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.

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.

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.

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.

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.

Crops depend on microbial partners for their growth, development, and overall resilience. A pivotal understanding has emerged showing the direct involvement of the root microbiota in regulating the tiller number of rice, a crucial architecture that influences yield. Novel frontiers in microbiological applications for agriculture highlight the profound role of the root microbiota in shaping crop architecture to boost productivity. We propose that improvements in crop production are moving from a genetic perspective on “architecture” to embracing “holobiont architecture.” As such, microbial orchestration provides a dynamic fine-tune function for breeding “architecture-smart crops” characterised by phenotypic plasticity under environmental uncertainty.

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.

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.