Temperate Bacillus phages use arbitrium peptides to coordinate lysis–lysogeny decisions, but whether the mature communication peptide can be sensed directly by Bacillus subtilis and affect its physiology and behavior is unknown. Here we show that the φ3T arbitrium peptide SAIRGA elicits a sequence– and stereochemistry-dependent response in Bacillus subtilis that is strongly expressed in surface-grown colony biofilms but is not accompanied by comparable changes in planktonic growth or static-liquid pellicle morphology. The response persists in the absence of AimR, the canonical arbitrium receptor. Within colonies, SAIRGA alters spatial PtapA activity and increases heat-resistant spore formation without increasing total viable cell yield. Untargeted metabolomics reveals broad dose-dependent remodeling that tracks peptide activity, while program-level proteomics independently converges on late-sporulation and mature-spore-associated states. This study highlights how a phage-derived peptide may act as a signal, enabling the host to pivot toward a survival-focused developmental state.
Integrating multi-omics data is essential for microbiome research, as microbial communities are shaped by and respond to interdependent processes, including taxonomic composition, metabolite production and utilization, and gene expression. However, accurately capturing ecosystem-wide patterns across these modalities is statistically challenging due to differences in scale, sparsity, and compositionality. While a growing number of multi-omics methods have emerged, they differ in their mathematical objectives and modeling assumptions, which in turn shape how biological patterns are represented and interpreted. This underscores the need for tools that explicitly account for the statistical properties of microbial ecosystems. Here, we present Joint Robust Principal Component Analysis (Joint-RPCA), a method designed with these statistical properties in mind and broadly applicable to multi-omics settings with similar challenges. Built on the OptSpace matrix completion framework, Joint-RPCA assumes an underlying shared low-rank structured component across modalities to identify shared variation and cross-modal associations from matched samples. Within this setting and under these statistical assumptions, Joint-RPCA showed stronger performance than the benchmarked general-purpose methods in phenotype separation and feature association tasks, achieving up to sixfold improvement in classification accuracy and over 100-fold faster runtimes. Applied to real-world datasets, including the Integrative Human Microbiome Project (iHMP), mammalian gut microbiomes, and decomposition studies, Joint-RPCA reveals replicable and interpretable multi-omic patterns, offering a scalable and domain-aware solution for systems-level microbiome analysis. Joint-RPCA is available in both Python ( https://github.com/biocore/gemelli ) and R ( https://bioconductor.org/packages/mia ).
Bianca Cordazzo Vargas, C. Martino, A. Dilmore et al.· Molecular Systems Biology· 1 citation
This review highlights recent advances in the development of sulfonyl- and sulfinyl-containing organosulfur compounds as modulators of QS and biofilm formation in clinically relevant Gram-negative pathogens.
D. Nwobodo, M. Egbujor, Samuel S. Kiprotich et al.· FEMS Microbiology Reviews· 0 citations
