remains low, highlighting the challenges of extending chain elongation pathways beyond a single cycle. Megasphaera elsdenii naturally condenses acetyl-CoA to efficiently generate C4–C8 organic acids, making it a promising candidate for producing fuels and chemicals from lactate and plant-derived carbohydrates. This high native flux through the chain elongation pathway offers the potential for higher yields and titers of medium-chain products, such as hexanol, compared to conventional hosts. Recent advances—most notably the development of a transformation method for M. elsdenii—have further opened the organism to detailed physiological studies and bioengineering. While full development of M. elsdenii as a hexanol-producing platform was not achieved, significant progress was made in understanding its metabolism and building foundational genetic tools for future engineering.

60 APPLIED LIFE SCIENCES↗

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗

Feedstock-efficient conversion through hydrogen and formate-driven metabolism in Escherichia coli

Product yields for biomanufacturing processes are often constrained by the tight coupling of cellular energy generation and carbon metabolism in sugar-based fermentation systems. To overcome this limitation, we engineered Escherichia coli to utilize hydrogen gas (H 2 ) and formate (HCOO - ) as alternative sources of energy and reducing equivalents, thereby decoupling energy generation from carbon metabolism. This approach enabled precise suppression of decarboxylative oxidation during acetate growth, with 86.6 ± 1.6 % of electrons from hydrogen gas (via soluble hydrogenase from Cupriavidus necator H16) and 98.4 ± 3.6 % of electrons from formate (via formate dehydrogenase from Pseudomonas sp. 101) offsetting acetate oxidation. Hydrogen gas supplementation led to a titratable and stoichiometric reduction in CO 2 evolution in acetate-fed cultures. Metabolomic analysis suggests that this metabolic decoupling redirects carbon flux through the glyoxylate shunt, partially bypassing two decarboxylative steps in the TCA cycle. Here, we demonstrated the utility of this strategy by applying it to mevalonate biosynthesis, where formate supplementation during glucose fermentation increased titers by 57.6 % in our best-performing strain. Flux balance analysis further estimated that 99.0 ± 2.8 % of electrons from formate were used to enhance mevalonate production. These findings highlight a broadly applicable strategy for enhancing biomanufacturing efficiency by leveraging external reducing power to optimize feedstock and energy use.

Biomanufacturing↗

Expression of dehydroshikimate dehydratase in poplar induces transcriptional and metabolic changes in the phenylpropanoid pathway

Abstract Modification of lignin in feedstocks via genetic engineering aims to reduce biomass recalcitrance to facilitate efficient conversion processes. These improvements can be achieved by expressing exogenous enzymes that interfere with native biosynthetic pathways responsible for the production of the lignin precursors. In planta expression of a bacterial 3-dehydroshikimate dehydratase in poplar trees reduced lignin content and altered the monomer composition, which enabled higher yields of sugars after cell wall polysaccharide hydrolysis. Understanding how plants respond to such genetic modifications at the transcriptional and metabolic levels is needed to facilitate further improvement and field deployment. In this work, we acquired fundamental knowledge on lignin-modified poplar expressing 3-dehydroshikimate dehydratase using RNA-seq and metabolomics. The data clearly demonstrate that changes in gene expression and metabolite abundance can occur in a strict spatiotemporal fashion, revealing tissue-specific responses in the xylem, phloem, or periderm. In the poplar line that exhibited the strongest reduction in lignin, we found that 3% of the transcripts had altered expression levels and ~19% of the detected metabolites had differential abundance in the xylem from older stems. The changes affected predominantly the shikimate and phenylpropanoid pathways as well as secondary cell wall metabolism, and resulted in significant accumulation of hydroxybenzoates derived from protocatechuate and salicylate.

59 BASIC BIOLOGICAL SCIENCES↗

Defined synthetic microbial communities colonize and benefit field-grown sorghum

The rhizosphere constitutes a dynamic interface between plant hosts and their associated microbial communities. Despite the acknowledged potential for enhancing plant fitness by manipulating the rhizosphere, the engineering of the rhizosphere microbiome through inoculation has posed significant challenges. These challenges are thought to arise from the competitive microbial ecosystem where introduced microbes must survive, and the absence of adaptation to the specific metabolic and environmental demands of the rhizosphere. Here, in this study, we engineered a synthetic rhizosphere community (SRC1) with the anticipation that it would exhibit a selective advantage in colonizing the host Sorghum bicolor, thereby potentially fostering its growth. SRC1 was assembled from bacterial isolates identified either for their potential role in community cohesion through network analysis or for their ability to benefit from host-specific exudate compounds. The growth performance of SRC1 was assessed in vitro on solid media, in planta under gnotobiotic laboratory conditions, and in the field. Our findings reveal that SRC1 cohesion is most robust when cultivated in the presence of the plant host under laboratory conditions, with lineages being lost from the community when grown either in vitro or in a native field setting. We establish that SRC1 effectively promotes the growth of both above- and below-ground plant phenotypes in both laboratory and native field contexts. Furthermore, in laboratory conditions, these growth enhancements correlate with the transcriptional dampening of lignin biosynthesis in the host. Collectively, these results underscore the potential utility of synthetic microbial communities for modulating crop performance in controlled and native environments alike.

60 APPLIED LIFE SCIENCES↗