Genome mining now yields tens of thousands of putative biosynthetic gene clusters (BGCs) per project, yet, separating genuinely novel candidates from rediscoveries of known compounds remains the rate-limiting step before experimental validation. Single-axis prioritization tools, antiSMASH similarity, BiG-FAM GCF distance, and self-resistance-enzyme (SRE) filters such as ARTS, each surface a different facet of evidence, yet their isolated use systematically over-ranks rediscovery-prone BGCs and overlooks genuinely orphan clusters. We present novelBGC, a web-hosted framework that converts these disparate outputs into two deliberately non-inverse continuous metrics per BGC, a Novelty (N) and a Reference Similarity (RS) score which together define a 2D decision plane that resolves rediscoveries, divergent family members, contig-edge artefacts, and uncharted chemistry with interactive visualisations, with all component weights user-tuneable at submission. Retrospective validation across three independent experimental datasets demonstrates the utility of the framework for candidate prioritization. Within the first 186-BGC SRE-guided cloning study, every confirmed bioactive product fell within the low-to-mid N band whereas 55 high-N (N ≥ 0.50) BGCs were never selected. Moreover, in the other two studies, it correctly prioritised the fully orphan lariocidin BGC of Paenibacillus sp. M2 and the divergent within-family indanopyrrole-A idp BGC of Streptomyces sp. CNX-425. Together, these case studies demonstrate that the joint (N, RS) space facilitates prioritization decisions that are difficult to achieve using any single criterion alone. from identical input data. novelBGC requires no command-line expertise, no local tool installation, and no manual integration of intermediate output formats, addressing a well-documented accessibility barrier for wet-laboratory researchers engaging with genome-mining workflows. novelBGC is freely available at https://project.iith.ac.in/sharmaglab/novelbgc/.

Poly(butylene adipate-co-terephthalate) (PBAT) is an aromatic–aliphatic copolyester widely used in packaging and consumer products. Its aromatic rings confer high resistance to hydrolysis, limiting biological degradation. To enhance PBAT biodegradation, we engineered Paracoccus denitrificans PD1222, a metabolically versatile and genetically tractable bacterium that can accumulate poly(3-hydroxybutyrate) (PHB). A novel plasmid (pV1) was constructed to express the broad-specificity cutinase FsCut under the constitutive Ptuf promoter and fused to the PorG signal peptide for extracellular secretion. Using an optimized transformation protocol, we stably transformed P. denitrificans PD1222 with pV1, enabling secretion of active FsCut and efficient PBAT hydrolysis. In degradation assays, the engineered strain exhibited significantly higher depolymerization rates than the strain carrying the pV0 vector, used as a control. Furthermore, the FsCut-expressing strain accumulated 1.16-fold more PHB than the pV0 strain and exhibited degradation rates of PBAT 2.27-fold higher in enriched medium and 1.9-fold higher in defined mineral medium. These findings demonstrate that targeted expression of a secreted cutinase substantially improves PBAT degradation by P. denitrificans, supporting its potential as a microbial platform for plastic bioremediation.

When E. coli ribosomes are assembled in vitro, manipulation of incubation temperature and magnesium ion concentration has been an essential procedure, which is a crucial step for the assembly of active large subunits. The present study tackles this issue to develop a single-step procedure, which can be performed in near-physiological conditions, where cell-free protein synthesis is active. We found that GTPase factors EngA and ObgE can complement the changes in temperature and magnesium ion concentrations. In the presence of these factors, both the ribosome assembly and translation processes were successfully integrated in the reconstituted cell-free protein synthesis system. Furthermore, we found that these GTPase factors can reassemble the ribosomes to an active state, whose structure was disrupted by EDTA chelation of magnesium ions, indicating that these two factors can reversibly induce the ribosome structure to an intact state. The findings are essential for the bottom-up construction of synthetic cells.

Corynebacterium glutamicum is a crucial food-grade (GRAS) bacterial chassis widely utilized for the industrial production of amino acids and nutraceuticals. However, the efficient production of recombinant proteins and secondary metabolites in this host remains limited by context dependence and low translational efficiency. To overcome this, we introduce a 5′-end translationalization strategy. By repurposing passive 5′ untranslated regions (5′UTRs) into actively translated fore-cistrons, we converted conventional monocistronic designs into context-independent, leaderless polycistronic designs (PCDs). This assembly of concatenated fore-cistrons functions as a translational amplifier, largely decoupling protein output from mRNA abundance. We validated this platform by optimizing two biomanufacturing paradigms: achieving a 4.07-fold enhanced secretion of OmlA, a porcine vaccine antigen, and boosting biosynthesis of the food-grade pigment indigoidine to 1.20 g/L (a 7.33-fold increase over baselines). Together, this framework establishes a versatile, portable toolkit to overcome translational bottlenecks, enabling robust hyperproduction of recombinant proteins and engineered metabolites in biotechnology.

Streptomyces species are well known for their immense genomic potential for the discovery of natural products. However, the majority of their biosynthetic gene clusters (BGCs) remain silent or are poorly expressed under laboratory conditions. This silence is largely attributed to the regulatory complexity encoded within their large genomes, which feature thousands of regulators and multilayered control systems. In this review, we summarize the current knowledge regarding genome-scale transcriptional regulation in Streptomyces, alongside the emerging experimental platforms designed to investigate these mechanisms. By integrating and comparing fragmented data on regulatory architectures, we highlight the extensive hierarchical, combinatorial, and condition-dependent regulatory networks that govern secondary metabolite biosynthesis in Streptomyces. Furthermore, integrative analyses reveal the conservation of regulatory architectures across Streptomyces species, facilitating the translation of findings from model organisms to BGC discovery, activation, and engineering in less studied species. Beyond transcription, we also discuss the additional regulatory layers, including post-transcriptional, translational, post-translational, and chromosome topology-based controls, and their practical applications in natural product research. Collectively, this review reframes the complex transcriptional regulatory networks not as a bottleneck but as a central principle for understanding and exploiting the biosynthetic potential of Streptomyces.

Temperature is a fundamental determinant of bacterial physiology and ecology. Optimal growth temperature (OGT) is highly variable across species, contributing to differences in where and when species are most likely to thrive. Although the OGTs for most bacteria remain unknown, the increasing availability of genomes from uncultivated and cultivated taxa has made it advantageous to build genomic, cultivation-independent models to infer OGT. However, pre-existing genomic models often lack the generalizability and mechanistic grounding required for robust inferences of OGT. We propose a novel framework for predicting bacterial OGT which uses learned protein structural signatures of thermal adaptation. We hypothesize that biophysical tradeoffs which dictate enzymatic functions across variable temperatures provide a more robust empirical basis for OGT prediction than broad genomic features. Our OGT-predicting model, ROSEATE, is based on a single gene, adenylate kinase (ADK), that encodes for a ubiquitous enzyme essential for energy homeostasis. ROSEATE uses high-dimensional latent space encoding via MSA Transformer, a protein language model which embeds ADKs in a manner which preserves biophysical information about embedded proteins. We show that the accuracy of the ROSEATE model is on par with other genome-based models, has a high degree of phylogenetic generalizability, and the ESM embeddings effectively capture key temperature-adaptive enzyme characteristics derived from AlphaFold structures. Because ROSEATE is based on analyses of a single ubiquitous protein, it can be used with metagenomic data to infer the community-level variation in bacterial OGTs. We demonstrate this feature of ROSEATE by reconstructing ADK sequences from over 500 environmental and host-associated metagenomes, successfully distinguishing community-wide thermal preferences across diverse habitats, from polar oceans to mammalian guts. By transitioning from genomic proxies to informationally dense protein structural features, this work provides an efficient, interpretable tool for predicting bacterial OGTs across taxa and whole communities.

When growing bacteria start to reach stationary phase, the nucleotide guanosine tetra-phosphate (ppGpp) accumulates intracellularly and regulates the transition of cells from growth to growth arrest. Because commonly studied bacteria remain viable in stationary phase only briefly under laboratory conditions, the role of ppGpp in sustaining long-term bacterial survival after growth arrest has not been widely studied. Rhodopseudomonas palustris strain CGA009 is a phototrophic alpha-proteobacterium that survives under anaerobic conditions for months when not growing due to carbon starvation if provided light. When we quantified intracellular ppGpp in growing and growth-arrested R. palustris, we found that it was undetectable in growing cells but accumulated to about 100 µM when cells ran out of carbon and entered the stationary phase. These elevated levels of ppGpp were maintained over a 60-day period of growth arrest. Intracellular GTP was 100–200 µM in growth-arrested cells, and ATP was at 2–4 mM. ppGpp had global effects on gene expression, with over half of the genes in the R. palustris genome being activated or repressed by ppGpp in stationary phase cells. These results suggest that, in addition to its known role in facilitating the transition of bacteria from growth to stationary phase and accompanying stress responses, ppGpp is important for prolonging bacterial survival in stationary phase.

Mirror-image peptides and proteins are attracting interest as therapeutics, as key building blocks for constructing mirror-image life, and as tools to probe the origin of life. Their resistance to proteolytic degradation and unique stereochemistry make D-peptides/proteins particularly appealing for biomedical applications, yet a critical unresolved question is how their intracellular uptake compares to that of natural L-forms. To address this, we systematically investigated the role of cargo chirality in cellular internalization while maintaining a constant delivery vehicle. Three model cargos of increasing size and structural complexity were synthesized in both L- and D-configurations and conjugated to an identical cyclic deca-arginine (cR10) cell-penetrating peptide (CPP). By keeping the CPP scaffold constant, we reduced delivery-related variability and directly assessed the influence of the cargo chirality on uptake. Quantitative uptake analysis using flow cytometry, gel analysis, and confocal microscopy across multiple mammalian cell lines reveals that L-cargos are internalized more efficiently than their mirror image D-counterparts, demonstrating that cargo chirality is a key determinant of uptake efficiency across the chiral biological membrane. Collectively, these findings provide a systematic basis for further exploration of chirality effects in CPP-mediated delivery and may inform the design of mirror image peptides and proteins for therapeutic or synthetic biology applications.

Lactobacillus species are renowned for their probiotic properties and niche adaptability, driven by unique genomic traits, stress-response mechanisms, and biofilm formation. This versatility makes them exceptional candidates for advanced biotechnological applications. Their biocompatibility and immunomodulatory effects allow them to serve as live biotherapeutic products. Through genetic engineering and encapsulation, Lactobacillus can be programmed to deliver recombinant proteins and vaccines, cytokines and anti-inflammatory molecules, targeted enzymes, and peptides. Beyond therapy, these bacteria can be engineered into biosensors to detect pathogens, toxins, and clinical biomarkers. By integrating CRISPR-Cas systems and reporter genes into whole‑cell or cell‑free platforms, they offer robust solutions for food safety, environmental monitoring, and diagnostics. While challenges in stability and regulation persist, advancements in synthetic biology are transforming Lactobacillusfrom a simple probiotic into a precise, multifunctional tool for improving global health and environmental oversight.