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.

Bacteriophages (phages) play essential roles in microbial systems, yet most phage proteins remain poorly characterized. Protein tertiary and quaternary structure information contributes valuable information about protein function. As many phage proteins function as homooligomers, complexes that consist of multiple identical subunits, there is great interest in computationally predicting their configurations. Here we present a computational framework, the Phage Homomer Level Estimate and Generation Method (PHLEGM) for inferring homooligomeric states directly from the protein sequence by combining AlphaFold-Multimer modelling with inter-subunit interface quality assessment. We proceeded to experimentally validate two out of nine predicted homooligomers using size exclusion chromatography and complementary hydrodynamic techniques. These efforts confirmed our predictions for a dimer and a trimer, highlighting the value of experimentally benchmarked computational predictions and showing the challenges of heterologous phage protein production. Applied to >22,000 phage protein sequences in the PHROGs database, our approach revealed extensive diversity in phage homooligomeric protein complexes. Benchmarking against protein language model-based predictors on a curated reference set of known phage homooligomers demonstrated superior accuracy of our structure-based method, achieving robust performance in classifying protein homooligomeric states, with the highest accuracy observed for trimers and higher-order complexes. These results highlight the value of computational predictions to decipher the complexities of the vast viral sequence space. All predicted complex structures and functional inferences are made publicly available to support structural and functional studies of phage proteins.

Cross-feeding interactions, in which a producer species release by-products that serve as resources for a consumer species, play an important role in shaping microbial community diversity. Producers create opportunities for consumers by supplying high-energy resources that are often scarce in the environment. However, they also exert strong top-down effects by releasing metabolites in pulses and generating spatial gradients of resource availability. How these spatiotemporal constraints shape consumer evolution remains poorly understood. To address this question, we used a two-species cross-feeding system in which Acinetobacter johnsonii excretes benzoate (a by-product of benzyl alcohol oxidation) into the external environment where it is consumed by Pseudomonas putida. To assess how the origin of benzoate (externally supplied or produced by cross-feeding) shapes consumer evolution, we evolved P. putida for 200 generations in monoculture or in co-culture with A. johnsonii. Populations evolved in monoculture exhibited improved growth relative to the ancestor, whereas populations evolved under cross-feeding showed little to no growth improvement. Whole-genome sequencing revealed pervasive loss-of-function mutations in flagellar genes among populations evolved in monoculture, but not under cross-feeding conditions. High-throughput imaging assays showed that populations evolved under cross-feeding not only maintained but also enhanced functional motility. Competition experiments with single mutants revealed context-dependent fitness effects: loss-of-function mutations were highly beneficial when benzoate was externally supplied but deleterious when benzoate was supplied by A. johnsonii, highlighting the importance of motility in cross-feeding interactions. Together, our results show that resource origin fundamentally reshapes selective pressures and alters evolutionary outcomes in microbial communities.

We identify a peptide discovered through fluorescence-based screening of a cell-penetrating peptide (CPP) library that preferentially associates with bacterial spores and evaluate its utility as a fluorescence-guided tool for spore isolation. Specifically, an eight-ornithine peptide conjugated with 5(6)-carboxyfluorescein (Orn8-K-FAM) generates a distinct and spatially confined fluorescence signal in spores. Across multiple spore-forming bacterial species, more than 90% of spores were labelled by Orn8-K-FAM, whereas only a minor fraction of vegetative cells from non-spore-forming bacteria exhibited detectable fluorescence, indicating a high degree of preference at the cellular level under the conditions tested. To assess practical applicability, Orn8-K-FAM labeling was combined with flow-cytometric sorting FACS, enabling fluorescence-guided isolation of spores from mixed microbial populations, including both a defined synthetic community and an environmentally derived bacterial fraction. Compared with conventional spore isolation approaches based on selective cultivation or heat-mediated inactivation of vegetative cells, this workflow provides a cultivation-independent means to separate spore-associated cellular states from heterogeneous microbial samples. This work highlights the potential of CPP-derived probes as analytical tools for spore-focused studies in applied and environmental microbiology, while providing a foundation for further investigation into the scope and mechanistic basis of CPP-spore interactions.

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations incorporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and HuggingFace (https://huggingface.co/BGI-HangzhouAI/Genos-m).

Horizontal gene transfer (HGT), the movement of genetic material between unrelated organisms, is widely recognized as an important driver of genome evolution in bacteria. In eukaryotes, however, the evolutionary impact of HGT remains debated. The identification of interkingdom HGT (iHGT) is especially challenging due to the lack of gold standard methods. Traditionally, iHGT identification has relied on manual inspection of phylogenetic trees, a process that is subjective, difficult to reproduce, and not scalable to large datasets. In this study, we present a computational framework that formalizes phylogenetic tree interpretation into a supervised machine-learning problem. We define five recurrent phylogenetic patterns—iHGT, NoHGT, Limited donor evidence, Multiple major clades (Multiple MC), and Patchy phylogeny—capturing clear and ambiguous evolutionary scenarios. To operationalize these patterns, we developed a feature-extraction pipeline that quantifies taxonomic composition and phylogenetic topology using seven biological descriptors derived from gene trees. These features were used to train and evaluate multiple machine-learning models, among which a Random Forest (RF) classifier achieved the best performance (AUC–ROC = 0.98; accuracy = 0.89). Model interpretability analyses revealed that topological distance to additional clades and lineage diversity are the most informative predictors, reflecting key signals used in expert-driven phylogenetic interpretation. The RF model was further validated using 1,000 simulated phylogenies and 1,438 real iHGT candidates, achieving low misclassification rates (7.8% and 10.43%, respectively). Benchmarking against AVP (Alienness vs. Predictor), a comparable tool for iHGT detection, demonstrated improved performance across all evaluation metrics, highlighting the advantages of incorporating global phylogenetic structure into the classification process. This study provides a reproducible and scalable framework for phylogenetic pattern classification that captures complex evolutionary signals while maintaining biological interpretability. Beyond improving iHGT detection, the approach offers a more nuanced representation of evolutionary scenarios by explicitly accounting for inconclusive cases, supporting more robust inference in comparative genomics.

Naphthenic acids are amphipathic compounds whose toxicity has primarily been attributed to narcosis toxicity to cell membranes. However, few methods exist that specifically study the membrane disruption and toxicity of this complex family of cyclic, polycyclic and acyclic alkyl-substituted carboxylic acids. Here we describe a whole cell biosensor approach that relies on the ability of Pseudomonas aeruginosa, a ubiquitous environmental organism and opportunistic pathogen, to sense membrane damage (narcosis) and induce protective genes to repair and protect the outer membrane. Many classes of membrane disrupting antimicrobials induce the expression of two operons that encode protective defense systems against outer membrane (OM) damage, including antimicrobial peptides, chelators, and detergents. We demonstrate that the pmrF and spdE2 transcriptional lux reporters are induced by exposure to individual NA compounds with diverse structures, as well as mixtures and naphthenic acid fraction compounds (NAFCs). To further support the narcosis hypothesis, we demonstrated that NA permeabilizes the outer membrane to assist in lysozyme killing, and disrupts the inner membrane integrity, allowing uptake of the DNA binding dye propidium iodide. The conventional OM permeability assay that measures NPN fluorescence is not applicable to study NAs, because they stimulate NPN fluorescence in the absence of cells. This narcosis biosensor approach constitutes a rapid and simple method to measure narcosis and could be developed as a novel toxicity indicator of oil sands tailings.

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.

Bottom-up manufacturing of structural DNA nanotechnology requires a long single-stranded DNA (ssDNA) scaffold and hundreds of short (~30 nt) ssDNA staples. However, scaling production remains bottlenecked by the high economic cost and environmental footprint of solid-phase chemical staple synthesis. To address these limitations, a phage-free, biological nanomanufacturing platform engineered in E. coli is developed here. Two intracellular strategies for producing programmable ssDNA were systematically evaluated: retron-based multicopy ssDNA (msDNA) synthesis via the Ec67 system and plasmid-encoded rolling circle replication (RCR). While native structural topology constraints within the retron (msd) cassette limit its sequence-design flexibility, the alternative RCR-driven engine successfully decouples ssDNA replication from sequence secondary structures, enabling the synthesis of arbitrary staples. This RCR platform reliably generates long circular ssDNA (cssDNA) precursors of at least 1.8 kb with exceptional sequence fidelity (>99%). Integrating programmable BseGI cleavage sites allows targeted strand-selective enzymatic processing to cleanly release stoichiometric, origami-grade pools of 32-nt staple strands. Atomic force microscopy (AFM) confirms that these biologically produced staples successfully drive the high-fidelity self-assembly of complex DNA tiles and hollow tubules. Crucially, robust structural folding is demonstrated directly within crude, unpurified cellular lysates, establishing a green, cost-effective framework for the one-pot fabrication of advanced DNA-based nanomaterials.

Amino acid-based phylogenetics usually relies on first clustering and aligning orthologous proteins. This approach is powerful but computationally demanding. Here, we present kamino, a reference-free and alignment-free method that builds amino acid phylogenomic alignments directly from proteomes. kamino adapts a local graph-based variant-calling algorithm to efficiently identify variable homologous positions among proteins and concatenate these polymorphic regions. Across diverse prokaryotic and eukaryotic datasets, we showed that kamino is able to generate good quality alignments. Phylogenetic analyses revealed that kamino generally recovered signals broadly similar to those obtained from marker-based approaches, while being much faster. Its main limitations are reduced performance on deeply divergent prokaryotic datasets and substantial memory requirements for large eukaryotic datasets. kamino therefore provides a fast and simple approach for constructing phylogenomic amino acid alignments, complementing classical marker-based workflows. The program is implemented in Rust and is freely available at https://github.com/rderelle/kamino.

Plants rely on root-associated microbiota for growth and pathogen protection, yet the community properties underlying stable beneficial functions remain unclear. Here, we show that low intra-microbiota antagonism is associated with stability and pathogen protection in the barley root microbiota. Using a comprehensive culture collection of barley root endophytes, we reconstructed bacterial synthetic communities (SynComs) and compared a core SynCom resembling the natural microbiota with a trait-prioritized SynCom assembled from strains selected for predicted host benefits. Although strains from both communities exhibited broad pathogen-inhibitory potential, the communities behaved differently in planta. The core SynCom showed low internal antagonism, remained stable, and protected barley roots against fungal pathogens without impairing growth. In contrast, the trait-prioritized SynCom exhibited strong internal antagonism, unstable assembly, loss of protection, and reduced root growth. Together, our findings indicate that microbiota function cannot be predicted from individual strain traits alone. Rather, low intra-microbiota antagonism promotes community stability and enables protective functions to emerge in barley roots.