Microbial communities play a key role in biogeochemical transformations in a wide range of ecosystems, but they also hold significant potential to enhance the bioproduction of desired chemicals. Although designing synthetic microbial consortia has generated a lot of interest, a more in-depth understanding of the interactions between strains is required, particularly when strains are engineered to cross-feed, but are not isolated from related environments. Challenges include enhancing stability, productivity and controllability. Here, we used a synthetic microbial co-culture consisting of engineered strains of the photosynthetic cyanobacterium Synechococcus elongatus PCC 7942 cscB/SPS and nitrogen-fixing bacterium Azotobacter vinelandii AV3. Each relies on the other for conversion of atmospheric carbon (CO2) and nitrogen (N2) into organic forms, i.e. sucrose and ammonia, respectively, resources which can be shared. As both strains have such contrasting growth dynamics in co-culture compared to monoculture, we applied a label-free quantitative proteomics approach to characterise metabolism in both strains. The proteomes of both shifted when in co-culture to reflect adaptive restructuring of carbon and nitrogen metabolism, although A. vinelandii appeared to transition to a more stressed state, inducing proteins linked to polymer biosynthesis. An analysis of the co-culture over 16 days led to phenotypic changes, including cell structure alterations in A. vinelandii AV3 over time, with the proteome suggesting cell envelope remodelling and potentially encystment. These findings suggest that physiological control of parameters, such as oxygen and nutrient availability, may enable cultivation of more stable co-cultures.
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Amide bond formation is widely used in pharmaceutical synthesis, typically involving stoichiometric coupling reagents to activate carboxylic acid substrates for a condensation reaction. As an alternative approach, we repurposed aldehyde dehydrogenases into oxidative amidases by creating a more hydrophobic and spacious catalytic pocket for amines to capture the thioester intermediate. This biocatalyst efficiently facilitates the formation of amide bonds between diverse aldehydes and amines. We also developed a two-step enzymatic cascade to synthesize amides from broadly available aliphatic alcohols. This biocatalytic strategy enabled the redesign of synthetic routes for five drug molecules. Our findings highlight the potential of oxidative amidases in advancing the synthesis of structurally diverse drug molecules through efficient amide bond formation.
mhryu@live.com's insight:
amide (R-C=O), ALDHs oxidize aldehydes to carboxylic acids through a thioester intermediate. We proposed that, by protein engineering, we intercept this reactive intermediate with amines before hydrolysis, thereby yielding amides directly. amino acid residues in the binding pocket were substituted to create a more spacious, hydrophobic environment 
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Engineering bacteria to address human health challenges has been an active and productive area of research for many decades. Historically, a major emphasis of this work has been on modifying laboratory-adapted species to generate chemical or biological compounds for therapeutic use or for further study. In recent years, however, there has been a growing interest in utilizing nonmodel commensal and probiotic bacterial strains for development as in situ engineered living therapeutic or diagnostic machines. While substantial insight can be gained from previous work in well-studied organisms such as E. coli, effective genetic and metabolic manipulation of novel species often requires novel tools. Here, we highlight strategies for the development of synthetic biology toolboxes for nonmodel bacterial strains to assist researchers across disciplines in establishing the molecular biology framework required to work with relatively understudied species. We focus on advances in engineering the Bacteroides genus as an example of how to establish such a pipeline.
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Protein biopharmaceuticals play a key role in providing effective, targeted, and personalized therapies for diverse diseases, while also preventing and mitigating a broad range of infections. N-glycosylation is a key post-translational modification influencing the biological activity of many protein-based therapeutics, yet structure–function relationships of N-glycans remain poorly understood due to challenges in producing homogeneous glycoforms. Current go-to production hosts, mammalian and yeast cells, often yield heterogeneous glycan profiles and require extensive genetic manipulation. Alternative production hosts such as the plant Nicotiana benthamiana, provide more homogeneous glycosylation and flexibility through transient expression, but are limited in the generation of certain glycoforms. In vitro glycoengineering can overcome these limitations but is time consuming and requires expensive resources. In this study, we show that by combining in planta and in vitro glycoengineering strategies, we can quickly produce a wide range of homogeneous glycoforms of pharmaceutical proteins with high mannose, paucimannose, hybrid and complex N-glycan structures. Using N. benthamiana as a transient expression host, we produced two pharmaceutical glycoproteins — the monoclonal antibody rituximab and the helminth vaccine candidate OoASP-1 — and modified them in vitro using E. coli produced glycoenzymes. The combination of these two glycoengineering systems minimizes the amount of time and resources required, while maintaining high glycan homogeneity. This scalable, flexible, and cost-effective platform opens the door to glycan structure–function relationship studies and can support rational design of next-generation biopharmaceuticals.
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glycosylation, m-2st
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Cultivated meat is a promising solution to global food security challenges, but cultivated ingredients offer an equally compelling and potentially more economically stable path forward. Components such as flavour enhancers, cultivated fat, and proteins present scalable opportunities for improving alternative proteins, highlighting the untapped potential of cultivated ingredient-focused strategies.
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Researchers have published the recipe for an artificial-intelligence model that reviews the scientific literature better than some major large language models (LLMs) are able to, and gets the citations correct as often as human experts do. OpenScholar — which combines a language modelwith a database of 45 million open-access articles — links the information it sources directly back to the literature, to stop the system from making up or ‘hallucinating’ citations. Several commercial AI-based literature-review tools already exist that use similar techniques, but few have been released as open source, says Akari Asai, an AI researcher at Carnegie Mellon University in Pittsburgh, Pennsylvania, and a co-author of the work, published in Nature on 4 February1. Being open source means that researchers can not only try OpenScholar for free in an online demonstration, but also deploy it on their own machine and use the method in the paper to boost the literature-review skills of any LLM, says Asai. In the 14 months since OpenScholar was first published in the arXiv repository2, AI firms such as OpenAI have used similar methods to tack ‘deep research’ tools onto their commercial LLMs, which has greatly improved their accuracy. OpenScholar also outperformed GPT-4o, as well as rival tools such as PaperQA2
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review generation with llm. Synthesizing scientific literature with retrieval-augmented language models https://www.nature.com/articles/s41586-025-10072-4
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Organofluorine compounds are of considerable concern due to their environmental persistence and human health effects. Their persistence stems from an inability of native microorganisms to metabolize them. Polyfluorinated compounds are unnatural, offer little nutritional benefit to microorganisms, and their breakdown releases toxic fluoride anion. Yet, the biological breakdown of dispersed polyfluorinated compounds would offer a compelling strategy for environmental remediation. In this context, molecular biotechnology is being pursued. The first required tools are methods for monitoring biodefluorination, often determining fluoride release by different methods. The use of those methods revealed fluoride toxicity as a major selection against biodefluorination. Despite that, microbial enzymes catalyzing defluorination of monofluorinated compounds are known and provide templates for bioengineering systems to handle polyfluorinated compounds. Whereas biodegradation often evolves in nature, effective biodegradation of perfluorinated chemicals may require laboratory evolution and engineering.
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Efficient co-utilization of hexose and pentose sugars from lignocellulose is essential for microbial production of bio-based chemicals, yet engineered non-native catabolic pathways can be suboptimal and evolutionarily unstable in complex resource environments. We used a Pseudomonas putida strain, previously engineered to catabolize xylose and arabinose to examine how resource abundance, temporal availability, and sub-culturing criteria shape evolutionary outcomes. Using an automated adaptive laboratory evolution (ALE) platform, we evolved the strain under static conditions with single selection pressures and dynamic regimes that imposed selection pressures on multiple sugars. These environments drove divergence between catabolic specialists and generalists. While selection regimes with weak or absent selection for xylose frequently resulted in loss of xylose catabolism, evolution under carbon-limited, mixed-sugar environments promoted stable retention and coordinated optimization of multiple catabolic pathways, increasing total sugar consumption in mixed-sugar conditions. Genomic, proteomic, and biochemical analyses showed that evolutionary stability was determined by pathway-specific fitness costs, leading to either pathway loss or cost-reducing refinement, depending on selection strength. An isolated generalist clone also exhibited improved indigoidine production from mixed sugars when compared to the parental strain. Together, these findings link resource dynamics to fitness landscapes that determine catabolic specialization, generalization, evolutionary trade-offs, and applicability to bioconversion.
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feist am, 3st, evolutionary stability
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An unresolved challenge in the field of computational protein design is to create proteins that bind non-protein partners, e.g. DNA, RNA, and small molecules. Most machine learning (ML) algorithms for protein design can only work with systems composed entirely of amino acids, and therefore cannot be directly applied to this task. The few algorithms that accommodate non-proteins still represent amino acids differently than other molecules, and therefore cannot easily recognize the similarity between a sidechain and a small molecule that share a functional group. We introduce a new method, called AtomPaint, that avoids these limitations by employing a fully-atomic representation of protein structure. Starting from a model of a desired binding interaction, our method proceeds by (i) converting that model to a 3D image, (ii) masking out the parts of that image that need to be redesigned, (iii) using a diffusion model to inpaint the masked voxels, then (iv) using a classification model to identify the amino acids in the inpainted image. Both models are SE(3)-equivariant ResNets, and were trained on a dataset of structures from the Protein Data Bank (PDB) curated to emphasize protein/non-protein interactions. In a sequence recovery benchmark, AtomPaint performed better than random guessing, suggesting that it understands some aspects of molecular structure. We discuss possible avenues of improvement, in the hopes that the advantages of our novel image-based approach can be fully realized.
mhryu@live.com's insight:
church gm, A) Input model creation: This could be done in different ways. Here, we illustrate the creation of a binding site for a ligand (purple) in an existing scaffold protein (green). Note that this model contains the native backbone, but not the native sidechains. B) Voxelization: The input model is converted into a 3D image. C) Inpainting: A diffusion model fills in any atoms that are missing from the input model. D) Amino acid classification: A small region around each unknown amino acid is cropped, and a classification model is used to identify the amino acid in each crop. In this example, threonine (T) is shown to be the most probable amino acid in the highlighted position. This step is repeated for each unknown amino acid in the image, and the result is a new protein sequence.
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The human gastrointestinal tract is home to a diverse community of microorganisms from all domains of life, collectively referred to as the gut microbiome. While gut bacteria have been studied extensively in relation to human host health and physiology, other constituents remain underexplored. This includes the gut virome, the collection of bacteriophages, eukaryotic viruses, and other mobile genetic elements present in the intestine. Like gut bacteria, the gut virome has been causatively linked to human health and disease. However, the gut virome is substantially more difficult to characterize, given its high diversity and complexity, as well as multiple challenges related to in vitro cultivation and in silico detection and annotation. In this mini-review, we describe various methodologies for examining the gut virome using both culture-dependent and culture-independent tools. We highlight in vitro and in vivo approaches to cultivate viruses and characterize viral-bacterial host dynamics, as well as high-throughput screens to interrogate these relationships. We also outline a general workflow for identifying and characterizing uncultivated viral genomes from fecal metagenomes, along with several key considerations throughout the process. More broadly, we aim to highlight the opportunities to synergize and streamline wet- and dry-lab techniques to robustly and comprehensively interrogate the human gut virome.
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software list
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Methanotrophs are of fundamental importance in the global methane cycle and hold promise for the bioconversion of methane into valuable products. This study elucidates the impact of alternating aerobic–anoxic conditions on metabolism of the two phylogenetically distinct groups of methanotrophs: Methylomonas koyamae (type I, γ-proteobacteria) and Methylocystis bryophila (type II, α-proteobacteria). Using batch cultivations with single and repeated oxygen pulses, we demonstrated that cyclic aerobic–anoxic alternation significantly increased the secretion of organic compounds. Notably, acetate secretion by the type II methanotroph M. bryophila was observed for the first time. Genome-scale metabolic modeling and flux balance analysis revealed that acetate secretion serves as an auxiliary ATP-generating pathway for M. bryophila under oxygen limitation. We further uncovered a dynamic mechanism: aerobic phases trigger methane uptake and build up intracellular organic carbon pools, while anoxic phases redirect these pools toward acetate and/or hydrogen release to sustain survival under oxygen limitation. This cyclic regimen effectively channels more methane-derived carbon into extracellular acetate. Our findings underscore that redox conditions critically shape methanotrophic metabolism, influencing both the biological methane cycle and the global climate.
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The research on synthetic methylotrophic bacteria for one-carbon (C1) feedstock assimilation has garnered substantial interest and is regarded as the forefront of biomanufacturing advancements. Nevertheless, the effective utilization of C1 feedstocks faces challenges due to inadequate tolerance toward C1 compounds. This study elucidates that the buildup of formaldehyde causes severe DNA–protein cross-linking (DPC), and thus hampers growth and methanol assimilation in E. coli. To tackle this issue, we exploited a metalloproteinase, SpWss1, from Schizosaccharomyces pombe. By fine-overexpressing SpWss1 in the E. coli genome, we were able to alleviate DPC damage and enhance formaldehyde tolerance. Remarkably, the engineered strain displayed a 10-fold increase in the amount of methanol assimilated (142 mM) compared to that of the control strain lacking SpWss1 (14 mM). Moreover, through iterative substrate feeding of methanol and xylose in shake-flask experiments, the genetically modified strain exhibited improved consumption levels, reaching up to 309 mM (∼10 g/L), making it one of the highest methanol-consuming strains among all E. coli strains without adaptive evolution. Additionally, the modified strain significantly enhanced the sustainable production of valuable products, such as triacetic acid lactone and fatty acids, from methanol. Overall, our findings underscore the significant scientific and biotechnological importance of addressing DPC to optimize C1 assimilation, providing valuable insights for sustainable chemistry, engineering, and industrial biotechnology applications.
mhryu@live.com's insight:
metalloprotease Wss1 functions within a comprehensive network of DNA repair mechanisms crucial for preserving genome stability and integrity. Wss1 specifically targets and cleaves the protein component of DPCs, enabling their removal and subsequent DNA repair.
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Mushroom-forming basidiomycetes are increasingly recognized for their significant potential to remediate polluted environments and mitigate climate change. This review synthesizes evidence positioning mushroom-forming basidiomycetes at the nexus of ecological resilience and a sustainable bioeconomy, highlighting their dual roles in environmental repair and green innovation. Ectomycorrhizal (ECM species) enhance carbon acquisition by plants and long-term soil carbon sequestration; ECM-dominant forests stockpile upto 70% more below-ground carbon than their non-mycorrhizal counterparts. Saprotrophic fungi drive lignocellulose degradation, nutrient cycling, and the stabilization of soil organic matter. Basidiomycetes also play a crucial role in mycoremediation by degrading recalcitrant contaminants (pesticides, hydrocarbons) and immobilizing heavy metals. Furthermore, mycelium-based biomaterials are being developed as green-technology alternatives to plastics and synthetic foams, reflecting the growing commercialization of fungal biotechnology, as evidenced by the global mycelium material industry projected to exceed USD 5 billion by 2032. The intersection of ecological function and economic value positions mushrooms at the forefront of the circular bioeconomy. However, challenges remain, including production scalability, environmental sensitivity, and economic viability. Addressing these challenges through interdisciplinary research could unlock the full potential of fungi as nature-based climate solutions.
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Komagataella pastoris is extensively used as a microbial cell factory for the production of recombinant proteins and high-value compounds. However, tightly controlled promoter systems responsive to safe and economical inducers are required for precise metabolic and pathway engineering in this yeast species. Cumate-inducible promoters are an ideal choice due to the safety and low cost of cumate. In this study, we systematically optimised the insertion sites of the CuO operator sequence within the strong promoter PGCW14 to isolate a high-activity variant that we designated as PGCWCuO03. To fine-tune the expression of the repressor protein CymR, we developed a truncated promoter of PGAP, designated as PGAP200. Based on the optimal promoter PGCWCuO03 and the CymR expression unit, we constructed a robust CymR/CuO-mediated cumate-inducible promoter, designated as Pgc, in K. pastoris. Pgc demonstrated outstanding induction properties, resulting in an approximately 11-fold increase in target protein production following induction. Promoter substitution assays validated the effectiveness of Pgc in temporal gene expression control, highlighting the significant potential of this promoter for both basic research and industrial bioprocessing applications in synthetic biology and biotechnology in K. pastoris.
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m-1str
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Enzymes are highly efficient biocatalysts known for their chemical, regio-, and stereoselectivity, making them valuable in industrial applications. While directed evolution has expanded the scope of enzyme-catalyzed reactions, the range of enzymatic reactions remains limited compared to reactions catalyzed by chemical catalysts. Computational enzyme design has achieved de novo enzyme design, but the approach is often complex, time-intensive, and has a low success rate. A promising strategy to design novel enzymes involves developing noncanonical amino acids (ncAAs) with catalytic potential and integrating them into protein scaffolds via genetic codon expansion technology. This method combines the novel reactivity of ncAAs with the high selectivity provided by protein scaffolds, significantly enhancing the diversity of enzyme-catalyzed reactions. This review discusses recent advancements in novel enzyme design using ncAAs, including those being used as catalytic groups, metal-coordinating groups, heme ligands, and photocatalytic groups. The article emphasizes the broad potential of using ncAAs in enzyme design to expand the diversity of enzyme-catalyzed reactions, and outlooks the potential applications of artificial intelligence technology in this area.
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Microbial communities play a key role in biogeochemical transformations in a wide range of ecosystems, but they also hold significant potential to enhance the bioproduction of desired chemicals. Although designing synthetic microbial consortia has generated a lot of interest, a more in-depth understanding of the interactions between strains is required, particularly when strains are engineered to cross-feed, but are not isolated from related environments. Challenges include enhancing stability, productivity and controllability. Here, we used a synthetic microbial co-culture consisting of engineered strains of the photosynthetic cyanobacterium Synechococcus elongatus PCC 7942 cscB/SPS and nitrogen-fixing bacterium Azotobacter vinelandii AV3. Each relies on the other for conversion of atmospheric carbon (CO2) and nitrogen (N2) into organic forms, i.e. sucrose and ammonia, respectively, resources which can be shared. As both strains have such contrasting growth dynamics in co-culture compared to monoculture, we applied a label-free quantitative proteomics approach to characterise metabolism in both strains. The proteomes of both shifted when in co-culture to reflect adaptive restructuring of carbon and nitrogen metabolism, although A. vinelandii appeared to transition to a more stressed state, inducing proteins linked to polymer biosynthesis. An analysis of the co-culture over 16 days led to phenotypic changes, including cell structure alterations in A. vinelandii AV3 over time, with the proteome suggesting cell envelope remodelling and potentially encystment. These findings suggest that physiological control of parameters, such as oxygen and nutrient availability, may enable cultivation of more stable co-cultures.
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In this study, we constructed two engineered E. coli strains that utilized glucose (E. coli GL10) and xylose (E. coli XL12) simultaneously via division of labor. Both two engineered strains blocked the synthetic pathway of several byproducts, including succinate, formate, ethanol, and acetate. XL12 demonstrated the alleviation of carbon catabolite repression effect and the improvement of xylose utilization in mixed sugar. GL10 and XL12 produced D-lactate from glucose and xylose with an optical purity of 100%, respectively. Microbial consortium using GL10 and XL12 with the inoculation ratio of 1:1 achieved complete glucose depletion and 53.21% xylose utilization simultaneously. Notably, when the fermentation was proceeded using mixed sugar for 36 h, the D-lactate titer of the consortium achieved 3.76 g/L, which is 5.96-fold higher than that of the wild-type (WT) fermentation. The effect of shaking speed and inoculum ratio on the fermentation performance were investigated. Specifically, the xylose utilization in the mixed sugars was improved with an increase of shaking speeds from 50 to 250 rpm, while the yield of D-lactate did not further increase at 250 rpm. When the inoculation ratio of GL10 and XL12 was 1:5, the consortium showed better performance in D-lactate production, which was 6.55-fold higher than that of the WT. The consortium was also utilized mixed sugars effectively in corn straw hydrolysate and produced D-lactate of 5.15 g/L, which was 41.87% higher than that of WT. Our study offers a feasible approach via metabolic engineering and artificial consortium construction for bio-based D-lactate production.
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Genomic prediction has become central to human, animal and plant biology, enabling quantitative inference of how genetic variation shapes complex traits. Although these domains share statistical foundations, such as linear mixed models, Bayesian regression and deep-learning frameworks, they have advanced largely in parallel. Here we synthesize their methodological evolution and highlight opportunities for integration and deeper collaborations. Agricultural genetics contributed to the mixed-model and Bayesian frameworks underlying modern polygenic scores, while human genomics has driven advances in nonlinear modeling, federated learning and biology-informed artificial intelligence. We propose a roadmap centered on interoperable data standards, shared benchmarks and cross-disciplinary training to unify predictive genomics across species. Together, these efforts establish genomic prediction as a comparative science capable of explaining how genetic information drives form and function across the diversity of life. We emphasize that shared biological architectures and knowledge transfer across species can directly improve the robustness, interpretability and generalizability of predictive models. This Review compares predictive genomics across humans, animals and plants, and outlines shared statistical foundations and key differences in phenotype structure, as well as opportunities for biologically grounded, generalizable artificial intelligence models.
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Biofilms have emerged as promising biocatalysts due to their distinct structural and functional advantages. Since biofilm dynamics shape biofilm architecture and catalytic performance, engineering strategies to control these dynamics are key to improving biofilm-based catalysis. In this review, we outline the fundamental features and catalytic benefits of biofilms, with a focus on biofilm dynamics. We highlight recent advances in regulatory strategies, from the manipulation of biofilm-associated genes to the design of synthetic circuits based on signaling networks that govern biofilm development. We further discuss current challenges, including limited regulatory efficiency, restricted applicability beyond model organisms, and the need for biofilm functional enhancement. Collectively, these insights position the control of biofilm dynamics as a frontier for advancing next-generation biofilm-based biocatalysis.
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Emerging aromatic pollutants originating from pesticides, including insecticides, herbicides, pesticide additives, surfactants, and mulching films, are increasingly contaminating agro-ecosystems. Their widespread use, recalcitrance, and toxicity pose serious risks to the environment as well as human health. Bacteria possess remarkable potential to degrade these persistent, bioaccumulative, and toxic aromatic pollutants. This review focuses on metabolic fate and eco-physiological traits that bacteria employ for survival and effective degradation of xenobiotics that commonly persist in agricultural fields. It also highlights opportunities and challenges of a holistic approach using bacteria, that is, integrating pollutant biodegradation, plant growth promotion, and biocontrol activities to restore soil health and crop productivity.
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Microbial natural products represent a chemically diverse repertoire of small molecules with major pharmaceutical potential. Despite the increasing availability of microbial genome sequences, large-scale natural product discovery remains challenging because the existing genome mining approaches lack integrated workflows for rapid dereplication of known compounds and prioritization of novel candidates, forcing researchers to rely on multiple tools that requires extensive manual curation and expert intervention at each step. To address these limitations, we introduce LoReMINE (Long Read-based Microbial genome mining pipeline), a fully automated end-to-end pipeline that generates high-quality assemblies, performs taxonomic classification, predicts biosynthetic gene clusters (BGCs) responsible for biosynthesis of natural products, and clusters them into gene cluster families (GCFs) directly from long-read sequencing data. By integrating state-of-the-art tools into a seamless pipeline, LoReMINE enables scalable, reproducible, and comprehensive genome mining across diverse microbial taxa. The pipeline is openly available at https://github.com/kalininalab/LoReMINE and can be installed via Conda (https://anaconda.org/kalininalab/loremine), facilitating broad adoption by the natural product research community.
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genome assembly software
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Canonical nucleic acids (DNA and RNA) naturally store genetic information with high density and programmability, making them promising candidates for molecular data storage. However, their susceptibility to degradation under harsh conditions, such as extreme pH, nuclease activity, and chemical attack, limits practical applications. In contrast, non-canonical nucleic acids (ncNAs) with natural or synthetic structural modifications exhibit enhanced stability and unique functional potential. This review systematically summarizes the fundamental properties of ncNAs, evaluates their suitability for molecular data storage, and discusses how their distinctive advantages may overcome the intrinsic limitations of canonical nucleic acids while addressing challenges in next-generation storage systems. Canonical nucleic acids, with high storage density, are promising media for data storage but are limited by instability. Here, authors summarise the unique properties and advantages of non-canonical nucleic acids in data storage and highlight key challenges for their practical application.
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Nodule-forming bacteria play crucial roles in plant health and nutrition by providing fixed nitrogen to leguminous plants. Despite the importance of this relationship, how nodule-forming bacteria are affected by plant exudates and soil minerals is not fully characterized. Here, the effects of plant-derived methanol and lanthanide metals on the growth of nitrogen-fixing Rhizobiales are examined. Prior work has demonstrated that select Bradyrhizobium strains can assimilate methanol only in the presence of lanthanide metals; however, the pathway enabling assimilation remains unknown. In this study, we characterize Bradyrhizobium diazoefficiens USDA 110, Bradyrhizobium sp. USDA 3456, and Sinorhizobium meliloti 2011 to determine the pathways involved in methanol metabolism in previously characterized strains, other clades of Bradyrhizobium, and the more distantly related Sinorhizobium. Based on genomic analyses, we hypothesized that methanol assimilation in these organisms occurs via the lanthanide-dependent methanol dehydrogenase XoxF, followed by oxidation of formaldehyde via the glutathione-linked oxidation pathway, subsequent oxidation of formate via formate dehydrogenases, and finally assimilation of CO2 via the Calvin-Benson-Bassham (CBB) cycle. Transcriptomics revealed upregulation of the aforementioned pathways in USDA 3456 during growth with methanol. Enzymatic assays demonstrated increased activity of the glutathione-linked oxidation pathway and formate dehydrogenases in all strains during growth with methanol compared to succinate. 13C-labeling studies confirmed the presence of CBB intermediates and label incorporation during growth with methanol. Our findings provide multiple lines of evidence supporting the proposed XoxF-CBB pathway and, combined with genomic analyses, suggest that this metabolism is widespread among Bradyrhizobium and Sinorhizobium species.
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Innovative Biotechnological Strategies for Sustainable Myo-Inositol Production and Application | acs
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Myo-inositol, a high-value cyclic polyol, is increasingly sought by pharmaceutical, food, feed, and cosmetic industries. This review systematically surveys the latest biotechnological advances poised to replace traditional, high-pollution methods. First, multienzyme cascades that convert renewable carbohydrates─starch, glucose, xylose, cellulose, sucrose─are reviewed, highlighting immobilized reactors, porous microspheres, biomimetic mineralized capsules, and biofilm systems that boost stability, reusability, and space-time yields. Second, microbial cell-factory strategies are examined, covering chassis benchmarking (E. coli, Pichia pastoris, Kluyveromyces marxianus, cyanobacteria), carbon-flux redirection, glucose-glycerol synergistic feeding, and dynamic regulatory circuits. A unified analysis pinpoints recurrent bottlenecks─cofactor imbalance, enzyme thermostability gaps, narrow substrate spectra, product inhibition, and downstream complexity─and distills the targeted fixes discussed in the field, from cofactor regeneration circuits to modular process design. By integrating cutting-edge research with industrial techno-economic indicators, this review offers a comprehensive roadmap for sustainable, cost-competitive myo-inositol biomanufacturing and guides future research toward greener production systems.
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Plant-based expression systems, known as molecular farming, have emerged as sustainable and scalable platforms for producing recombinant proteins used in pharmaceuticals, industrial enzymes, and agricultural products. Among the key determinants of transgene performance, promoter elements play a central role in defining transcriptional strength, specificity, and regulation. This review highlights current advances in promoter engineering tailored for plant systems, encompassing natural, synthetic, hybrid, inducible, and tissue-specific promoters used in stable transgenic plants, transient expression systems, and plant cell cultures. The structural and functional features of promoter elements are discussed, along with strategies to mitigate challenges such as transcriptional silencing, genomic context dependency, and variability cross species and production platform. Emerging synthetic biology tools, such as CRISPR-based transcriptional control, high-throughput screening, and machine learning–assisted promoter design, are enabling the creation of tunable, orthogonal promoters suited for complex multigene expression. As promoter engineering continues to evolve, it remains foundational to advancing plant molecular farming and expanding the role of plants as versatile biofactories for high-value recombinant proteins.
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In its natural soil habitat, B. subtilis regularly encounters fluctuating conditions that require adaptive survival strategies, including the production and secretion of antimicrobial compounds. One such compound, surfactin, play a central role in multicellular differentiation processes such as biofilm formation, swarming, and competence development. Competence and surfactin biosynthesis are transcriptionally co-regulated via the quorum sensing-mediated activation of the srfAABCD operon, which contains comS in a distinct ORF overlapping with srfAB. This study aimed at uncoupling competence from surfactin production by introducing targeted stop mutations in comS to selectively disrupt competence without affecting surfactin synthesis. For this, we introduced single nucleotide polymorphisms (SNPs) that preserved the srfAB codons, while simultaneously introducing a premature stop codon in comS. The effects on competence development were assessed using luciferase-based reporter assays monitoring the ComS-dependent expression of comK and comGA expression. Surfactin production was analyzed by mass spectrometry imaging and phenotypic assays examining the impact on multicellular behavior. Our findings demonstrate that the generated point mutations severely reduce competence gene expression, measured via PcomKand PcomGAactivity, to levels comparable with a full comS deletion, while leaving multicellular behaviors such as biofilm and pellicle formation, as well as swarming and sliding motility, unaffected. Thus, ComS is specifically essential for competence development but dispensable for other surfactin-mediated multicellular processes and not involved in structuring biofilms. Taken together, our results demonstrate that it is possible to genetically decouple competence from other developmental pathways in B. subtilis.
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