SEARCH · bioRxiv Science

Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55Linked to original sources

bioRxiv2017

Signaling architectures that transmit unidirectional information despite retroactivity

A signaling pathway transmits information from an upstream system to downstream systems, ideally in a unidirectional fashion. A key obstacle to unidirectional transmission is retroactivity, the additional reaction flux that affects a system once its species interact with those of downstream systems. This raises the fundamental question of whether signaling pathways have developed specialized architectures that overcome retroactivity and transmit unidirectional signals. Here, we propose a general procedure based on mathematical analysis that provides an answer to this question. Using this procedure, we analyze the ability of a variety of signaling architectures to transmit one-way (from upstream to downstream) signals, as key biological parameters are tuned. We find that single stage phosphorylation and phosphotransfer systems that transmit signals from a kinase show a stringent design trade-off that hampers their ability to overcome retroactivity. Interestingly, cascades of these architectures, which are highly represented in nature, can overcome this trade-off and thus enable unidirectional transmission. By contrast, phosphotransfer systems, and single and double phosphorylation cycles that transmit signals from a substrate are unable to mitigate retroactivity effects, even when cascaded, and hence are not well suited for unidirectional information transmission. Our results identify signaling architectures that, allowing unidirectional transmission of signals, embody modular processes that conserve their input/output behavior across multiple contexts. These findings can be used to decompose natural signal transduction networks into modules, and, at the same time, they establish a library of devices that can be used in synthetic biology to facilitate modular circuit design.

biophysics↗

bioRxiv2024

Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale

Language models applied to protein sequences have become a panacea, enabling therapeutics development, materials engineering, and core biology research. Despite the successes of protein language models, genome language models remain nascent. Recent studies suggest the bottleneck is data volume or modeling context size, since long-range interactions are widely acknowledged but sparsely annotated. However, it may be the case that even short DNA sequences are modeled poorly by existing approaches, and current models are unable to represent the wide array of functions encoded by DNA. To study this, we develop AIDO.DNA, a pretrained module for DNA representation in an AI-driven Digital Organism [1]. AIDO.DNA is a seven billion parameter encoder-only transformer trained on 10.6 billion nucleotides from a dataset of 796 species. By scaling model size while maintaining a short context length of 4k nucleotides, AIDO.DNA shows substantial improvements across a breadth of supervised, generative, and zero-shot tasks relevant to functional genomics, synthetic biology, and drug development. Notably, AIDO.DNA outperforms prior encoder-only architectures without new data, suggesting that new scaling laws are needed to achieve computeoptimal DNA language models. Models and code are available through Model-Generator in https://github.com/genbio-ai/AIDO and on Hugging Face at https://huggingface.co/genbio-ai.

bioinformatics↗

bioRxiv2025

Disentangling the cost of gene expression

Gene expression is essential for biological functions but also incurs a fitness cost. Although the fitness cost can be experimentally measured as the relative reduction in growth rate, it remains unclear how the cost quantitatively depends on different limiting factors. In this work, we establish a resource competition model and disentangle the fitness cost into components arising from limiting resources, including ribosomes, RNA polymerases, and transcription factors. Comparing our model predictions with experimental data for Saccharomyces cerevisiae, we demonstrate that ribosome competition dominates the translation cost, and that transcription factor competition dominates the transcription cost. Our model reveals that the fitness costs originate from the processes of transcription and translation, rather than from the products. The model also systematically connects the fitness cost to genetic and environmental properties, making quantitative predictions consistent with various experimental observations. Our work establishes a systematic framework for gene expression cost, guiding synthetic biology to optimize genetic design.

systems biology↗

bioRxiv2025

DNA Framework Nanoreactor for Programmable Membrane Fusion

Membrane fusion is a fundamental yet transient process that has long resisted direct structural and kinetic dissection. Here we introduce a DNA framework vesicle (DFV) nanoreactor that transforms this elusive biological phenomenon into a programmable, visualizable, and quantifiable process-a nanoreaction confined in space yet extended in time. By embedding lipid membranes and SNARE proteins within precisely defined DNA apertures, DFVs convert stochastic vesicle collisions into geometry-controlled fusion events with tunable kinetics. Cryo-electron microscopy resolves a complete sequence of six intermediates, revealing how nanoscale confinement reshapes the energetic landscape of bilayer merger. Quantitative fluorescence and nano-flow cytometry further establish a direct link between spatial design and fusion probability. Extending this concept to living cells, DFVs enable controllable membrane fusion and augmentation on VAMP2-expressing membranes, achieving direct cytosolic delivery of functional siRNA via fusion-driven transfer rather than endocytosis. This framework bridges structural precision and functional mimicry, offering a unified platform to reconstruct, quantify, and harness membrane fusion as a programmable process for synthetic biology and nanomedicine.

bioengineering↗

bioRxiv2018

ElectroPen: An ultralow-cost piezoelectric electroporator

Electroporation is a basic yet powerful method for delivering small molecules (RNA, DNA, drugs) across cell membranes by application of an electrical field. Due to its vital role, electroporation has wide applicability from genetically engineering cells, to drug- and DNA-based vaccine delivery. Despite its broad applications in biological research, the high cost of electroporators is an obstacle for many budget-conscious laboratories. To address this need, we describe a simple, inexpensive, and hand-held electroporator inspired by a common household piezoelectric gas lighter. The proposed \"ElectroPen\" device costs about 20 cents, is portable (13 g), is fabricated on-demand using 3D-printing, and delivers repeatable exponentially decaying pulses of about 2000 V in 5 ms. We provide a proof-of-concept demonstration by genetically transforming plasmids into E. coli strains and show comparable transformation efficiency and cell growth with commercial devices, but at a fraction of the cost. Our results are validated by an independent team across the globe, providing a real-world example of democratizing science through frugal tools. Thus, the simplicity, accessibility, and affordability of our device holds potential for making modern synthetic biology accessible in high-school, community, and field-ecology laboratories.

bioengineering↗

bioRxiv2026

Programming Metabolic Dependency in Synthetic Cells Under Resource Scarcity

Engineering synthetic cells where metabolism directly controls gene expression is one of the greatest challenges in synthetic biology. This coupling between metabolic activity and protein synthesis is also thought to have been a vital step in the evolution of the earliest cellular life. This fundamental process is essential for developing minimal, self-sustaining cells, both engineered for biotechnology applications and as models explaining the origins of life. Here, we present a programmable cell-free platform that links metabolic activity to translation under defined resource limitations. Using an engineered amino-acid-dependent cell-free translation system, we introduced a tunable metabolic bottleneck (depleting tyrosine). This enabled imposing a controlled metabolic constraint on protein synthesis. To alleviate this constraint, we then incorporated phenylalanine hydroxylase (PAH) as a minimal module for tyrosine synthesis. The PAH-driven tyrosine synthesis established a system in which protein expression is directly controlled by amino acid biosynthesis. This relationship was recapitulated in liposome compartments, resulting in three distinct synthetic cell populations. The metabolically active population had significantly higher fitness in protein production. Overall, this work establishes an experimentally tractable platform to investigate how primitive cells may have evolved internal metabolic capabilities, and it represents a foundational step toward constructing more autonomous and self-regulating synthetic cells.

biochemistry↗

bioRxiv2025

Remote sensing of endogenous pigmentation by inducible synthetic circuits in grasses

Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built, and tested in plants to demonstrate proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterization method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pathways, stimulating plants to overproduce secondary metabolites with value to human health. Applying inducible pigmentation coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems. SummaryO_ST_ABSSynbio tools for C4 grass modelC_ST_ABS[bullet] Advantage of synthetic switches as tools for biopharming and functional genomics [bullet]Our workflow to optimize the gene circuits from a transient system to a stable transgenic [bullet]Testing and taming golden gate elements in monocot system, S. viridis

plant biology↗

bioRxiv2025

The Dark Side of Biomolecular Condensates: Quantifying the Role of Nucleic Acids

Liquid-Liquid Phase Separation (LLPS) is being increasingly recognized as a major organizational principle for proteins and nucleic acids (NAs) in cells, as well as a promising strategy for synthetic biology and biomedical applications. Extensive work has explored the role of protein sequence and properties in the regulation of LLPS. On the contrary, the role of nucleic acids has often been overlooked. Here, to fill this gap we focus on model systems made of oligonucleotides with tuned lengths and degree of hybridization, mixed with a moderately charged, disordered peptide. Combining multiple length scales through experiments and molecular dynamics simulations, we unravel the distinct effect of the properties of NAs on the phase behavior and we propose a metric for the stability of biocondensates. We also characterize the conditions for the onset of liquid crystalline order in the droplets and we show that it is associated with a dramatic slowing down of the dynamics of oligonucleotides, while peptides retain high mobility. Our results can be generalized to natural and non-natural NAs of arbitrary structure, providing a guide for the design of synthetic NA-containing coacervates. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/683552v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@179be04org.highwire.dtl.DTLVardef@1aa0192org.highwire.dtl.DTLVardef@4d411aorg.highwire.dtl.DTLVardef@1ed238d_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Programmable DNA shell scaffolds for directional membrane budding

In the pursuit of replicating biological processes at the nanoscale, controlling cellular membrane dynamics has emerged as a key area of interest. Here, we report a system mimicking virus assembly to control directional membrane budding. We employ three-dimensional DNA origami techniques to construct cholesterol-modified triangles that self-assemble into polyhedral shells on lipid vesicles, resulting in gradual curvature induction, bud formation and spontaneous neck scission. Strategic positioning of cholesterols on the triangle surface provides control over the directionality of bud growth and yields daughter vesicles with DNA endo- or exoskeletons reminiscent of clathrin-coated vesicles. This process occurs with rapid kinetics and across various lipid compositions. When combined into a two-step process, nested bivesicular objects with DNA shells encapsulated between lipid vesicles could be produced. Our work replicates key aspects of natural endocytic and exocytic pathways, opening new avenues for exploring membrane mechanics and applications in targeted drug delivery and synthetic biology.

biophysics↗

bioRxiv2025

Engineering and evaluation of Sinorhizobium meliloti nodulation (nod) gene reporter systems in rhizobia and non-rhizobia

Developing N{square}-fixing partnerships between diazotrophic microbes and non-legumes can enhance soil fertility and reduce dependence on synthetic fertilizers. Unlike legumes, non-legumes lack the genetic ability to form root nodule symbiosis with rhizobia but can form facultative associations with free-living diazotrophs. Engineering root nodule symbiosis in non-legumes remains a central aim in synthetic biology to enhance biological nitrogen fixation in cereals. Such a symbiosis will require specific symbiotic signaling that mimics the rhizobium-legume interaction. However, lack of effective tools for identifying compatible and engineerable microbial partners is a key challenge. To address this, we have developed inducible nodulation (nod) gene reporters to screen both rhizobial and non-rhizobial strains capable of expressing Sinorhizobium meliloti nod genes, which encode bacterial signals initiating nodule formation in legumes. The reporters include a superfolder GFP reporter controlled by the inducible nod box promoter (PnodA), plant signal-dependent activators nodD1 and nodD2, and a constitutively mScarlet-I marker, named nodD1-PnodA and nodD2-PnodA. Their functionality was validated in various S. meliloti backgrounds using in vitro induction and two in planta induction approaches. These advancements facilitated the identification of both rhizobia and non-rhizobia capable of expressing S. meliloti nod genes, thereby supporting the development of synthetic N{square}-fixing symbioses in cereals.

microbiology↗

bioRxiv2020

A multiplexed, automated evolution pipeline enablesscalable discovery and characterization of biosensors

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Using aptamer-coupled ribozyme libraries and a novel ribozyme regeneration method, we developed de novo rapid in vitro evolution of RNA biosensors (DRIVER) that enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identified and validated biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors were applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors were also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

bioengineering↗

bioRxiv2026

Efficient approximate Bayesian inference for large, heterogeneous biological datasets

Simulation-based inference (SBI) offers a route to connect biological data with mechanistic models. At the frontier of biology, datasets continue to grow in scale and richness, and increasingly record heterogeneity between individual observations that a mechanistic model can take into account. Approximate Bayesian Computation (ABC) remains a popular approach to SBI due to its ease of use, but incorporating this heterogeneity often requires a separate simulation of each observation, which is prohibitively expensive at scale. A previously proposed technique, minibatch Sequential Monte Carlo ABC (SMC ABC), reduces this cost by simulating a fixed-size random subset of observations for each proposal, at the price of sampling noise that limits precision. Here, we present an adaptive minibatch approach that uses small batches during early exploration and increases the batch size as the posterior is refined. The adaptive batch size is set by the target precision and does not grow with the size of the dataset, so the savings over full-data SMC ABC increase for larger datasets. We demonstrate the method on synthetic biological models and on real single-cell gene-expression data that vary in scale and type of heterogeneity, including cell geometry and initial condition. In each case, the adaptive scheme obtains posteriors close to those of full-data SMC ABC at a fraction of the cost. Altogether, adaptive minibatching improves the tradeoff between speed and precision in SMC ABC and opens the door to mechanistic inference on large, heterogeneous biological datasets.

bioinformatics↗

bioRxiv2020

PlasmidHawk: Alignment-based Lab-of-Origin Prediction of Synthetic Plasmids

With advances in synthetic biology and genome engineering comes a heightened awareness of potential misuse related to biosafety concerns. A recent study employed machine learning to identify the lab-of-origin of DNA sequences to help mitigate some of these concerns. Despite their promising results, this deep learning based approach had limited accuracy, is computationally expensive to train, and wasnt able to provide the precise features that were used in its predictions. To address these shortcomings, we have developed PlasmidHawk for lab-of-origin prediction. Compared to a machine learning approach, PlasmidHawk has higher prediction accuracy; PlasmidHawk can successfully predict unknown sequences depositing labs 63% of the time and 80% of the time the correct lab is in the top 10 candidates. In addition, PlasmidHawk can precisely single out the signature sub-sequences that are responsible for the lab-of-origin detection. In summary, PlasmidHawk represents a novel, explainable, accurate tool for lab-of-origin prediction of synthetic plasmid sequences. PlasmidHawk is available at https://gitlab.com/treangenlab/plasmidhawk.git

bioinformatics↗

bioRxiv2026

kinGEMs: A Robust and Scalable Framework forResource-Constraint Models through StochasticTuning of Deep Learning-Predicted KineticParameters

The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated framework combining CPI-Pred, a deep learning model to predict kinetic parameters (kcat) from sequence and compound embeddings, with kinGEMs, a pipeline to incorporate these uncertainty-aware parameters into ecGEMs for metabolic optimization. By leveraging representations at multiple scales, the approach captures sequence, structure, and kinetic data to enhance model generalizability and accuracy. Rigorous benchmarking demonstrates the frameworks capability to predict growth rates and fluxes that are consistent with experimental observations and measured proteome fractions, reduce median flux variability by several folds, and yield better-constrained metabolic models by explicitly accounting for the uncertainty inherent in ML-predicted kinetic parameters. kinGEMs successfully generated ecGEMs for 93 models spanning a phylogenetically diverse set of organisms including Gram-negative and Grampositive bacteria, mycobacteria, parasitic protists, fungi, and mammalian cell lines. These innovations breakdown the barrier to application of ecGEMs to non-model organisms and open new avenues for metabolic engineering and synthetic biology in more industrially relevant hosts.

bioengineering↗

bioRxiv2022

Laboratory-acquired mutations fall outside the wild-type alleleome of Escherichia coli

Inexpensive DNA sequencing has led to a rapidly increasing number of whole genome sequences in the public domain. Natural sequence variation can now be assessed across a large number of sequenced strains of a bacterial species, resulting in the definition of the wild-type alleleome (the collection of alleles for every gene found in the species). Concurrently, laboratory evolution emerged as a new approach to address biological questions and to develop new phenotypic traits, and a large number of laboratory acquired mutations can be found in databases. The availability of this large-scale sequence variation data now allows for a detailed comparison of mutations fixed in natural versus laboratory evolutions. Such comparison shows that laboratory-acquired mutations are rarely found in the wild-type alleleome of Escherichia coli. The E. coli alleleome is highly conserved as most of the sequence variation is concentrated in about 2% of the coding region. We find that there are typically two alternate amino acids coded for in the variable locations, and switches between the two are found in the data sets. Finally, we find that adaptive laboratory mutations, unlike wild-type mutations, do not utilize the redundancy built into the genetic code: they are less likely to be synonymous and rely on changing a single nucleotide in a codon. However, the uniqueness of mutations fixed in laboratory evolutions bodes well for synthetic biology by revealing novel exploitable sequence space untouched by natural evolution.

bioengineering↗

bioRxiv2020

Convergent evolution of Streptomyces protease inhibitors involving a tRNA-mediated condensation-minus NRPS

Small peptide aldehydes (SPAs) with protease inhibitory activity are natural products typically synthesized by nonribosomal peptide synthetases (NRPS). SPAs are widely used in biotechnology, as therapeutic agents, they are physiologically relevant and regulate development of the natural hosts. During genome evolutionary analysis of Streptomyces lividans 66 we identified an NRPS-like biosynthetic gene cluster (BGC) that lacked a condensation (C) domain but included a tRNA-Utilizing Enzyme (tRUE) belonging to the leucyl/phenylalanyl (L/F) transferase family. This system was predicted to direct the synthesis of a novel SPA with protease inhibitory activity, called livipeptin. Following genome mining and phylogenomic analyses we confirmed the presence of tRUEs within diverse Streptomyces genomes, including fusions with a C-minus NRPS-like protein. We further demonstrate functional cooperation between these enzymes and provide the biosynthetic rules for the synthesis of livipeptin, expanding the known universe of acetyl-leu/phe-arginal SPAs. The L/F-transferase C-minus NRPS productive interaction was shown to be tRNA-dependent after semisynthetic assays in the presence of RNAse, which contrasts with leupeptin, an acetyl-leu-arginal SPA that we show to be produced by Streptomyces roseous ATCC 31245 via a tRUE-minus BGC with multiple complete NRPSs. Thus, livipeptin and leupeptin are the result of convergent evolution, which has driven the appearance of unprecedented biosynthetic logics directing the synthesis of protease inhibitors thought to be at the core of Streptomyces colony biology. Our results pave the way for understanding this Streptomyces trait, as well as for the discovery of novel natural products following evolutionary genome mining approaches. Abstract importanceConvergent evolution in microbiology is believed to be highly recurrent yet examples that have been comprehensively characterized are scarce. Proteases inhibition by small peptide aldehydes is at the core of many microbiological processes, both within the cell and during colony development, and in microbial ecology. Here we report the biosynthetic foundations of leupeptin, the main Streptomyces protease inhibitor, and of livipeptin, a protease inhibitor produced by Streptomyces lividans. Although these peptides belong to the same chemical class, here we show that their biosynthetic routes result from convergent evolution, as they involve unrelated biosynthetic mechanisms, including the recruitment of a tRNA-utilizing enzyme that functionally replaces the condensation domain of a nonribosomal peptide synthetase during livipeptin biosynthesis. Thus, these results pave the way for understanding Streptomyces protease inhibitors as a trait and provide unprecedented knowledge for genome mining of natural products and synthetic biology where proteases inhibition is desirable.

microbiology↗

bioRxiv2024

SpatialPPI 2.0: Enhancing Protein-Protein Interaction Prediction through Inter-Residue Analysis in Graph Attention Networks

Protein-protein interactions (PPIs) are fundamental to cellular functions, and accurate prediction of these interactions is crucial to understanding biological mechanisms and facilitating drug discovery. SpatialPPI 2.0 is an advanced graph neural network-based model that predicts PPIs by utilizing interresidue contact maps derived from both structural and sequence data. By leveraging the comprehensive PINDER dataset, which includes interaction data from the RCSB PDB and the AlphaFold database, SpatialPPI 2.0 improves the specificity and robustness of the prediction of PPI. Unlike the original SpatialPPI, the updated version employs interaction interface prediction as an intermediate step, allowing for a more effective assessment of interactions between isolated proteins. The model utilizes Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) to capture both local and global structural features. SpatialPPI 2.0 outperforms several state-of-the-art PPI and interface predictors, demonstrating superior accuracy and reliability. Furthermore, the model shows robustness when using structures predicted by AlphaFold, indicating its potential to predict interactions for proteins without experimentally determined structures. SpatialPPI 2.0 offers a promising solution for the accurate prediction of PPIs, providing insight into protein function and supporting advances in drug discovery and synthetic biology. SpatialPPI 2.0 is available at https://github.com/ohuelab/SpatialPPI2.0

bioinformatics↗

bioRxiv2026

Physiological, Behavioral, and Genetic Factors that Shape Interactions in a Plant-Growth-Promoting Maize Rhizosphere Synthetic Community

Profiling microbiomes is an important way to understand the function and composition of communities in the wild, but natural microbiomes are often highly complex and often unamendable to experimentation to reveal cause and effect relationships. By using a small group of cultivable strains to represent those found in the wild, synthetic communities are one solution to this problem. Here we describe the MAize Rhizosphere Synthetic Community (MARSc), a genome-enabled 31-member bacterial community representative of the diversity found on the roots of maize grown in Iowa soils. This community is built around Pseudomonas putida KT2440, a model maize rhizosphere colonist and synthetic biology chassis. We characterized microbe-microbe interactions and biofilm formation of MARSc members in a variety of environmental contexts, finding that both behaviors are broadly controlled by nutrient levels. Genomic analysis and microbiome profiling of these organisms revealed that annotated biofilm genes (such as surface attachment and exopolysaccharide production) correlated to rhizosphere colonization, but neither trait correlated to in vitro biofilm formation. In vitro interactions assay findings were surprisingly consistent with co-correlations of rhizosphere abundance amongst MARSc members on roots. Finally, we found that when applied to the roots, MARSc can increase maize growth under nitrogen-limiting conditions. Altogether, MARSc is a useful tool for identifying some of the factors influencing rhizosphere microbiome assembly and will be a strong foundation for further work in this area. ImportanceThe microbiome surrounding the roots of plants can play an integral role in plant health and growth, and it is composed of thousands of species of microbes that are specific to the plant and environment it is grown in. However, due to this complexity, little is known about the means by which microbiomes form. In this study, we developed the MAize Rhizosphere Synthetic community (MARSc) to gain a better understanding of the formation and function of microbiomes on the roots of plants. This consortium consists of 30 bacterial isolates plus Pseudomonas putida KT2440, a model maize root colonist. In this study, we investigated the interactions between these organisms, their genomes, cultural characteristics, and growth on the roots of maize. We show that MARSc increases maize growth under low nitrogen fertilizer, suggesting it will be a useful tool for identifying the mechanisms behind microbiome formation and plant growth promotion.

microbiology↗