Lignin depolymerization generates mixtures of aromatic compounds that are promising carbon sources for microbial bioconversion, yet the constraints governing microbial growth on these substrates remain unclear. Here, we investigated how Rhodopseudomonas palustris CGA009 organizes metabolism to grow on chemically distinct lignin-derived aromatics under aerobic and anaerobic conditions. Quantitative proteomics across 14 substrate-oxygen environments revealed extensive oxygen-dependent proteome remodeling, consistent with shifts between respiratory and photoheterotrophic programs. Despite this reorganization, predictive modeling showed that growth rate is encoded by a relatively small subset of proteins whose abundance tracks physiological performance across environments. Using CorePredX, a machine-learning framework that combines global importance analysis with dependence-aware redundancy filtering, we identified 118 high-confidence proteins whose variation made conditionally non-redundant contributions to growth-rate prediction among 1,857 quantified proteins. These proteins are organized into a hierarchical architecture comprising a cross-condition core, adaptive regulators, substrate-specific specialists, and conditional hubs. The cross-condition core linked translational capacity, sulfur assimilation, redox buffering, and carbon storage cycling, highlighting conserved proteomic features associated with growth across oxygen regimes and substrate chemistries. Notably, an uncharacterized cystathionine beta-synthase domain protein, RPA3416, emerged as a strong predictive component of this core, raising the hypothesis of an adenylate-responsive process associated with aerobic growth on lignin-derived aromatics. Together, these results suggest that the metabolic versatility of R. palustris arises from flexible regulation layered onto partially conserved proteome-encoded growth-associated constraints, providing candidate targets for lignin bioconversion engineering and metabolic model refinement.

Biosynthetic gene clusters (BGCs) encode microbial natural products, many of which have important ecological and biomedical roles. Genome mining tools enable large-scale BGC prediction, but their outputs differ substantially, complicating comparison and interpretation. We present BGC-QUAST, a framework for evaluating and comparing BGC predictions across three analysis modes: comparison across samples, assessment of BGC recovery in draft assemblies relative to reference genomes, and comparison of predictions from different tools using overlap analysis. BGC-QUAST provides standardized metrics, interactive visualizations, and integrated outputs for joint inspection of predictions, enabling the comprehensive comparison of genome mining results and facilitating sample prioritization based on biosynthetic potential.  https://github.com/gurevichlab/bgc-quast

Fluorescent biosensors that report protein conformation in vivo have been invaluable for understanding how the spatio-temporal dynamics of signaling controls cells. However, for GTPases these biosensors report the activated conformation using reagents that block the binding of downstream proteins, generating dominant negative effects and altering normal cell physiology. We present here a generalizable design to make GTPase biosensors (AlloRac1 and AlloCdc42), in which a circularly permuted fluorescent protein is inserted into a conserved loop allosterically connected to the effector binding site, generating activity-dependent fluorescence without blocking ligand interactions. The Rac1 biosensor showed that effector interactions led to increased Rac1 activation, indicating an auto-regulatory positive feedback made visible by the new biosensor design. This feedback regulated the kinetics and localization of Rac1 activity, including Rac1 activity gradients that controlled motility. Feedback was generated through Rac1 interaction with the effector Pak1, which led to further activation of Rac1 by the guanine exchange factor β-Pix. The new biosensor approach enables quantitative imaging of previously obscure spatio-temporal dynamics in GTPase regulation.

Bacteria display an incredible genetic plasticity, which enables them to adapt to various environmental stressors, such as antibiotic compounds that may imperil their existence. Bacterial resistance mechanisms include degradative enzymes, inactivation of antibiotics, antibacterial target site mutation, change in target, altered cell wall permeability to antibiotics, bypass of metabolic pathways, and efflux pumping of antibiotics across the cell membrane. These mechanisms are encoded by genomic changes ranging from point mutation via genetic elements assembly to horizontal transfer of genes from the environment. Antibiotic resistance in bacteria can be inherited or acquired. Antibacterial resistance genes may accumulate mobile elements, leading to multi-drug-resistant phenotypic transfer via a single genetic event. The resistance to antibiotics has been frequently increasing in clinical settings, which drives scientists to research alternative antibacterial medicines to prevent the growth and spread of drug-resistant bacteria. Technological advancements and the discovery of innovative drug moieties with targeting potential have led to the development of novel drug compounds with diverse therapeutic properties. This includes alternative cellular, physiological, and metabolic patterns of bacteria that may be potential pharmacological targets for the next generation of antibiotics. It is beneficial to characterize antibiotic resistance genotypes and phenotypes causing antibiotic bacterial resistance. An understanding of mechanisms that lead to the development and spread of antibiotic resistance will help clinicians in making appropriate decisions regarding antibiotic usage in a wide range of circumstances. The current review has highlighted the mechanism of drug resistance in bacteria, and has enlisted the antibiotic resistance genes (ARGs) and their importance in aggravating the resistance phenomenon.

Engineered protein chimeras enable new biological functions but remain difficult to design due to context-dependent constraints on insertion tolerance and the need to preserve host protein function. Here, we report CRISPR-guided protospacer adjacent motif (PAM) scanning in yeast to map chimera-permissive sites in living cells. We apply this approach to peptide and reporter insertions. In the first application, we generated 91 insertion chimeras encoding a defined protease cleavage sequence across six components of a model G protein-coupled receptor (GPCR) signaling pathway. Sixty-three percent of sites retain signaling, identifying positions that preserve host function and reveal broad, position-dependent tolerance. Coupling insertional scanning with cognate proteases enables site-resolved mapping of in-cell accessibility, distinguishing protected and exposed regions and defining EX1- and EX2-like regimes. These chimeras are responsive to proteolysis and pharmacological inhibition, enabling reversible control of protein activity. In a second application, we scanned 32 positions in yeast Ste2 and human A2A and MTNR1A receptors to engineer bi-functional chimeras that retain native function while incorporating reporter activity. Together, these results establish PAM scanning as a scalable, protein-agnostic framework for mapping insertion tolerance, interrogating protein accessibility in vivo, and enabling scalable ground-truth benchmarking of predictive chimera engineering.

Bacteria produce high-affinity, iron-chelating secondary metabolites called siderophores to access insoluble Fe(III) in their environments. Genome mining has revealed many predicted siderophore biosynthetic gene clusters (BGCs) in bacterial genomes; however, the structures of their siderophore products remain mostly undetermined. This limits our molecular-level understanding of how bacteria acquire iron. Here, we apply inverse stable isotope labeling (InverSIL) to rapidly connect predicted siderophore BGCs to their products. With InverSIL, bacteria are grown on 13C-substituted carbon sources and then fed predicted biosynthetic precursors at their natural isotopic abundance to identify BGC products by mass spectrometry, removing issues with the availability of isotopically substituted precursors. We use InverSIL to determine the structures of the siderophore products of predicted BGCs from the methylotrophic genera Methylophilus and Methylorubrum, as well as the siderophores produced by the opportunistic pathogen Chromobacterium violaceum, which were previously shown to be essential for virulence yet remained structurally uncharacterized. We next use this approach to reveal the unexpected production of enterobactin by the genera Kushneria and Paracoccus, which was difficult to predict from genome sequences due to the distributed nature of the biosynthetic genes within the genomes. Finally, we use InverSIL to discover new siderophores, the cellulochelins, from the cellulose-degrading plant symbiont Cellulomonas sp. strain Leaf334. These findings demonstrate the utility of InverSIL for functional BGC characterization and expand our molecular understanding of bacterial iron acquisition strategies.

Aromatic compounds are ubiquitous arising from natural sources as well as anthropogenic activities posing significant ecological and health risks due to their persistence and toxicity in nature. While bacterial biodegradation of these compounds offers a sustainable strategy, its success usually hinges on integrated phenotypes that are beyond mere catabolic pathways. Phenotype involves multiple processes like sensing pollutants, chemotaxis, transport, membrane adaptation, stress tolerance, regulation at molecular level, and community co-operation. Bacteria sense aromatics via specialized chemoreceptors, triggering metabolism-dependent or independent chemotaxis. Partitioning of hydrophobic pollutants into membranes is countered by membrane modifications and efflux pumps. While facilitated uptake occurs using biosurfactants and specific transporters. Some bacteria exhibit unique carbon-source utilization hierarchies that prioritize aromatics over other carbon sources or co-metabolize, subverting canonical catabolite repression leading to niche dominance. Biofilm formation, cross-feeding and division of labor enhance resilience in bacterial communities. Understanding and integrating these sensing, chemotactic, adaptive and metabolic capabilities are crucial for the rational engineering of bacteria for effective remediation of contaminated sites.