Prophages are increasingly recognized as drivers of bacterial adaptation, yet how domesticated prophage genes shape microbial symbiosis remains poorly understood. Here, we identify GDPDXhp1, a glycerophosphodiester phosphodiesterase encoded by prophage Xhp1 of Schaalia odontolytica XH001, as a metabolic regulator linking bacterial physiology, episymbiosis and phage fitness. Association with the epibiotic bacterium Nanosynbacter lyticus TM7x strongly induced gdpdXhp1 expression and triggered GDPDXhp1-dependent lipid droplet accumulation in XH001. GDPDXhp1 orchestrated glycerophospholipid remodeling, membrane biophysical properties, respiration and cell-surface glycan composition in XH001, thereby promoting TM7x episymbiosis. Remarkably, GDPDXhp1 was also required for productive Xhp1 infection by remodeling susceptible bacterial host envelope structures to facilitate phage-host interactions. Together, our findings reveal a domesticated prophage enzyme that contributes to bacterial metabolism, interspecies symbiosis and viral reproduction, highlighting prophage as an overlooked source of metabolic innovation in microbial symbiosis.

Eukaryotic life spans species that diverged over a billion years ago, yet our molecular view of gene regulation remains heavily skewed toward Opisthokonta model systems, particularly yeast and humans. The degree to which the protein features governing transcriptional regulation are conserved across eukaryotes or have diversified within individual eukaryotic supergroups remains unresolved. To provide a distinct evolutionary reference point, we used a massively parallel reporter assay in plant cells to identify transcriptional effector domains and define their sequence constraints, measuring ~26,000 protein fragments tiling nearly all Arabidopsis transcription factors (1,859 proteins). Comparison with the activity of the same peptide sequences in yeast revealed that activation is governed by a mixture of deeply conserved biochemical features and lineage-dependent functional differences across widely divergent eukaryotic lineages. Likewise, plant repression domains are governed by flexible sequence grammars that extend beyond conserved repression motifs. Together, these results reveal both broadly conserved and clade-specific principles governing eukaryotic transcriptional regulation.

Protein-cofactor selectivity prediction is important for enzyme annotation, metabolic engineering, and biocatalyst discovery, but it remains challenging because experimentally supported associations are limited and unevenly distributed, and because biologically relevant differences can be subtler than those captured by general protein-ligand interaction models. Here, we present MINT (Multimodal Interaction Network with Teacher supervision), a multimodal framework for cofactor specificity prediction. MINT combines protein sequence features with cofactor sequence and graph representations and is trained with a gradual adaptation strategy that transfers useful information from a pretrained protein-ligand interaction model to the cofactor prediction task. On a fixed balanced held-out test set, MINT achieved the best overall performance among all compared methods (AUC = 0.932), and this ranking was reproduced in an independent family-stratified evaluation at the natural positive rate (pooled AUC = 0.939). Further analyses suggest that the pretrained model provides transferable structural information, while the gradual training scheme helps adapt that information to cofactor specificity. In a case study of the chemically near-identical NAD/NADP pair, MINT recovered 95.0-98.6% of true binders and resolved a single-phosphate-level distinction with direct relevance to engineering enzyme cofactor specificity. These results show that gradual transfer learning is an effective approach to cofactor specificity prediction when annotation is limited.

In diverse natural communities, cells and phages interact with many potential partners, but the impacts of pairwise coevolution on the overall structure of phage-bacteria interaction networks (PBINs) is not fully understood. Here, we conducted multiple pairwise coevolution experiments and examined how coevolution with one partner could simultaneously influence interactions with other partners. We then analyzed the underlying genetic changes that alter interactions in a large cross-experiment PBIN. The results reveal that interactions at the cell surface as well as intracellular interactions can alter patterns across the PBIN. When phages utilized the same set of host cell-surface receptor genes, pairwise coevolution with one partner did alter interactions with other partners. In contrast, if different sets of host cell-surface receptor genes were used, modular patterns in the cross-experiment PBIN were generally observed. On the phage side, resistance-breaking mutations arose in many putative receptor-binding proteins, and these changes could alter interactions with other hosts. However, in both the hosts and phages, over half of the mutated genes were not associated with cell-surface interactions. Host mutations likely altering intracellular phage-host interactions, including translation, protein stability, and DNA replication often led to broad resistance across the PBIN. In phages, mutations occurring in genes encoding endolysins, DNA methylases, oxygenases, transcriptional regulators, and tRNAs were observed potentially also influencing intracellular interactions. Based on these results, we propose a model to illustrate how these two different types of genetic signatures (i.e. extracellular cell-surface interactions verses intracellular interactions) can impact the structure of PBINs.

Fucoidan is the second most abundant polysaccharide in brown macroalgae, contributing up to 50% of the dissolved organic matter released daily from these algae into the ocean, substantially influencing the marine carbon cycle. Due to its complex chemical structure, fucoidan is highly resistant to microbial degradation and accumulates on the surface of macroalgae, where it contributes to regulating their microbiome. Here, we used a range of microfluidic assays to quantify the chemotactic and physiological responses of marine bacteria to fucoidan. Our results reveal that all the strains tested (14 out of 14) are attracted to fucoidan produced by two different species of brown macroalgae. Attraction occurred only at high fucoidan concentrations (0.1 to 1 mg/mL), such as those expected in the immediate vicinity of algal surfaces, whereas low concentrations elicited no response or modest chemorepulsion. Although fucoidan did not support bacterial growth under our experimental conditions, it did not impair the utilization of other carbon sources. Together, these findings suggest that fucoidan gradients can act as chemical cues guiding marine bacteria toward the nutrient-rich microenvironment of macroalgal surfaces, pointing to a potential ecological role of fucoidan in shaping macroalgal microbiota, bacterial behavior, and ultimately carbon cycling in the ocean.

RNA-based therapeutics increasingly rely on nuclease-resistant xeno-nucleic acids (XNAs), but engineering polymerases capable of reading and amplifying these non-canonical substrates remains a major challenge. Natural DNA polymerases cannot process C2' sugar modifications, and the complete structural determinants governing this limitation remain poorly understood, limiting rational design strategies. In this study, we apply directed evolution methods to Thermus thermophilus DNA polymerase using a microfluidic implementation of the Compartmentalized Self-Replication (CSR). We identify an engineered variant, termed E5, capable of reverse transcribing C2'-modified RNA, a previously inaccessible activity for this enzyme. Next-generation sequencing allow us to track evolutionary trajectories across nine rounds of evolution. Through an integrated analysis combining statistical methods, protein language models, and crystallographic data, we reveal unknown epistatic networks controlling polymerase function. This large-scale experimental dataset (>196,000 thermostable enzyme variants) provides a robust basis for predicting sequence-to-function relationships, accelerating development of enzymes for aptamer discovery and synthetic biology applications.

Bacterial small-molecule messengers that function in diverse immune signaling pathways have been thought to derive strictly from biosynthesis using nucleotide and cofactor substrates. Here we report a nuclease-driven immune signaling pathway in which tRNA cleavage generates a specific small molecule that triggers cell death in response to RNA-guided transcript recognition in prokaryotes. We identify a class of type VI-B CRISPR systems in which the RNA-guided nuclease Cas13 and its accessory transmembrane protein, 2TMβ, are co-encoded in an operon and are jointly required for bacteriophage defense. Upon target RNA recognition, Cas13 activates 2TMβ by producing mono- and dinucleotide messengers bearing 2′,3′-cyclic uridine phosphate ends by tRNA cleavage. Activated 2TMβ destroys infected cells by forming a multimeric complex that disrupts cell membranes to mediate cell death. These findings demonstrate RNA-programmed tRNA cleavage for second-messenger biogenesis, revealing a new logic for prokaryotic immune signaling.

Local alignment tools struggle at querying against thousands of bacterial genomes, because the reference indexes are memory-intensive and base-level dynamic programming is slow. We present alamem (https://github.com/yunwilliamyu/alamem), an approximate local aligner that streams unprocessed reference databases and estimates average nucleotide identity (ANI) from Maximal-Exact-Match (MEM) chaining statistics. Alamem can query a bacterial genome against >85,000 prokaryotic genomes in under 30 seconds (with multithreading) using 4 GB of memory, making it over 16x faster and 21x more memory-efficient than existing local aligners using their default running modes. On top of alamem, we built alasight (https://github.com/graceoualline/alasight) for detecting signatures of horizontal gene transfer (HGT) by finding pairs of alamem hits to divergent genomes. Alasight achieves over an order of magnitude better sensitivity on data from real bovid microbiomes than existing mobile genetic element-detection software and provides depth and sparsity information supporting each putative HGT region.