Enzymes derived from extremophilic organisms, also known as extremozymes, offer sustainable and efficient solutions for industrial applications. Valued for their resilience and low environmental impact, extremozymes have found use as catalysts in various processes, ranging from dairy production to pharmaceutical manufacturing. However, discovery of novel extremozymes is often hindered by challenges such as culturing difficulties, underrepresentation of extreme environments in reference databases, and limitations of traditional sequence-based screening methods. In this work, we present a computational pipeline designed to discover novel enzymes from metagenomic data derived from extreme environments. This pipeline represents a versatile and sustainable approach that promotes reuse and recycling of existing datasets and minimises the need for additional environmental sampling. In its core, the algorithm integrates both traditional bioinformatic techniques and recent advances in structural prediction, enabling rapid and accurate identification of enzymes. However, due to its design, the algorithm relies heavily on existing databases, which can limit its effectiveness in situations where reference data is scarce or when encountering novel protein families. As a proof-of-concept, we applied the pipeline to metagenomic data from deep-sea hydrothermal vents, with a focus on β-galactosidases. The pipeline identified 11 potential candidate proteins, out of which 10 showed in vitro activity. One of the selected enzymes, βGal_UW07, showed strong potential for industrial applications. The enzyme exhibited optimal activity at 70 °C and was exceptionally resistant to high pH and the presence of metal ions and reducing agents. Overall, our results indicate that the pipeline is highly accurate and can play a key role in sustainable bioprospecting, leveraging existing metagenomic datasets and minimising in situ interventions in pristine regions.

Conjugative transfer of plasmids represents a major route through which antibiotic resistance genes are spread. In the case of the prevalent and deadly pathogen Staphylococcus aureus , more than 90% of clinical isolates carry at least one plasmid. While plasmid-encoded mechanisms [e.g. plasmid copy number (PCN)] can influence conjugation frequency, host factors and environmental stimuli can also affect transmission. In particular, stress responses like the stringent response have been associated with increased movement of mobile genetic elements. We have previously shown that clinical mutations in the stringent response controller, Rel, lead to elevated levels of the alarmones guanosine tetra- and pentaphosphate [(p)ppGpp] and antibiotic tolerance in S. aureus . Here, we report that elevated (p)ppGpp in these strains promotes the conjugal transfer of diverse staphylococcal resistance plasmids. We observed that clinical Rel mutations promote donation, but not receipt, of plasmids from the three families of staphylococcal plasmid and a mobilizable plasmid. This increased conjugation frequency could also be induced by chemical induction of the stringent response by mupirocin. Intriguingly, detailed experimental analysis revealed that the effect of elevated (p)ppGpp on plasmid donation was not due to CodY derepression, SOS response induction or increased PCN. Furthermore, comparative transcriptomics of wild-type and mutant donor did not highlight any putative plasmid- or host-derived mechanisms to explain this observation. Further investigations are required to explore the mechanistic link between (p)ppGpp and conjugation, given the pervasive transcriptional and post-translational effects of (p)ppGpp. Overall, the association between Rel mutation and increased plasmid donation is alarming, especially as Rel mutations are being increasingly identified among clinical isolates.