Training physical neural networks directly in matter remains difficult because most platforms do not implement weight storage and weight update within the same physical substrate. Here we show that engineered E. coli can implement a genetically encoded local learning rule acting on a persistent biological memory. In memregulons, analogue weights are stored as plasmid copy-number ratios in a coupled two-plasmid system and are rewritten by activity-dependent growth bias under a global negative learning signal. In single-strain cultures, theory predicts that the change in mean weight is proportional to the activity of the learning channel and to the standing variance of the stored distribution, and flow-cytometry trajectories across eight distinct promoters driving the learning channel support this prediction quantitatively. At the single-cell level, repeated negative learning also reshapes the stored distribution by narrowing it and increasing its skewness as weights approach the lower boundary. In mixed populations and nine-strain co-cultures, one global negative learning signal selectively rewrites only the active memregulons, enabling supervised adaptation in a bacteria-versus-bacteria tic-tac-toe tournament. We then generalise this principle across nine orthogonal chemical inputs and combinatorial promoters, including channels controlled by quorum-sensing molecules, and use it to rationally design a biological XOR gate. Finally, we examine multilayer ANN-like architectures with a human-in-the-loop protocol in which weight updates remain physically implemented and parameterised by experimental measurements, while inter-layer communication is supplied externally. These results establish a route to physical learning in living matter and provide a modular foundation for adaptive multicellular computation, paving the way for autonomous biological hardware capable of distributed environmental sensing and next-generation cellular therapeutics.

The ability to access, search, and analyse large collections of RNA molecules together with their secondary structure and evolutionary context is essential for comparative and phylogeny-driven studies. Although RNA secondary structure is known to be more conserved than primary sequence, no existing resource systematically associates individual RNA molecules with curated phylogenetic classifications. Here, we introduce PhyloRNA, a curated meta-database that provides large-scale access to RNA secondary structures collected from public resources or derived from experimentally resolved 3D structures. PhyloRNA allows users to search, select, and download extensive sets of RNA molecules in multiple textual formats, each entry being explicitly linked to phylogenetic annotations derived from five curated taxonomy systems. In addition to taxonomic information, each RNA molecule is accompanied by a rich set of descriptors, including pseudoknot order, genus, and three levels of structural abstraction - Core, Core Plus, and Shape - which facilitate comparative analyses across sets of molecules. PhyloRNA is publicly available at https://bdslab.unicam.it/phylorna/ and is regularly updated to incorporate newly available data and revised taxonomic annotations.

Terminal-based workflows are central to large-scale structural biology, particularly in high-performance computing (HPC) environments and SSH sessions. Yet no existing tool enables real-time, interactive visualization of protein backbone structures directly within a text-only terminal. To address this gap, we present StrucTTY, a fully interactive, terminal-native protein structure viewer. StrucTTY is a single self-contained executable that loads mulitple PDB and mmCIF files, normalizes three-dimensional coordinates, and renders protein structures as ASCII graphics. Users can rotate, translate, and zoom in on structures, adjust visualization modes, inspect chain-level features and view secondary structure assignments. The tool supports simultaneous visualization of up to nine protein structures and can directly display structural alignments using Foldseek's output, enabling rapid comparative analysis in headless environments. The source code is available at https://github.com/steineggerlab/StrucTTY

Zymomonas mobilis is an ethanologenic Alphaproteobacterium with many interesting characteristics for fundamental research and applied microbial engineering. Although genetic engineering has been established for Z. mobilis since the 1980s, a rich set of inducible transcriptional regulators is still unavailable. In this work, seven different chemically inducible promoters have been systematically tested for their functionality in Z. mobilis. In particular, for the first time, NahR-PsalTTC, VanRAM-PvanCC, CinRAM-Pcin and LuxR-PluxB have been characterized in Z. mobilis, alongside the commonly used regulator-promoter pairs TetR-Ptet and LacI-PlacT7A1_O3O4, and the less commonly used XylS-Pm. All promoters investigated in this work are compatible with the Golden Gate modular cloning framework Zymo-Parts. Characterization was carried out with a shuttle vector backbone based on pZMO7, which has so far been rarely used for applications in Z. mobilis but seems to be completely stable without selection and generates high and uniform levels of expression. From the experimental results presented, it can be concluded that VanRAM-PvanCC and CinRAM-Pcin are particularly promising for broad use in the Z. mobilis community.

Bacteria in the human gut influence host physiology and disease risk, but their ecology is strongly shaped by mobile genetic elements (MGEs) such as phages and plasmids. Past interactions between bacteria and MGEs can be inferred from CRISPR-Cas cassettes, which contain short DNA fragments derived from invading elements. To lay the groundwork for research on the impact of such interactions on the human host, we examined bacteria, MGEs, and CRISPR-Cas in the gut microbiome. Using fecal shotgun metagenomes from 1034 Norwegians, we constructed an extended microbiome resource comprising 1.7K prokaryotic mOTUs, 19.5K viral vOTUs and 24.2K plasmid PTUs. We also recovered 74.2K unique CRISPR-Cas cassettes to map past bacteria-MGE interactions and assessed their associations with human diet and lifestyle factors. CRISPR-Cas spacers, and which viruses and plasmids they targeted, varied substantially within bacterial species, but were predominantly directed towards cohort-specific MGEs. Moreover, bacteria were more likely to target MGEs present in the same sample, consistent with local exposure. Bacteria also shared more targets within taxonomic families than across families, where mobilizable plasmids were more frequent among the targets. We did not find evidence that CRISPR-Cas spacers were related to characteristics of the human host, beyond the host-bacteria associations. Together, this research provides a large-scale resource and a structured analysis of bacteria-MGE interactions in the gut microbiome, and their contribution to microbial ecosystem dynamics.

The transfer of virulence factors into eukaryotic cells is a hallmark of bacterial pathogenesis. We report the expression, interspecies transfer, subcellular localization, and potential functions of three unusually large virulence factor-like proteins that underlie a bipartite mutualistic bacterial symbiosis. These proteins are synthesized by green sulfur bacterial epibionts surrounding a central motile chemoheterotroph in the multicellular phototrophic consortium 'Chlorochromatium aggregatum'. While symbiosis-proteins remain intracellular during axenic epibiont growth, they are transferred to the partner bacterium in the association. An RTX-like protein secreted towards the central bacterium is capable of degrading its alginate capsule, thereby promoting direct cell-to-cell contact. Two gigantic hemagglutinin-like proteins are predicted to fold when binding extracellular Ca2+ to form Type 6-like auto injection needles, explaining their observed transfer into the central bacterium. These functionalities extend far beyond the known pathogenic interactions of bacteria with eukaryotes and provide new perspectives on the evolution of bacterial virulence factors.

Plastic biodegradation in natural environments is increasingly recognized as a multi-organism process, yet the mechanisms enabling coordinated depolymerization and metabolism of polyethylene terephthalate (PET) remain poorly understood. Previously, we demonstrated that a full consortium containing three Pseudomonas and two Bacillus strains isolated from hydrocarbon-rich coastal soils of Galveston Bay, Texas, can synergistically depolymerize PET plastic and utilize it as a sole carbon source, a capacity not observed in individual isolates. In this report, using integrated comparative genomics, proteomics, and chemical analyses, we show that PET degradation in this system reflects exaptation of hydrocarbon metabolism reinforced by metabolic division of labor. Within this naturally occurring consortium, Bacillus strains persist under environmental stress, establish biofilms, and perform essential secondary hydrolysis, while Pseudomonas strains catabolize aromatic monomers and buffer oxidative stress. Genes supporting these functions are enriched within the accessory genomes of the consortium strains, indicating consortium-enriched horizontal gene transfer (HGT). In addition to the canonical two-step hydrolytic pathway well documented in PET biodegradation, we identify a secondary methylation- and redox-associated process, mechanisms where the full consortium acts on the oligomer mono(2-hydroxyethyl) terephthalate (MHET), yielding nearly complete conversion to terephthalic acid (TPA) and methylated MHET (MMHET). Together, these findings demonstrate how cooperation and competition within consortia facilitate targeted gene exchange, enabling emergent plastic biodegradation in natural microbial communities.

By mapping ribosome-protected fragments (RPFs) genome-wide, ribosome profiling (Ribo-seq) has uncovered extensive translation beyond conventional coding sequences, revealing non-canonical ORFs (ncORFs) with emerging roles in diverse biological processes. However, protocol-induced biases introduced during library construction can substantially distort RPF signals. Most existing ORF callers are not designed to explicitly account for such artifacts, limiting robust ncORF identification. Here, we present RiboBA, a bias-aware probabilistic framework to address this challenge. RiboBA consists of two main components: a generative module that recovers protocol-induced biases and codon-level ribosome occupancy, and a supervised module that identifies translated ORFs and initiation sites using the resulting bias-adjusted profiles. Evaluated through simulations and on a range of Ribo-seq datasets-particularly supported by cell-type-specific immunopeptidomics-RiboBA robustly recovers protocol-induced parameters and achieves superior accuracy and sensitivity in ncORF identification. Notably, RiboBA performs particularly well on RNase I libraries with attenuated three-nucleotide periodicity, as well as on MNase and nuclease P1 libraries, while maintaining competitive runtimes. In a Drosophila case study, RiboBA identifies conserved ncORFs with coding potential, including recurrent upstream translation of ThrRS and Mettl2 that suggests a potential threonine-specific translational control axis.

Microbes have the potential to manufacture plastics from sustainable feedstocks while enabling novel material properties and functions that are not easily accessible through conventional chemical synthesis. Realising this potential requires a comprehensive genetic and process engineering framework that spans chassis and bioprocess optimisation, polymer property control, and downstream functionalisation. Here we develop such a platform in Cupriavidus necator, with a focus on high-value polyhydroxyalkanoate (PHA) nanoparticles. To this end we first optimize the transformation protocol for the organism. Next, we create a library of PhaC synthase variants from C. necator, Aeromonas caviae and Brevundimonas sp. in a ΔphaC background, demonstrating that they allow customisation of the material properties of produced PHA particles. Our results combine data from Flow cytometry, Transmission Electron Microscopy (TEM), Fourier Transform InfraRed Spectroscopy (FTIR), and Differential Scanning Calorimetry (DSC) to show that it is possible to generate materials ranging from highly crystalline PHAs to softer P(3HB-co-3HHx) copolymers and that an A. caviae PhaC variant can double the yield of large PHA granules. To improve bioprocess sustainability, we coupled C. necator with B. subtilis in sucrose-fed co-cultures, using tetracycline tolerance differences and inoculation ratios to enhance PHA production from inexpensive, sugar-rich feedstocks. Finally, we add function to the produced PHA nanoparticles by using the molecular protein-fusion technology SpyTag-SpyCatcher, showing it is possible to efficiently capture SpyCatcher-GFP on PHA granules as a proof of concept for PHA′s use as a customisable bio-based nanoparticle. Together, our work offers an innovation to produce bio-PHA nanoparticles in a customisable way, with potential applications in sustainable biomanufacturing, biosensing, drug delivery and future bioremediation technologies.

Many experiments rely on expensive or scarce liquids, such as costly reagents, or biological samples available only in limited quantities. Droplet microarrays are an especially promising approach to conserving these materials because they support highly parallelized reactions in small volumes. However, existing droplet microarray loading methods based on discontinuous dewetting suffer from loading inconsistencies and large dead volumes. In this work, we present the Small Volume Loader (SVL) for the Surface Patterned Omniphobic Tiles (SPOTs) platform that enables precise deposition on droplet microarrays while minimizing reagent waste. By establishing a physical model of the loading process, we identified that deposition volume is governed by the sum of hydrostatic and Laplace pressures at the reservoir outlet. To optimize performance, we engineered a pressure-compensating flared reservoir geometry that maintains constant total pressure regardless of the remaining liquid level. This design ensures that the deposited volume is independent of reservoir volume and reduces dead volume to 5 µL. We demonstrated the platform's utility through high-throughput elicitor screening for natural antimicrobial production from Streptomyces venezuelae. The resulting assays used 100-fold less material than conventional methods, allowing us to conduct over 32,000 assays with modest quantities of starting material. This enabled us to identify specific stressors that optimize the production of the antibiotics chloramphenicol and jadomycin B. Together, we demonstrated improved loading performance for droplet microarray platforms, allowing precise, accessible, and high-throughput assays using only minimal volumes of scarce materials.