Abstract Dopamine plays a critical role in animal physiology and interactions with gut microbes. In plants, dopamine is known to function in plant defense and abiotic stress tolerance; however, its role in mediating plant-microbiome interactions remains unexplored. In this study, we show that dopamine is one of the most abundant exometabolites with natural variation in root exudates across diverse Brachypodium distachyon lines, suggesting a potential role in rhizosphere microbial assembly. To further investigate this, we colonized ten natural B. distachyon lines with a 16-member bacterial synthetic community (SynCom), collected metabolomic and 16S rRNA sequencing data, and performed an association analysis. Our results reveal that dopamine levels in root exudates were significantly associated with the abundance of six SynCom members in a hydroponic system. In vitro growth studies demonstrated that dopamine had a significant effect on the growth of the same six bacterial isolates. Additionally, treating soil directly with dopamine enriched Actinobacteria, consistent with both the SynCom-dopamine correlations and the isolate growth results. Collectively, our study underscores the selective influence of dopamine on rhizosphere microbial communities, with implications for precision microbiome management.

Understanding and predicting bacterial substrate preferences has broad utility from microbial interactions to selecting prebiotics. Isolate exometabolite profiling directly measures which compounds a given microbe utilizes from an array of metabolites in the environment. However, modeling, mining, and integrating these data are challenging. Here, we introduce a Bayesian Personalized Ranking (BPR) model applied to substrate preferences which we find learns to rank compounds by a given microbe’s preference. It was found to outperform the other ranking models (AUC = 0.93), proved robust to ablation, showed strong within-genus isolate pairs correlation (Spearman rank = 0.78) and predictive ability for new data. BPR was then used to create the Web of Microbes (WoM) Agent by integrating it with the Phydon growth model and Large Language Model (LLM) for autonomous orchestration tool calling and analysis. The WoM Agent accurately predicted substrate consumption by existing strain grown on a novel medium and correctly identified bacteria enriched in soil metabolite spike-in experiments. Additionally, the WoM Agent can use autonomous reasoning including to predict substrates that will selectively promote the growth of one clade of bacteria over another including helping interpret results and suggest new hypotheses and experiments. We anticipate broad applications in microbial cultivation, microbiome engineering, and environmental microbiology, with the agent’s capabilities further extensible through the integration of additional tools and use of rapidly improving LLMs.

IntroductionAdvances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools.MethodsThe EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated.ResultsThe results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement.DiscussionThe findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.

Lignin is a key component of the plant cell wall and a barrier to microbial interaction and biomass digestibility. While lignin biosynthesis has been engineered in grasses to reduce recalcitrance for agroindustrial applications, the consequences for root-associated microbial communities remain poorly understood. Here, we show in the model grass species Brachypodium distachyon that genetic modification of lignin biosynthetic genes, particularly caffeic acid O -methyltransferase ( COMT ), alters root lignin syringyl/guaiacyl (S/G) ratios, root exudate composition, and significantly reshapes the rhizosphere microbiome. Using a synthetic microbial community (SYNCOM) in controlled fabricated ecosystem (EcoFAB) environments, we observed a distinct reduction in Burkholderia colonization and enrichment of Rhodococcus in COMT mutant lines. LC-MS/MS profiling of root exudates revealed increased p -coumaric acid, caffeic acid and hydroxycinnamic acid amides in these lines, potentially linked to both metabolic remodeling and plant stress responses. These findings demonstrate that altering lignin composition can have profound, genotype-specific impacts on rhizosphere microbiome assembly, with implications for plant-microbe interactions, nutrient cycling, and biomass conversion strategies in bioenergy crops. ### Competing Interest Statement The authors have declared no competing interest. United States Department of Energy, m-CAFES SFA Australian Research Council, CE230100015, IC210100047, IH230100006

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grass Brachypodium distachyon, and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure, where Paraburkholderia sp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability of Paraburkholderia, which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies.

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

AbstractHorizontal gene transfer (HGT) is a major process by which genes are transferred within microbes in the rhizosphere. However, examining HGT remains challenging due to the complexity of mimicking conditions within the rhizosphere. Fabricated ecosystems (EcoFABs) have been used to investigate several complex processes in plant associated environments. Here we show that EcoFABs are efficient tools to examine and measure HGT frequency in the rhizosphere. We provided the first demonstration of gene transfer via triparental conjugation system in theBrachypodium distachyonrhizosphere in the EcoFABs usingPseudomonas putidaKT2440 as both donor and recipient bacterial strain with the donor having the mobilizable and non-self-transmissible plasmid. We also observed that the frequency of conjugal plasmid transfer in the rhizosphere is potentially dependent on the plant developmental stage, and composition and amount of root exudates. The frequency of conjugation also increased with higher numbers of donor cells. We have also shown the transfer of plasmid fromP. putidato anotherB. distachyonroot colonizer,Burkholderiasp. showing the possibility of HGT within a rhizosphere microbial community. Environmental stresses also influence the rate of HGT in the rhizosphere between species and genera. Additionally, we observed transfer of a non-self transmissible donor plasmid without the helper strain on agar plates when supplemented with environmental stressors, indicating reduced dependency on the helper plasmid under certain conditions. This study provides a robust workflow to evaluate conjugal transfer of engineered plasmids in the rhizosphere when such plasmids are introduced in a field or plant associated environment.ImportanceWe report the use of EcoFABs to investigate the HGT process in a rhizosphere environment. It highlights the potential of EcoFABs in recapitulating the dynamic rhizosphere conditions as well as their versatility in studying plant-microbial interactions. This study also emphasizes the importance of studying the parameters impacting the HGT frequency. Several factors such as plant developmental stages, nutrient conditions, number of donor cells and environmental stresses influence gene transfer within the rhizosphere microbial community. This study paves the way for future investigations into understanding the fate and movement of engineered plasmids in a field environment.

Studying plant–microbe–soil interactions is challenging due to their high complexity and variability in natural ecosystems. While fabricated ecosystems provide opportunities to recapitulate aspects of these systems in reduced complexity and controlled environments, inoculation can be a significant source of variation. To tackle this, we evaluated how different bacterial inoculation practices and plant harvesting time points affect the reproducibility of a synthetic microbial community (SynCom) in association with the model grass Brachypodium distachyon. We tested three microbial inoculation practices, seed inoculation, transplant inoculation, and seedling inoculation, and two harvesting points, early (14-day-old plants) and late (21 days postinoculation). We grew our plants and bacterial strains in sterile devices (EcoFABs) and characterized the microbial community from root, rhizosphere, and sand using 16S ribosomal RNA gene sequencing. The results showed that inoculation practices significantly affected the rhizosphere microbial community when harvesting at an early time point but not at the late time point. As the SynCom showed a persistent association with B. distachyon at 21 days postinoculation regardless of inoculation practices, we assessed the reproducibility of each inoculation method and found that transplant inoculation showed the highest reproducibility. Moreover, plant biomass was not adversely affected by transplant inoculation treatment. We concluded that bacterial inoculation while transplanting coupled with a later harvesting time point gives the most reproducible microbial community in the EcoFAB− B. distachyon−SynCom fabricated ecosystem. We recommend this method as a standardized protocol for use with fabricated ecosystem experimental systems. [Formula: see text] This manuscript has been authored by an author at Lawrence Berkeley National Laboratory under Contract number DE-AC02-05CH11231 with the U.S. Department of Energy. The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges, that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grass Brachypodium distachyon , and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure where Paraburkholderia sp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability of Paraburkholderia , which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies. ### Competing Interest Statement P.F.A. and T.R.N. are inventors of patent US11510376B2, held by the University of California, covering an Ecosystem device for determining plant-microbe interactions. In addition, T.R.N. is an advisor to Brightseed Bio. All other authors declare no competing interest.

Horizontal gene transfer (HGT) is a major process by which genes are transferred between microbes in the rhizosphere. However, examining HGT remains challenging due to the complexity of mimicking conditions within the rhizosphere. Fabricated ecosystems (EcoFABs) have been used to investigate several complex processes in plant-associated environments. Here we show that EcoFABs are efficient tools to examine and measure HGT frequency in the rhizosphere. We provide the first demonstration of gene transfer via a triparental conjugation system in the Brachypodium distachyon rhizosphere in an EcoFAB using Pseudomonas putida KT2440 as both donor and recipient bacterial strain with the donor containing a mobilizable and non-self-transmissible plasmid. We observed that the frequency of plasmid transfer in the rhizosphere is potentially dependent on the plant developmental stage and the composition and amount of root exudates. The frequency of plasmid transfer also increased with higher numbers of donor cells. We demonstrate the transfer of plasmid from P. putida to another B. distachyon root colonizer, Burkholderia sp. OAS925, showing HGT within a rhizosphere microbial community. Environmental stresses also influenced the rate and efficiency of HGT in the rhizosphere between different species and genera. This study provides a robust workflow to evaluate transfer of engineered plasmids in the rhizosphere when such plasmids are potentially introduced in a field or other plant-associated environments.IMPORTANCEWe report the use of EcoFABs to investigate the HGT process in a rhizosphere environment. It highlights the potential of EcoFABs in recapitulating the dynamic rhizosphere conditions as well as their versatility in studying plant-microbe interactions. This study also emphasizes the importance of studying the parameters impacting the HGT frequency. Several factors such as plant developmental stages, nutrient conditions, number of donor cells, and environmental stresses influence gene transfer within the rhizosphere microbial community. This study paves the way for future investigations into understanding the fate and movement of engineered plasmids in a field environment.

Understanding plant-microbe interactions requires examination of root exudation under nutrient stress using standardized and reproducible experimental systems. We grew Brachypodium distachyon hydroponically in fabricated ecosystem devices (EcoFAB 2.0) under three inorganic nitrogen forms (nitrate, ammonium, and ammonium nitrate), followed by nitrogen starvation. Analyses of exudates with liquid chromatography-tandem mass spectrometry, biomass, medium pH, and nitrogen uptake showed EcoFAB 2.0's low intratreatment data variability. Furthermore, the three inorganic nitrogen forms caused differential exudation, generalized by abundant amino acids-peptides and alkaloids. Comparatively, nitrogen deficiency decreased nitrogen-containing compounds but increased shikimates-phenylpropanoids. Subsequent bioassays with two shikimates-phenylpropanoids (shikimic and p-coumaric acids) on soil bacteria or Brachypodium seedlings revealed their distinct capacity to regulate both bacterial and plant growth. Our results suggest that (i) Brachypodium alters exudation in response to nitrogen status, which can affect rhizobacterial growth, and (ii) EcoFAB 2.0 is a valuable standardized plant research tool.

IntroductionStudying plant-microbe interactions is one of the key elements in understanding the path to sustainable agricultural practices. These interactions play a crucial role in ensuring survival of healthy plants, soil and microbial communities. Many platforms have been developed over the years to isolate these highly complex interactions however, these are designed for small model plants. This creates a need for complementary devices for larger plants, such as sorghum.MethodsThis work introduces a novel platform, EcoFAB 3.0, which is designed to enable studying bioenergy plants such as sorghum for up to 4 weeks in a controlled sterile environment. Several other advantages of this platform such as dark root chambers and user-friendly assembly are also discussed in this work.Results and discussionEcoFAB 3.0 was found to replicate previous greenhouse and field observations when comparing an engineered sorghum line overproducing 4-hydroxybenzoic acid (4-HBA) and wildtype (variety BTx430). Consistent with greenhouse and field observations, it was found that the engineered line of sorghum grown in EcoFAB 3.0 had a higher 4-HBA content and a lower dry biomass.

Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.

For a deeper and comprehensive understanding of the composition and function of rhizosphere microbiomes, we need to focus at the scale of individual roots in standardized growth containers. Root exudation patterns are known to vary along distinct parts of the root even in juvenile plants giving rise to spatially distinct microbial niches. To address this, we analyzed the microbial community from two spatially distinct zones of the developing primary root (tip and base) in young Brachypodium distachyon grown in natural soil using standardized fabricated ecosystems known as EcoFABs as well as in more conventional pot and tubes. 16S rRNA based community analysis showed a strong rhizosphere effect resulting in significant enrichment of several OTUs belonging to Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria. However, microbial community composition did not differ between root tips and root base or across different growth containers. Functional analysis of bulk metagenomics revealed significant differences between root tips and bulk soil. The genes associated with different metabolic pathways and root colonization were enriched in root tips. On the other hand, genes associated with nutrient-limitation and environmental stress were prominent in the bulk soil compared to root tips, implying the absence of easily available, labile carbon and nutrients in bulk soil relative to roots. Such insights into the relationships between developing root and microbial communities are critical for judicious understanding of plant-microbe interactions in early developmental stages of plants.

ABSTRACTDigital cameras have the ability to capture daily images of plant roots, allowing for the estimation of root biomass. However, the complexities of root structures and noisy image backgrounds pose challenges for advanced phenotyping. Manual segmentation methods are laborious and prone to errors, which hinders experiments involving several plants. This paper introduces Rhizonet, a supervised deep learning approach for semantic segmentation of plant root images. Rhizonet harnesses a Residual U-Net backbone to enhance prediction accuracy, incorporating a convex hull operation to precisely outline the largest connected component. The primary objective is to accurately segment the biomass of the roots and analyze their growth over time. The input data comprises color images of various plant samples within a hydroponic environment known as EcoFAB, subject to specific nutrition treatments. Validation tests demonstrate the robust generalization of the model across experiments. This research pioneers advances in root segmentation and phenotype analysis by standardizing processes and facilitating the analysis of thousands of images while reducing subjectivity. The proposed root segmentation algorithms contribute significantly to the precise assessment of the dynamics of root growth under diverse plant conditions.

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS "barcodes" (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

Microorganisms have evolved various life-history strategies to survive fluctuating resource conditions in soils. However, it remains elusive how the life-history strategies of microorganisms influence their processing of organic carbon, which may affect microbial interactions and carbon cycling in soils. Here, we characterized the genomic traits, exometabolite profiles, and interactions of soil bacteria representing copiotrophic and oligotrophic strategists. Isolates were selected based on differences in ribosomal RNA operon (rrn) copy number, as a proxy for life-history strategies, with pairs of "high" and "low" rrn copy number isolates represented within the Micrococcales, Corynebacteriales, and Bacillales. We found that high rrn isolates consumed a greater diversity and amount of substrates than low rrn isolates in a defined growth medium containing common soil metabolites. We estimated overlap in substrate utilization profiles to predict the potential for resource competition and found that high rrn isolates tended to have a greater potential for competitive interactions. The predicted interactions positively correlated with the measured interactions that were dominated by negative interactions as determined through sequential growth experiments. This suggests that resource competition was a major force governing interactions among isolates, while cross-feeding of metabolic secretion likely contributed to the relatively rare positive interactions observed. By connecting bacterial life-history strategies, genomic features, and metabolism, our study advances the understanding of the links between bacterial community composition and the transformation of carbon in soils.

This protocol describes the production of the EcoFAB-N chamber published in the study Kuang et al., "N-dependent dynamics of root growth and nitrate and ammonium uptake are altered by bacteria Herbaspirillum seropedicae in cereal modelBrachypodium distachyon" The volume of the EcoFAB-N chamber is 4 mL. The mold is re-designed using SolidWorks 2017 software (Fig SM1) and the 3D printer files are available as attachments to this protocol. Figure: EcoFAB-N mold. All in one, single piece EcoFAB-N (a) compared to components of EcoFAB mold from Sasse et al 2019 (frame (b); base (c); insert (d)), the insert (d) is used in both molds.

Exometabolomics is an approach to assess how microorganisms alter, or react to their environments through the depletion and production of metabolites. It allows the examination of how soil microbes transform the small molecule metabolites within their environment, which can be used to study resource competition and cross-feeding. This approach is most powerful when used with defined media that enable tracking of all metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse soil bacteria but also is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 108 of the 110 phylogenetically diverse (spanning 36 different families) soil bacterial isolates tested and all of its metabolites were trackable through LC–MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for growing and characterizing diverse microbial isolates and communities.