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.

Cell differentiation and Turing-like patterning are tightly associated in a group of filamentous cyanobacteria that differentiate specialized N2-fixing cells, called heterocysts. Based on systematic genetic analyses, in particular genome-wide identification of recognized promoters and assays with a reconstituted Anabaena transcription system in E. coli, we established HetZ as the central activator of the gene regulatory network of heterocyst differentiation. Biochemical and cryo-EM analyses further established HetZ as a σ factor (σHetZ). Unique domain insertions in σHetZ are involved in promoter DNA recognition-unwinding and interaction with the inhibitor PatU3. σHetZ-PatU3 and the master regulator-diffusible inhibitor constitute the minimal core regulatory circuit (CRC) for cell fate determination and patterning. σHetZ activates not only genes of the CRC but also downstream regulator/effector genes involved in morphological and functional development. The gene regulatory network and the structure–function relationship of σHetZ depict how cell differentiation and patterning are coordinated in this group of multicellular cyanobacteria.

Rapid technological progress has enabled plant biologists to accumulate unprecedented volumes of multi-scale, multi-modal data, yet this abundance of data has intensified the challenge of translating complexity into biological understanding. Foundation models (FMs), large-scale artificial intelligence (AI) systems pretrained on millions of sequences, structures, or images and adaptable to diverse tasks are breaking through this barrier. Across plant science, these FMs are already making an impact: genomic FMs decode regulatory grammar, protein FMs enable rational protein engineering, vision FMs score phenotypes at breeding-population scale, single-cell FMs annotate cell types across species, and FM-powered AI agents accelerate knowledge retrieval and automate research workflows. While experimental validation remains indispensable, foundation models empower plant scientists to accelerate scientific discovery.

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.

E. coli encounters chemically diverse carbon sources, and the observed outputs of its transcriptional regulatory network (TRN) vary with substrate chemistry, metabolic entry route, and growth physiology. Here, we compiled PRECISE-NP881, an 881-condition transcriptome compendium comprising 346 RNA-seq profiles generated for this study during growth on 43 individual carbon sources, and used independent component analysis to quantify condition-specific activities of 137 iModulons, defined here as statistically independent gene-expression modules. We identified 25 carbon-catabolism iModulons and summarized their activity patterns across the 43 substrates into four activity-defined substrate groups. These activity patterns were associated with measured growth rates, substrate chemical classes, central-metabolic entry routes, carbon-normalized stoichiometric yield, and model-estimated proteome allocation. Faster-growing sugar conditions showed low CRP-linked iModulon activity, whereas slower-growing conditions showed elevated, condition-specific activity of CRP-linked and substrate-specific catabolic iModulons. TCA-entry and amino acid–associated conditions were linked with NtrC-1 and Propionate iModulon activities, with targeted knock-out assays supporting the conditional physiological relevance of selected propionyl-CoA-associated genes. A subset of nitrogen-containing, slower-growth conditions with predicted ammonium release induced the cryptic prophage-associated SgcABCEQX iModulon. Projection of an independent glucose starvation/refeeding time-course dataset revealed overlapping dynamics among selected carbon-catabolism iModulons and coordinated changes in growth- and stress-associated TRN outputs. Together, these results provide a systems-level atlas of observed carbon-responsive transcriptional states and systematize carbon physiology at scale.

Isoprenoid quinones are redox-active lipids essential for numerous cellular processes, including ATP synthesis via electron transport chains. They are found across all domains of life. Their diversity among microbial taxa has established them as chemotaxonomic markers, while recent studies have linked quinone repertoire variations to metabolic adaptations, and have identified novel quinone types. Despite this renewed interest, a comprehensive overview of quinone distribution across Bacteria is missing. Here, we systematically annotated quinone biosynthetic pathways for major quinones (ubiquinone, rhodoquinone, plastoquinone, and menaquinone) across 26,264 high-quality bacterial genomes. We also text-mined quinone data from over 6,000 species in published abstracts to validate our genomic annotations while compiling valuable information on quinone chains. Mapping these data onto a phylogenetic framework provides the most extensive synthesis of quinone distribution in Bacteria to date. Our analysis reveals an unexpectedly dynamic evolutionary history for the two menaquinone-producing pathways. Furthermore, the discovery and experimental validation of a deeply branching ubiquinone pathway in the phylum Desulfobacterota provide the first evidence of such a pathway outside the phylum Pseudomonadota, offering new insights into the nature of the ancestral ubiquinone biosynthetic route. Beyond these discoveries, the global overview of bacterial quinones distribution presented here paves the way for future investigations into quinone-associated biochemistry and physiology, including predicting quinone structural features from genomic data, exploring correlations between quinone structures and cellular traits, and studying quinone repertoire evolution in relation to microbial metabolic diversification.