In biotechnological applications, it is often necessary to introduce genes or entire pathways into a host cell, which can create a significant metabolic burden on the host, limiting productivity. In this study, we systematically investigated the physiological stress responses of Pseudomonas putida during heterologous protein production using a modular monitoring system consisting of a plasmid encoding a heterologous protein fused to eGFP and a chromosomally integrated capacity reporter. Our findings reveal that translation is the main bottleneck, with translational capacity becoming saturated under high expression loads. While increasing the strength of the RBS improved protein production for non-burdensome proteins, this effect was not observed for larger fusion proteins. Variations in fusion protein size suggested that it is not the overall mass of the produced protein, but rather the length of the mRNA transcript, that contributes to metabolic burden. We further evaluated how resource availability affects protein expression by modifying the metabolic regime or supplementing with amino acids. While the carbon source affected cellular capacity, it did not significantly alter heterologous protein production. Amino acid supplementation alleviated the growth defects of MBPeGFP-producing cells and modestly improved protein production rates. Together, these findings emphasize that metabolic burden is influenced not only by the size of the produced protein but also by transcript architecture, resource allocation, and the physiological state of the host. Therefore, successful optimization of heterologous protein production requires a holistic approach integrating construct design with host physiology and cultivation strategies.

Natural products remain a primary source of new chemical entities and a central medium of biological communication. Among them, sulfur-containing natural products from microbes stand out for their diverse motifs─such as thioether (S–Cα/β/γ), thiazole, and sulfonate groups─distributed across RiPPs, NRPs, PKs, lipids, terpenoids, and hybrids. These sulfur-containing metabolites functionally contribute to communal interactions─intra- and interspecies microbe–microbe and microbe–host interactions─through their antibacterial, antifungal, antiviral, anticancer, and immunomodulatory activities. Crucially, they may act as information-rich signals that tune quorum circuits, biofilms, membrane and ion homeostasis, and host pathways. This review provides a structure- and mechanism-guided overview of sulfur-containing natural products reported over the past decade (2014–2025), covering their microbial sources, chemical diversity, as well as mechanisms of action and emphasizing how sulfur functionality encodes interaction strategies from host microbiomes to environmental systems.

Mitochondria, which evolved from symbiotic bacteria, possess their own genomes (mtDNA) and support independent transcription and translation within the organelle. Given the essential role of mtDNA in energy production, metabolism, as well as cellular homeostasis, and the high density of confirmed pathogenic mutations that map to mtDNA, there is a pressing need for versatile methods to study and manipulate this genome. Although CRISPR technology has revolutionized the editing of nuclear genomes, it has not been successfully extended to mtDNA, primarily due to the challenge of delivering single guide RNAs (sgRNAs) across both outer and inner mitochondrial membranes. Here we develop a survival-based reporter in Saccharomyces cerevisiae to screen for potential RNA import motifs. We identify a 40-nucleotide aptamer (IM83) that facilitates sgRNA entry into the mitochondrial matrix, enabling CRISPR editing by a mitochondrially-localized adenine base editor. We show that mitochondrial import of IM83 is ATP-dependent and enhanced by the tRNA synthetase Msk1. Further investigations identify additional barriers to efficient CRISPR editing of mtDNA, including loss of membrane potential associated with mitochondrial targeting of the base editor. These insights lay the groundwork for future improvements in CRISPR-based editing of mtDNA in eukaryotes.

As biology grows increasingly data-driven, so too does the field of phage lysins, enzymes that degrade bacterial cell walls and hold promise as alternatives to traditional antibiotics. Five years ago, we introduced PhaLP, a centralized resource for Phage Lytic Protein sequences and associated metadata to support global research efforts. Here, we present PhaLP 2.0, a significantly enhanced database designed to overcome key challenges in the computational study of lysins by integrating the newly identified lysins obtained from thousands of metagenomes. To expand the known diversity of lysins beyond those from cultured phages, we developed SUBLYME, a protein embedding-based machine learning Software designed to Uncover and classify Bacteriophage Lysins in Metagenomic datasets. Using embeddings derived from the prior well-curated protein sequences of the original PhaLP database, we trained support vector machines to distinguish lysins from non-lysins in viromes and classify them as either endolysins or virion-associated lysins. The models achieved an average F1-score of 98% on held-out lysin clusters. SUBLYME enabled the discovery of 743,000 new lysin sequences from EnVhogDB, a virome-derived protein database, increasing the number of known lysin clusters by a factor of 40, from 1,000 to 40,000. PhaLP 2.0 entries were annotated by integrating Pfam functional predictions to the refined delineations obtained with SPAED, an algorithm that leverages the predicted aligned error matrix from AlphaFold predictions to identify domain boundaries. Both SUBLYME and the PhaLP 2.0 database are accessible online at https://github.com/Rousseau-Team/sublyme and http://phalp.ugent.be, respectively. Together, these advances establish PhaLP 2.0 as a comprehensive and scalable portal for the discovery, classification, and sequence analysis of phage lysins, paving the way for future antibacterial applications and evolutionary insights.

The application of microbial consortia in biotechnological areas has proven to be much more efficient than that of single microorganisms; however, the main difficulty lies in the large number of communities to be tested. The use of models to predict functional efficiency on a high-throughput scale is key to incorporating greater diversity. The BSocial tool (http://m4m.ugr.es/BSocial.html) assigns a social behavior to each strain based on its contribution to the overall growth of the consortium through a statistical analysis, defining a ‘social consortium’. To determine the effectiveness of the BSocial tool for designing a biofertilizer, the social behavior of 8 plant growth-promoting microorganisms belonging to Azospirillum, Bacillus, Bradyrhizobium, Ensifer and Pseudomonas, as well as 3 plant growth-promoting traits (siderophore production, phosphate solubilisation and indole acetic acid production) of the complete combinatorial (255 communities) were analysed. We selected 3 social consortia (X22, X93 and X149) with a diversity of 2–4 species, two of which presented high performance for more than one plant growth-promoting trait evaluated. Functional stability, following the increase in diversity, was observed in all functions except for siderophore production. Overall, the results show the effectiveness of the BSocial tool in selecting plant growth-promoting consortia to formulate efficient biofertilizers.

Global concern over food waste and plastic pollution highlights the urgent need for sustainable, high-performance materials that can replace petroleum-based plastics. Bacterial cellulose (BC), a biopolymer synthesized through microbial fermentation by Komagataeibacter and related genera, shows exceptional purity, mechanical strength, biodegradability, and structural tunability. Following PRISMA principles, this review analyzed studies from PubMed, Scopus, and Web of Science covering the period 1960–November 2025. Search terms included “bacterial cellulose”, “Komagataeibacter”, “Gluconacetobacter”, “static culture”, “agitated culture”, “in situ modification”, “ex situ modification”, “fermentation”, and “food packaging”. Inclusion and exclusion criteria ensured that only relevant and high-quality publications were considered. The review summarizes major developments in BC biosynthesis, structural organization, and modification approaches that enhance mechanical, barrier, antioxidant, and antimicrobial properties for food packaging. Recent advances in in situ and ex situ functionalization are discussed together with progress achieved through synthetic biology, green chemistry, and material engineering. Evidence shows that BC-based composites can reduce oxygen and moisture permeability, strengthen films, and prolong food shelf life while maintaining biodegradability. Remaining challenges such as high cost, lengthy fermentation, and regulatory uncertainty require coordinated strategies focused on metabolic optimization, circular bioeconomy integration, and standardized safety frameworks to unlock BC’s full industrial potential.

The fungus Trichoderma reesei is widely employed for industrial enzyme production. However, the mechanisms coordinating enzyme production with amino acid biosynthesis and metabolism remain incompletely understood. Here, we characterized a novel C2H2 zinc finger transcription factor, TrTRC-1, in T. reesei. The deletion of TrTRC-1 enhanced cellulase, xylanase, and extracellular protein production by 9.8–32.4% compared to that in T. reesei Rut C30, while simultaneously stimulating mycelial growth and conidiation. Transcriptomic analysis revealed that the deletion of TrTRC-1 significantly altered the expression of genes associated with primary metabolic pathways, amino acid biosynthesis/metabolism, and carbohydrate-active enzymes. In a 5 L bioreactor, T. reesei ΔTrTRC-1 achieved an FPase activity of 17.35 IU/mL and exhibited robust saccharification efficiency on lignocellulosic biomass. These findings demonstrate that TrTRC-1 serves as a multifunctional regulator in T. reesei, advancing our understanding of the regulatory network that balances enzyme production with cellular metabolism and providing a strategic basis for constructing microbial chassis strains with industrial application prospects.

Host production of nitric oxide in response to P. aeruginosa results in accumulation of nitrite and nitrate at the infection site, with both utilized for anaerobic respiration to support survival. Nitric oxide and nitrite also act as aerobic respiratory inhibitors. P. aeruginosa must overcome these toxic metabolites alongside self-produced cyanide to persist at the infection site. We previously identified a novel nitrite reductase (NirA) that supports P. aeruginosa virulence in a wide range of infection models. In this work, we demonstrate that mutation of nirA inhibits growth of P. aeruginosa at reduced oxygen tensions in the presence of nitrite or nitrate, with this phenotype shown to be dependent on cyanide. NirA is a siroheme-dependent enzyme, a classical target for inhibition with cyanide. Biochemical characterization confirms that NirA is a novel cyanide-tolerant nitrite reductase, which supports reduction of nitrite in the presence of cyanide. We hypothesise that NirA enables detoxification of nitrite to prevent build-up of multiple respiratory inhibitors and facilitate cyanide-resistant aerobic respiration at low oxygen tensions. Through targeting effectors of these resistance mechanisms, we could promote P. aeruginosa self-poisoning and prevent adaptation to the reduced oxygen environment typically encountered by P. aeruginosa in biofilms and during infection.

Bacterial growth and respiration are fundamental metabolic processes that affect how energy is used and impact carbon sequestration at the ecosystem scale. However, these traits are usually quantified independently, growth is quantified with endpoint biomass measurements while respiration is quantified by monitoring oxygen or carbon dioxide. Because the two physiological traits are collected at different temporal and volumetric scales (hours-to-days for growth versus minutes-to-hours for respiration), reconciling them is challenging and often introduces scale-mismatch bias, obscuring causal links between metabolism and environmental drivers. In this study, we develop a novel method for quantifying the rates of bacterial growth and respiration concurrently from a single dissolved oxygen time series. Our approach introduces a model that couples exponential biomass growth with biomass-specific respiration, enabling the simultaneous inference of both rates in real time. We applied our high throughput method to 15 bacterial strains isolated from natural environments. Our approach yielded growth estimates in close agreement with measurements based on popular methods using optical density or flow cytometry with no evidence of taxon-specific bias. We also tested our approach in quantifying the effects of temperature on respiration, growth and carbon use-efficiency in Pseudomonas sp. Our method yielded typical unimodal thermal response curves for growth and respiration where rates were highest at moderate temperatures, while carbon-use efficiency increased from cooler temperatures, peaked around the thermal optimum, and declined at high temperature. By quantifying respiration and growth simultaneously and in high throughput, our approach effectively enables measurement of microbial metabolic strategies and adaptations to stress. It offers a non-invasive and scalable tool for high throughput phenotyping and studies of environmental perturbations, enabling a new class of trait-based microbial ecology that links cellular physiology to broader ecosystem function.