Optogenetic methods are powerful tools for synthetic biology, allowing light to control cellular processes. While most bacterial optogenetic systems regulate gene expression at the transcriptional level, relatively few enable post-translational control, which can provide faster and growth-independent regulation of protein activity. Here, we describe the development of a post-translational optogenetic tool in E. coli using the Mesoplasma florum Lon (mfLon) protease, an AAA+ protease that is orthogonal to native E. coli degradation machinery. To engineer a light-responsive mfLon, we constructed a large library in which a blue-light responsive domain, Avena sativa LOV2, was introduced into nearly every codon position in the protease using an unbiased molecular approach. We screened 726 mfLon-LOV variants using fluorescence-activated cell sorting and multi-round enrichment campaigns. We identified a novel dark-active variant (mfLon-LOV-534) that degrades target proteins in the dark and is inactivated upon blue-light exposure. Characterization of this variant demonstrates that its proteolytic response can be tuned by varying blue-light intensity and transcriptional expression levels. Furthermore, we show that mfLon-LOV-534 can degrade a target protein in both exponential and stationary growth phases, which addresses the limitations of division-based protein dilution. This work establishes a scalable approach to engineering allosteric control in complex multimeric enzymes and provides a foundation for orthogonal, growth-independent control of protein stability in synthetic circuits.

The mutational spectrum is an increasingly important molecular phenotype that quantitatively describes mutagenesis in a given gene and species, enabling future comparative analyses to reveal differences in underlying mutagenic processes, whether internal, such as DNA repair processes, or external, such as ecological niches and conditions. Mutation accumulation experiments, although time-consuming and costly, remain the standard approach for reconstructing bacterial neutral mutation spectra. Here, we present BacNeMu, a phylogenetically informed pipeline that reconstructs neutral mutational spectra of bacterial genomes using open databases GTDB, AnnoTree and KEGG Orthology, building on previously developed NeMu pipeline. BacNeMu reconstructs mutation spectra that closely match mutation accumulation experiments results while requiring substantially less time, enabling comparative analyses across diverse bacterial taxa. Applied to obligate aerobes and anaerobes, BacNeMu recovered the expected excess of T:A>C:G transitions, consistent with oxidative-damage-associated mutational patterns previously described in mitochondrial genomes and yeast single-strand. We further asked if any other ecologic factors influence a mutational spectrum. As a pilot we compared three species living under different temperatures: one strong thermophile - Thermotoga maritima, one psychrophile - Clostridium algidicarnis, and one with intermediate temperature tolerance - Psychrobacter sanguinis. In the thermophile, the relative frequency of T:A>C:G substitutions was higher than in the psychrophile, consistent with the hypothesis that GC-biased mutagenesis contributes to thermal adaptation, although C:G>T:A transitions predominate across all three species. BacNeMu provides a rapid, phylogenetically informed framework for generating biologically meaningful mutation spectra from open databases.

Anaerocellum (formerly Caldicellulosiruptor) bescii, an anaerobic, extremely thermophilic (Topt ∼78 °C) lignocellulolytic bacterium, is a promising chassis for metabolic engineering and next-generation bioprocessing. Yet, a lack of well-characterized genetic parts in A. bescii has hampered metabolic engineering efforts. Here, using a previously developed hyperthermophilic β-galactosidase reporter system, we screened a diverse panel of putative A. bescii promoter sequences, identifying promoters that drove reporter output across a broad range. For a select subset, we mapped their transcriptional start sites (TSSs) and evaluated ribosome binding site (RBS) regions using chimeric promoter constructs. By constructing truncated promoter variants, we defined functional regions within the widely used, high-expression S-layer protein promoter (Pslp) and engineered a compact 99 bp variant that retained substantial reporter activity. Finally, we demonstrated that these new promoters can be used for metabolic engineering by using two newly characterized promoters to express an established thermostable alcohol dehydrogenase from Thermoclostridium stercorarium to drive ethanol production in A. bescii. Together, this work expands and diversifies the A. bescii genetic toolkit, opening doors to future metabolic engineering efforts in this species.

Over the past decade, protein design has evolved from a specialized discipline into a broadly accessible approach for engineering and interrogating biological systems. Despite these advances, protein design continues to be a technically challenging task, often requiring knowledge of programming to be able to use and combine the different software packages. To address this challenge, we have developed Prosculpt, an easy-to-use protein design pipeline. Prosculpt integrates RFdiffusion for backbone generation, ProteinMPNN for sequence design and multiple structure-prediction platforms (AF2, AF3, Colabfold, Boltz2). Candidate designs are evaluated using customizable Rosetta-based scoring protocols. Each project is specified through a single configuration file, enabling users with minimal computational expertise to perform sophisticated protein design tasks without writing code, while also allowing advanced users to access the full capabilities of the underlying programs. Prosculpt supports a wide range of applications, including design of symmetric homo-oligomers, design of binders, motif scaffolding, partial diffusion and fixed-backbone sequence redesign. By combining these capabilities within a single, user-friendly platform, Prosculpt provides a practical entry point to modern protein design for both novice and expert users.

Current cytosine base editors (CBEs) are limited to unidirectional C to T conversions, restricting their applications. Retrons, bacterial genetic elements, encode a reverse transcriptase that generates multicopy single-stranded DNA (msDNA) by reverse transcribing specific non-coding RNA (ncRNA). This msDNA mimics Okazaki fragments during DNA replication, making retrons promising for gene editing. Here, we developed a retron-based cytosine base editor (RCBE) by fusing cytosine deaminase with reverse transcriptase (RT-CDA) within the retron system. RCBE first transcribes ncRNA, allowing RT-CDA to deaminate cytosine on the ncRNA. The modified ncRNA is then reverse transcribed into msDNA, where RT-CDA induces further cytosine deamination. This mutant msDNA introduces specific mutations into target gene sequences, enabling both C to T and G to A conversions. Using RCBE, we demonstrated accelerated molecular evolution of the rpoB gene in E. coli. High-throughput sequencing confirmed that RCBE achieves a mutation rate of up to 0.2% in regions with high GC content. Our findings establish RCBE as a versatile tool, particularly suitable for directed evolution in GC-rich regions, with broad potential applications across various bacterial and eukaryotic hosts.

Synthetic gene circuits provide experimentally tractable systems for dissecting how genetic feedback and diffusible signals generate multicellular patterns. However, building multi-component circuits whose behavior can be quantitatively linked to module-level measurements, diffusion, and spatial boundary conditions remains challenging. Here, we designed and engineered a bacterial patterning system in which positive feedback, delayed negative feedback, and two orthogonal quorum-sensing signals are integrated in E. coli. We first implemented and characterized the feedback modules separately, measured the effective diffusion of the signals in the experimental setup, and used these data to parameterize a mathematical model. In quasi-2D bacterial lawns, the complete circuit generated self-organized spatiotemporal dynamics consisting of an sfGFP activation front followed by successive mCherry propagating pulses/traveling waves. Model-guided perturbations showed that lawn size, lawn position relative to the domain boundary, and signal degradation modulate the timing, amplitude, wavelength, and directionality of these patterns. Our work establishes a modular synthetic multicellular reaction-diffusion system in which circuit architecture, signal diffusion, and boundary-mediated signal exchange can be experimentally connected to emergent patterning dynamics.

How microbial populations respond to repeated environmental transitions determines both their ecological fitness and their utility in biotechnological applications. Using Pseudomonas putida KT2440 equipped with fluorescent biosensors and monitored by automated flow cytometry in the Segregostat platform, we show that exposure to benzoate, a plastic-derived aromatic feedstock, progressively reduces cellular responsiveness, defined as the fraction of cells that successfully activate a gene circuit following an environmental transition. Unlike classical switching costs, which promote phenotypic diversification, benzoate suppresses responsiveness without increasing population entropy, in a concentration-dependent and circuit-independent manner tightly correlated with fitness loss. A resource allocation model incorporating the competing demands of benzoate assimilation, toxicity, and tolerance reveals that this impairment emerges from a three-way competition for limited cellular resources. Above a critical benzoate load, insufficient resources remain available to sustain the adaptive reallocation required for circuit activation. In continuous culture, a non-responsive subpopulation accumulates as a leading indicator of population collapse. Exploiting this signal, we implement a two-stage connected bioreactor system in which benzoate feeding is autonomously regulated based on real-time population structure, enabling complete substrate consumption and stable operation at otherwise destabilizing concentrations. These results establish cellular responsiveness as a quantitative population variable and demonstrate that structure-aware feedback control, acting on population composition rather than bulk physiology, provides a principled route toward autonomous bioprocesses on challenging substrates.

Non-coding RNAs play diverse roles in a wide range of cellular processes, with their spatial structure being pivotal to their function. RNA secondary structure is a key determinant of its overall fold. Given the scarcity of experimentally determined RNA 3D structures, understanding secondary structure is vital for discerning RNA function. Currently, there is no universally effective solution for de novo RNA secondary structure prediction. Existing methods are becoming increasingly complex without marked improvements in accuracy and often overlook critical features such as pseudoknots and alternative folds. Here, we introduce SQUARNA, a new approach to de novo RNA secondary structure prediction that is suitable for both individual RNA analysis and large-scale structural searches. SQUARNA revisits the concept of base pair maximization and develops it into a stem maximization idea coupled with the widely used free energy minimization (MFE) framework. SQUARNA can predict alternative structures and handle pseudoknots of arbitrary complexity. Benchmarking shows that SQUARNA outperforms existing methods, including deep learning models, in both single-sequence and alignment-based RNA secondary structure prediction. SQUARNA seamlessly integrates sequence and alignment information with experimental data, such as residue reactivities obtained by chemical probing, as well as other structural restraints, including automated searches for Rfam database templates, G-quadruplex patterns, and protein-binding motifs. SQUARNA is available as a standalone tool at https://github.com/febos/SQUARNA and as a web server at https://larnal.imol.institute.

Akkermansia muciniphila is a key member of the gut microbiota and plays important roles in host metabolism and health. In the colon, A. muciniphila extracts nutrients from oligosaccharide-rich mucin glycans that comprise the mucosa. However, this environment is complex and shaped by dietary inputs, microbiome metabolism, and mucin glycan composition varying across hosts, gastrointestinal regions, and physiological states. How strains of A. muciniphila integrate these nutrient signals into growth initiation and niche colonization remains unclear. Here, we compare physiological responses of a human- and mouse-derived strain of A. muciniphila, finding that dietary sugars differentially affect these isolates, suggesting host-associated tuning of metabolic capacity. In contrast, several mucin-derived sugars impose a conserved, concentration-dependent delay in growth initiation, implicating the lag phase as a critical metabolic checkpoint for growth. Genetic suppressor analysis identified sugar kinases and a component of the tricarboxylic acid cycle as genetically encoded control points linking glycan sugar exposure to the energy balance required for growth. These findings demonstrate that mucin-derived sugars function as both nutrients and metabolic stressors, regulating growth initiation. We propose that A. muciniphila employs metabolic “brakes” to coordinate growth with mucin composition, putatively linking host glycan landscapes to microbial physiology and ecological fitness within the mucus layer.

Optimizing growth conditions and culture media is a major goal in microbiology. A challenge is that nutrients can have complex, non-additive effects on growth. The fact that resources interact with one another has long been known in ecology, but systematic maps of resource interactions at all orders have been lacking and it is not known whether interactions are primarily low order or fundamentally complex. To tackle this problem, we have followed a full factorial design approach and measured the growth of seven different bacterial species in all possible combinations of 8 carbon sources under carbon-limiting conditions. Our approach allows us to directly estimate interactions at all orders. Even though all C-sources stimulate growth on their own as well as in combination with other nutrients, most of them can also have negative effects on growth when they are added to at least some nutrient mixtures. We show that the switch from positive to negative fitness effects is governed by global epistasis among resources. An analysis of variance shows that additive effects and pairwise interactions explain most of the variation in fitness, allowing us to train simple regression models that accurately predict bacterial growth in novel environments. The generality of these findings across seven different bacterial strains belonging to two different families indicates that interactions between carbon sources under carbon-limiting conditions may be generally learnable from a relatively sparse set of constructed environments, enabling the rational optimization of growth conditions.