Biological nitrogen fixation in some filamentous cyanobacteria occurs in heterocysts, yet the subcellular organization of nitrogenase in these cells remains poorly defined. Using fluorescence microscopy of living Anabaena sp. PCC 7120, we found that a NifH-GFP fusion forms discrete puncta restricted to mature heterocysts, whereas constitutively expressed GFP in vegetative cells and a nifB-linked GFP reporter remained diffuse. To contextualize this phenotype, we built a homology-aware cyanobacterial condensate-prioritization framework across 31,028 proteins from seven proteomes, integrating ESM-2 embeddings, 27 biophysical features, and 44 curated condensate-associated positives. Because direct positives remain limited, we present the model as a ranking resource rather than a calibrated classifier. Under a deliberately conservative nitrogenase-withheld analysis that removed nitrogenase-family labels and homologous clusters from the training data, NifH retained a median rank in the highest-scoring 4.8% of cyanobacterial proteins. catGRANULE 2.0 also assigned high scores to nitrogenase iron proteins, while having only modest rank concordance to the whole atlas. Together, these data identify heterocyst-restricted NifH-GFP puncta as evidence of sub-cellular spatial organization and provide a curated resource for prioritizing cyanobacterial condensate candidates, some of which are also important for nitrogen fixation.

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.

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.