Bifidobacterium longum is a prevalent human gut symbiont whose carbohydrate metabolism is well characterized, whereas the quantitative contribution of amino acids to growth remains unclear. Here, we combined genome-based pathway analysis, growth phenotyping in chemically defined media, and iterative machine-learning-guided medium design to quantify amino acid preferences in B. longum subsp. longum JCM 1217T. Genome analysis predicted cysteine as the sole auxotrophy, and experiments confirmed that cysteine alone supported growth but did not restore the high maximum cell density and short lag time achieved with a complete amino acid mixture. Regression models and genetic algorithms identified amino acid formulations in the selected optimized compositions that reduced total amino acid input by 66.5% under glucose and 77.2% under lactose while maintaining growth comparable to complete medium. SHAP analysis highlighted tyrosine as the main determinant of maximum cell density, whereas glutamate, leucine, and valine consistently shortened lag time. These results show that amino acid requirements in B. longum extend beyond binary auxotrophy and provide a machine-learning framework for designing streamlined defined media.

The demand for sustainable agriculture has shifted bioprospecting towards microbial bioinputs as alternatives to chemical fertilisers and pesticides. Whole-genome sequencing accelerates the discovery of plant-growth-promoting bacteria (PGPB) by enabling the identification of functional genes and the prediction of traits such as nutrient solubilisation, phytohormone production and biocontrol. Traditionally a secondary tool for strain characterisation, genomics has evolved into a ‘genome-first’ strategy that effectively collapses the phenotypic bottleneck in prospective bioprospecting and the rational design of synthetic microbial communities (SynComs). In this review, we argue for a transition from empirical phenotypic screening towards a genomics-guided paradigm for the selection of next-generation bioinputs. This work demonstrates how actionable insights can be gained through the integration of high-resolution genome mining into discovery pipelines. We explore the application of reverse ecology to infer ecological roles from genomic content and emphasise the critical role of pangenomics in identifying traits linked to host colonization and niche adaptation. Furthermore, we advocate for biosafety screening as a non-negotiable prerequisite for bioinoculant development to ensure ecological and clinical safety. Finally, this work proposes that genome-scale metabolic networks are essential to enable the transition from single-strain inoculants to the assembly of stable SynComs. This framework establishes a comprehensive, data-driven approach to predictable interventions in the agricultural bioeconomy.

Bacillus subtilis is an important chassis for biotechnology, but its use in multiplex genome engineering is limited by low natural transformation efficiency. Here, we compared inducible promoter systems for synthetic activation of the competence regulator ComK and evaluated their effects on the comG operon competence reporter and transformation efficiency. Xylose- and mannitol-inducible systems outperformed IPTG-based constructs and shifted 96-99% of cells into a reporter-positive competent state. However, reporter activation alone did not predict transformation potential. Optimization of culture density and induction timing increased transformant yield 45-fold relative to the initial protocol and 2800-fold relative to the conventional Spizizen method. Disruption of native competence regulatory genes did not improve performance and often reduced transformation output, highlighting the importance of endogenous regulatory circuitry. Using the optimized strain and protocol, we achieved co-transformation frequencies of 11-18% and constructed multiplex spore-display libraries containing fluorescent protein fusions integrated at multiple loci. Screening identified strong dual-display combinations and showed that cargo loading depends on anchor protein, integration locus, and genetic background. SscA fusions supported the highest display capacity and promoted synergistic co-display. Together, these results show improvements in natural transformation-based genome engineering in B. subtilis and provide insight into the construction of multifunctional engineered spores.

Synthetic biology has driven growing interest in engineered bacteria for cancer therapy. Salmonella Typhimurium stands out as one of the most promising live biotherapeutic products (LBPs) because of its innate tumor-targeting and colonizing abilities. However, the pathogenicity and inconsistent performance of wild-type S. Typhimurium have hindered further clinical translation. Recent advances in programmable and modular genetic redesign now enable precise reprogramming of bacterial functions, facilitating the creation of customized LBPs with enhanced safety and efficacy. In this review, we propose a systematic engineering framework with adaptive optimization and therapeutic enhancement for transforming S. Typhimurium into an effective anticancer LBP. This integrated strategy encompasses multiple facets, including strain attenuation, targeted enhancement, colonization optimization, therapeutic production, lysis-controlled release, and auxiliary modules. This review summarizes the key methodologies for engineering S. Typhimurium, highlighting its evolution from a pathogen to an antitumor vehicle. Furthermore, we summarize the multimodule collaborative engineering design and applications and call for more combinatorial strategies to enhance therapeutic efficacy. We also discuss the challenges and bottlenecks of engineered S. Typhimurium from the perspectives of genetic stability and clinical translation. Finally, we highlight how these synthetic biology advances are refining the mechanistic understanding of bacteria-mediated tumor therapy and paving the way toward safer, more effective, and clinically controllable anticancer strategies.

The universe of possible protein sequences is astronomically large, yet our understanding of the sequence-structure relationship is confined to the infinitesimal fraction used currently by life. Determining whether "foldable" architectures are rare singularities or accessible solutions is critical for understanding protein evolution and designing novel proteins. Here, we map the structural landscape of random sequence space by screening one million synthetic proteins using a high-throughput in vivo FRET biosensor. We reveal that this space is structurally heterogeneous, populated not only by disordered chains and stress-inducing aggregates but also by "benign" compact structures that resemble globular proteins and evade cellular chaperone responses. By training machine learning models on these phenotypes, we show that structural potential is learnable and generalizes to natural proteomes. These findings demonstrate that biology-like folds are accessible from random sequences with surprising frequency, providing data required to expand generative protein design beyond evolutionary priors.

High-quality datasets that span broad sequence diversity are essential for understanding protein sequence-function relationships beyond local mutational landscapes. Here, we applied Growth-based Quantitative Sequencing (GROQ-seq) to measure function across an 11,722 member protease library, comprised of natural homologs and AI-shrunken variants. This library spans vast sequence diversity, with Levenshtein distances of up to 245 and a mean pairwise sequence identity of 41% to TEV protease S219V. We identified sequence-divergent TEV protease homologs that preserve function against the native TEV protease substrate. These findings reveal the robustness of protease activity across highly diverse sequences. Here, we demonstrate the aptitude of the GROQ-seq assay for screening large, diverse protein libraries for function, enabling efficient data generation at scale for training machine learning models across broad sequence landscapes.

Scalable genetic circuits are essential for implementing complex functions in living cells. Toward this goal, RNA regulators can provide a much-needed parts library with added benefits of low metabolic load, design flexibility, and logic capacity. However, despite the great potential of synthetic RNA circuits, constructing such circuits with wide dynamic ranges and multiplexed regulatory cascades remains a challenge. To address this, we introduce RATEX (Ribosome-Assisted Transcriptional EXpression controller) by integrating a translation-to-transcription converter with synthetic RNA regulators, enabling a compact and scalable RNA-programmed circuit architecture. The RATEX platform repurposes a large library of well-characterized translation regulators with up to 1,492-fold gene regulation, while leveraging natural ribosome-mediated sensing of diverse environmental inputs, such as metabolites. We demonstrated multi-input logic processing with up to a 6-input OR logic gate for RNA inputs and hybrid 3-input logic gates to sense diverse metabolite and small-molecule inputs alongside RNA signals. Signal amplification with multiplexed combinatorial control of RNA outputs was achieved through multiplexed signaling cascades. Finally, the RNA- and metabolite-sensing 3-input AND gates were used to control cellular morphology and intracellular spatial organization. Together, the RATEX platform, with its scalable and modular architecture, offers a broad potential design space for synthetic biology and biotechnology.

A wide range of microbially produced specialized metabolites (SMs) with bioactivity affecting micro- and macro-organisms has been described. These bioactive compounds are synthesized by enzyme complexes encoded in biosynthetic gene clusters (BGCs). Historically, the first role of SMs was revealed through antimicrobial activity-based assays. However, diverse bioactivities have since been deciphered that are not associated with inhibition of other organisms. To advance discoveries in chemical ecology, synthetic microbial communities (SynCom), simplified, experimentally tractable systems that recapitulate specific features of natural microbiomes, are increasingly employed. SynComs enable the systematic investigation of SM-mediated induction of BGC expression, the characterization of SM bioconversions, and the elucidation of the mechanism by which SMs influence microbial establishment and persistence within communities. Due to their reduced complexity, SynComs allow the controlled determination of microbial community composition and functional dynamics, as well as the characterization of associated chemical diversity. This review highlights representative publications describing how SynComs are employed to elucidate the roles of SMs in complex microbial interactions and emphasizes the emerging functions observed in SynCom-based chemical ecology studies.

Knowledge of the regulatory mechanism of riboswitches is vital for understanding how microorganisms cope with changes in both intracellular and extracellular environments and for developing and applying RNA biosensors. To date, two types of glutamine-based riboswitches, which are exclusively distributed in cyanobacteria, have been identified. Here, we found an RNA regulatory element in the 5′UTR of the nucleoside permease gene (nupC) in Bacillus thuringensis BMB171; it was identified as a novel glutamine riboswitch and named LRN (leader RNA of nupC). Unlike the two previously known types of glutamine riboswitches found in cyanobacteria, LRN is a single-domain RNA element representing a novel type III glutamine riboswitch. Binding glutamine leads to rearrangements the LRN RNA structure, which inhibits downstream gene expression at the transcriptional level. Biocomputational searches revealed that LRN is frequently found in the Bacillus cereus group and is located mainly upstream of the coding region of the nupC homologues. Thus, this RNA-based sensing mechanism establishes a regulatory feedback loop that couples intracellular glutamine levels to nucleoside transport, which is shared by the B. cereus group.

Protein language models (pLMs) capture evolutionary sequence constraints but are limited in modeling underrepresented functional classes due to training data imbalance. Metalloproteins constitute a fundamental but sparsely represented class in sequence databases. We therefore assess whether structure-conditioned synthetic sequences can be used to specialize pLMs toward metal-binding functionality. We fine-tuned the generalist model ProtGPT2 on synthetic sequences generated by the inverse-folding model ProteinMPNN, constructing training sets with controlled variation in size and diversity. Fine-tuning increased recovery of canonical metal-binding motifs from 43% in the baseline model to 91% in the fine-tuned models. Generated sequences retained high predicted structural confidence and structural similarity to known folds, despite low sequence identity. Analysis of latent representations from ProtGPT2 indicated that fine-tuned models occupy distinct regions of embedding space relative to both the baseline model and structure-conditioned sequences, consistent with partial incorporation of structural constraints while preserving sequence diversity. A multi-step filtering pipeline applied to sequences lacking canonical motifs identified candidate metal-binding sites in four-helical bundle topologies not detected in a non-redundant subset of Protein Data Bank structures or in AlphaFold-predicted proteomes.  https://doi.org/10.5281/zenodo.18672158  https://huggingface.co/gsgueglia