Our understanding of protein function and evolution is largely based on the relationship between amino acid sequence and overall fold, now effectively captured by computational models. Yet predicting how mutations—shaped by epistasis—alter protein behavior, especially in dynamic or structurally ambiguous regions, remains difficult. Here we present D2D, which combines a self-supervised protein language model with protein-specific evolutionary information to predict mutational effects using little to no task-specific labeled data. D2D captures long-range epistatic interactions, accurately predicts single and higher-order mutation effects on protein thermostability and binding, without being trained on the task. When fine-tuned, D2D outperforms state-of-the-art methods on latent driver cancer mutations and co-occurring proliferation-enhancing mutations across independent experimental studies. Unlike most existing approaches, D2D avoids biases linked to solvent accessibility or to multiple sequence alignment depth and quality, making it particularly effective for disordered or surface binding regions where structure-based predictors typically falter. Overall, D2D provides a general framework for modeling mutational effects in proteins with limited experimental or structural information.

How microbial communities maintain robust and reproducible ecological functions despite their extraordinary taxonomic diversity remains an open question. Here we show that functional organization in microbial communities can be uncovered by repurposing Principal Component Analysis to focus on directions of lowest variance in taxon abundance data, rather than maximal variance. These least-variance components are statistically significant and correspond to ecological constraints on taxon abundances that are consistently fulfilled across samples. Using consumer-resource models, we show that these constraints arise from resource-mediated interactions and express biomass conservation, effectively grouping taxa into producer and consumer guilds. We validate this interpretation in simulated communities and experimental systems under competition and cross-feeding. Finally, we show that low-variance structure is ubiquitous in natural microbial communities and reveals a sparse network of taxa with disproportionate influence on community structure. Together, our results establish low-variance components as indicators of ecological constraints linking taxonomic diversity to functional organization.

The tricarboxylic acid (TCA) cycle is an essential part of the central metabolic hub that provides energy and biosynthetic precursors. Efficient regulation of central carbon flux is critical for maintaining optimal productivity of microbial cell factories (MCFs). However, biosensors capable of sensing TCA intermediates remain limited. Here, we engineered the catabolite control protein C (CcpC) from Bacillus species to reconstruct citrate-responsive biosensors in E. coli. Through hybrid promoter engineering, we systematically characterized and identified the functional roles of two CcpC binding sites. By applying the hybrid promoter, the engineered biosensor BcCcpC-PLBs exhibited the broadest dynamic range and highest expression level among its counterparts. Ligand profiling revealed the diverse responsiveness of BcCcpC to multiple metabolites of the TCA cycle. By structure-guided mutagenesis of BcCcpC, the obtained variant BcCcpC(S138L) exhibited an improved dynamic range of up to 3.02-fold under 80 mM citrate induction. This work establishes the first transcription factor (TF)-based citrate-responsive biosensor, which broadens the regulatory toolkit for central metabolism engineering.

DNA methylation plays critical roles in gene regulation in bacteria, from regulating essential processes like the cell cycle to phenotypes of practical interest like pathogenicity and motility. Synthetic manipulation of global methylation levels has broad impacts on cellular physiology, changing expression patterns of hundreds of genes. However, whether or how environmental variation in natural settings similarly impacts DNA methylation patterns has been unclear. In this work, using the alphaproteobacteria Methylobacterium extorquens and Caulobacter crescentus as model systems, we discover the methylome is highly fluid in response to environmental variation, with different environments leading to distinct patterns of increased or decreased methylation levels along the chromosome. Despite a heterogeneous effect of different environments on methylation patterns, we find a general principle where the dependence of methylation states on position in the genome decreases in proportion to growth rate. A simple model that considers the methylation state through different phases of the cell cycle as a function of distance from an origin provides a framework to interpret the effects of different stressors upon the observed environmental responsiveness of the methylation patterns. Our work highlights how sequencing data alone can shed light on important aspects of microbial physiology.

Tandem gene duplication drives antibiotic resistance, metabolic adaptation, and gene-family expansion in bacteria, but no tool detects them in reference genomes, discovers their junctions in isolate sequencing, and quantifies the junctions in population samples. Existing callers (e.g. breseq) detect duplications without classifying formation mechanisms and often fail to quantify the duplication.  Tandem has 3 modules. Module 1 detects reference-genome duplications by NUCmer self-alignment and classifies each by homologous-recombination signature and the junction microhomology length. Module 2 confirms junctions in whole-genome sequencing at user-nominated coordinates after user inspecting the coverage plot. Module 3 quantifies known junction in population sequencing using the novel Junction Read Ratio (JRR). On 280 artificial population tests across seven bacterial species, Tandem achieves 100% recall and 4.3% mean absolute error. Applied to experimentally evolved Pseudomonas fluorescens SBW25 populations, Tandem resolves multiple co-segregating duplication fragments.

Root competence, the ability of soil bacteria to establish and grow on plant roots, is a key ecological trait influencing plant nutrition, growth, and health. However, identifying genomic determinants of root competence across bacteria remains challenging, in part because model generalizability depends strongly on how genomes are represented. Traditional approaches based on curated annotations are incomplete and biased toward well-characterised organisms and functions, limiting generalization. Sequence-similarity clustering improves coverage but yields high-dimensional features relative to dataset size, hindering training. Foundation models offer an alternative by learning compact representations without relying on prior annotation. Here, we compared pretrained genome representations from protein and DNA foundation models (ESM-2, Bacformer, DNABERT-S) with annotation- and clustering-based features (KEGG orthology, OrthoFinder protein families) for predicting root competence using synthetic microbial community data from Arabidopsis thaliana and assessed generalisability across bacteria. When training and test sets contained taxonomically related bacteria, most approaches performed similarly. However, when test bacteria belonged to phyla entirely absent from training, reflecting high evolutionary separation across all levels of bacterial classification, only pretrained protein representations retained predictive performance. Bacformer-derived representations, which incorporate genomic context, supported the strongest generalisation, suggesting that conserved genomic organisation contributes to predicting root competence. Feature attribution quantifying protein contributions to model decisions linked root competence to TonB/SusD-dependent receptors, small-molecule transporters, and unannotated proteins with conserved regulatory motifs and homology to carbon starvation-response loci. Protein foundation models support generalisation across evolutionarily distant bacteria and identify genomic determinants of root competence, including unannotated proteins.

Rhodobacter sphaeroides, a purple nonsulfur photosynthetic bacterium, displays exceptional metabolic versatility, enabling growth under both aerobic and anaerobic conditions and utilization of diverse carbon sources. Its flexible metabolism, combined with native pathways for terpenoid and tetrapyrrole biosynthesis, makes it a highly promising microbial chassis for the production of valuable compounds. Advances in metabolic and synthetic biology have allowed the engineering of R. sphaeroides for the efficient synthesis of coenzyme Q10 (CoQ10) and porphyrin derivatives through strategies such as precursor supply enhancement, pathway optimization, modulation of redox and energy balance, manipulation of global regulatory systems, and fermentation control. Beyond CoQ10 and porphyrins, this organism holds the potential to produce hydrogen, carotenoids, and other high-value terpenoids. This review summarizes the metabolic features, native regulatory networks, and engineering approaches in R. sphaeroides, highlighting its versatility and robustness as a platform organism. The insights provided here underscore its potential as a chassis for synthetic biology applications and industrial bioproduction of a wide range of bioactive compounds.

Microbial communities deliver essential functions in ecosystems. In plant environments, the plant microbiome facilitates nutrient uptake, supports plants during abiotic stress, and counteracts disease. As implementation of synthetic microbial communities becomes more of a realistic strategy for mitigating the effects of biotic and abiotic stressors on plant productivity, it is increasingly important to understand how interactions between microbes, which are essential for ecosystem function (hub microbes), are maintained. Recent research highlights the ecological role of bacteriophages, the viruses of bacteria, in host-associated microbial communities. Current evidence demonstrates the influence of the phageome on microbiomes, ranging from effects on an individual (transduction, lysogenic conversion, and evolutionary pressure) to entire populations and communities, such as Kill-the-Winner dynamics. These dynamics appear to affect the overall function of microbial communities and support plant growth. In this review, we lay out recent insights on the role of bacteriophages in plant-associated microbiomes through an eco-evolutionary lens and future directions of research to broaden our understanding of the ecological implications of bacteriophages.

Lactic acid bacteria (LAB), as one of the key microorganisms in the food industry, play a crucial role in functional food fermentation. However, current LAB strains are limited by challenges, such as plasmid instability, low gene expression efficiency, and complex regulation of metabolic fluxes, which hinder their broader application. This review provides an overview of the traditional applications of LAB while highlighting current limitations that constrain their effective use. Then, it focuses on synthetic biology-driven strategies for precisely designing and expanding functions through gene editing, metabolic engineering, and genetic circuits. Finally, this review discusses how to improve gene expression efficiency in LAB and the use of directed evolution to optimize exogenous genes, offering perspectives for future research in the development of personalized food applications. The emerging tools of synthetic biology will further improve the production efficiency and product diversity and meet the needs of consumers for high-quality, multifunctional, and personalized food.

The endosymbiotic evolution of plastids and mitochondria was central to the origin and success of eukaryotes. One of the most prominent molecular machineries thought to have disappeared early in eukaryote evolution is the multi-subunit bacterial DNA polymerase III (DNApol-III), which is the principal enzyme complex supporting DNA replication in bacteria. Here, we combined worldwide metagenomics and cultivation to characterisz the mosaic genomic landscape of abundant phytoplankton lineages of Teleaulax (Cryptophyceae), which contain an endosymbiotically-derived nucleomorph genome. Unexpectedly, the nuclear, plastid and nucleomorph genomes of Teleaulax contain ubiquitously expressed genes for plastid-targeted DNApol-III subunits. These genes shed light on the functioning of Teleaulax genomes when sequestered by the ciliate Mesodinium during its kleptoplastidic photosynthetic activity. In particular, the alpha subunit gene (encoding the polymerase activity), which resides in the nucleomorph genome, is continuously expressed in Mesodinium in controlled laboratory experiments. This provides a mechanistic explanation for the replication of Teleaulax plastid genomes weeks after the nuclear genome is lost. Beyond Teleaulax and close relatives, we also identified genes encoding plastid-targeted DNApol-III subunits (including alpha) in nuclear genomes of unicellular and multicellular lineages of Archaeplastida that form, along with those of Cryptophyceae, monophyletic clades firmly positioned within Cyanobacteria. Together, our results reveal a previously overlooked retention of bacterial DNA replication machinery from plastid primary endosymbiosis in Archaeplastida, its acquisition by Cryptophyceae during secondary endosymbiosis, and its direct role in contemporary plankton as a facilitator of kleptoplastidic photosynthetic activity by heterotrophic ciliates.