Plants produce antibiotic substances and bacteria need to cope with those substances to colonize plants. We analyzed the inventory of genes encoding resistance-nodulation-cell division (RND) efflux pumps originating from 282 bacterial isolates from leaves or roots of the model plant Arabidopsis thaliana. We confirmed that, on average, plant associated bacteria hold a significantly increased repertoire of genes encoding RND antiporters homologs compared to strains isolated from other ecological niches, as reported in a previous study. While some RND antiporter clades were enriched in plant colonizers, other clades found in the genomes of isolates from other environments were underrepresented. An in-depth analysis of RND antiporters from plant colonizing bacteria revealed that conserved motifs can be found in each clade, possibly contributing to substrate specificity. Interestingly, we found horizontal gene transfer markers in 10% of the antiporter homologs, suggesting that horizontal gene transfer may significantly contribute to the adaptation of bacteria to the specific chemical environment created by different organs of plant hosts. In addition, homologs from leaf-isolated bacteria showed a lower diversification, and harbored markers of horizontal gene transfer in the heavy metal exporting clade. Sequence and structural analysis revealed a high diversity in RND-antiporters, with few residues under purifying selection, indicating that RND diversity is driven by random mutations. Our findings have major implications for the origin of multidrug resistances and for our understanding of the forces shaping the outcome of plant-microbe ecology in general.

Understanding and predicting bacterial substrate preferences has broad utility from microbial interactions to selecting prebiotics. Isolate exometabolite profiling directly measures which compounds a given microbe utilizes from an array of metabolites in the environment. However, modeling, mining, and integrating these data are challenging. Here, we introduce a Bayesian Personalized Ranking (BPR) model applied to substrate preferences which we find learns to rank compounds by a given microbial preference. It was found to outperform the other ranking models (AUC = 0.93), proved robust to ablation, showed strong within-genus isolate pairs correlation (Spearman rank = 0.78) and predictive ability for new data. BPR was then used to create the Web of Microbes (WoM) Agent by integrating it with the Phydon growth model and Large Language Model (LLM) for autonomous orchestration tool calling and analysis. The WoM Agent accurately predicted substrate consumption by existing strain grown on a novel medium and correctly identified bacteria enriched in soil metabolite spike-in experiments. Additionally, the WoM Agent can use autonomous reasoning including to predict substrates that will selectively promote the growth of one clade of bacteria over another including helping interpret results and suggest new hypotheses and experiments. We anticipate broad applications in microbial cultivation, microbiome engineering, and environmental microbiology and that these capabilities will be extensible through the integration of additional tools and use of rapidly improving LLMs.

GenomeDepot is an open-source web-based platform for annotation, management, and comparative analysis of microbial genomic sequences and associated data including ortholog families, protein domains, operons, regulatory interactions, strain taxonomy, and sample metadata. GenomeDepot supports rapid creation of websites for user-defined genome collections that include bioinformatic tools for interactive genome browsing, Basic Local Alignment Search Tool (BLAST) search, annotation search, comparative genomic neighborhood visualization, and sequence download. Gene function annotations are generated by a customizable annotation pipeline. The pipeline runs annotation tools in Conda environments and can be easily extended with additional user-specified tools.

Conjugative plasmids drive bacterial evolution and antibiotic resistance spread, yet their gene expression must be silenced to protect the host. A histone-like protein H-NS represses many mobile and sedentary xenogenes but fails to silence the conjugal transfer vir operon of R6K, a prototype IncX plasmid. Instead, R6K encodes its own H-NS homolog, Sfx, to repress the vir operon. Here, we show that, unlike other plasmid silencers that target promoters, Sfx cooperates with Rho factor to arrest transcription elongation. ChIP-seq reveals that despite sharing similar DNA motifs and a preference for negative supercoiling, Sfx and H-NS occupy distinct niches: Sfx binds weakly to the chromosome but is enriched on the R6K vir operon, from which H-NS is excluded. We hypothesize that this selective targeting is mediated by Sfx-vir interactions and phase separation. We show that Sfx binding to vir DNA critically depends on DNA topology but not on the target location. Our results suggest that Sfx phase separates with R6K to ensure its preferential recruitment to the plasmid DNA and forms stable nucleoprotein filaments that are impermeable to competitors. These findings reveal how histone-like proteins can partition the genome into distinct regulatory niches, a strategy likely mirrored across all life. 

Biofilm communities exhibit emergent properties that exceed the sum of contributions from individual members of the community. Here, we describe a multilayered metabolic interaction that drives enhanced biofilm formation among three bacterial species from the plant rhizosphere. Comparative metatranscriptomic and metabolomic analyses reveal that Bacillus velezensis-secreted 5-aminovaleric acid promotes the growth of the other community members, Burkholderia contaminans and Acinetobacter baumannii. In return, B. contaminans supplies branched-chain amino acids for B. velezensis. Branched-chain amino acids and cell-cell signaling acyl-homoserine lactones from B. contaminans induce biosynthesis of the siderophore bacillibactin in B. velezensis, that is further enhanced by A. baumannii. In exchange, the B. velezensis-secreted siderophore promotes the growth of B. contaminans in iron-limited conditions, which benefits the multispecies biofilm community in vitro and promotes plant growth performance in iron-depleted soil. Our study reveals the molecular mechanisms underlying an emergent rhizosphere biofilm community function and demonstrates its importance in plant-microbe interactions.

Antimicrobial resistance (AMR) is accelerating worldwide, undermining frontline antibiotics and making the need for novel agents more urgent than ever. Antimicrobial peptides (AMP) are promising therapeutics against multidrug-resistant pathogens, as they are less prone to inducing resistance. However, current AMP prediction approaches often treat sequence and structure in isolation and at a single scale, leading to mediocre performance. Here, we propose MultiAMP, a framework that integrates multi-level information for predicting AMPs. The model captures evolutionary and contextual information from sequences alongside global and fine-grained information from structures, synergistically combining these features to enhance predictive power. MultiAMP achieves state-of-the-art performance, outperforming existing methods by over 10\% in MCC when identifying distant AMPs sharing less than 40\% sequence identity with known AMPs. To discover novel AMPs, we applied MultiAMP to marine organism data, discovering 484 high-confidence peptides with sequences that are highly divergent from known AMPs. Notably, MultiAMP accurately recognizes various structural types of peptides. In addition, our approach reveals functional patterns of AMPs, providing interpretable insights into their mechanisms. Building on these findings, we employed a gradient-based strategy and achieved the design of AMPs with specific motifs. We believe MultiAMP empowers both the rational discovery and mechanistic understanding of AMPs, facilitating future experimental validation and therapeutic design. The codebase is available at \url{https://github.com/jiayili11/multi-amp}.

Transposons are a convenient vehicle to insert DNA into a bacterial genome, but widely-used transposons such as Tn7 are not able to hit custom targets. Recently, CRISPR RNA-guided integrases have been shown to direct the insertion of a mini transposon to a chosen site using a short guide sequence. We adapted this system for the widespread plant-associated genus Sphingomonas, revealing flexibilities and limitations of the tool. We uniquely tagged five genetically diverse strains, both in neutral sites and in sites with phenotypic consequences, and we demonstrate the utility of the tags for quantitative strain tracking in complex bacterial populations. Although we initially focused on the attTn7 site recognized by Tn7 as an insertion site with minimal fitness effects, we discovered via a genus-wide search that this site disrupts potentially important genes in some strains. Therefore we validated an improved site for benign integration in Sphingomonas and used a construct with guides targeting this site to transform a heterogeneous Sphingomonas population, bypassing prior strain isolation. Using a novel rapid and economical transposon mapping method, we were able to identify correctly-tagged primary transformant colonies with novel genetic content, thus demonstrating a short cut towards the establishment of diverse tagged synthetic communities for the experimental study of bacterial natural variation.