Cultural heritage objects constitute a very diverse environment, inhabited by various bacteria and fungi. The impact of these microorganisms on the degradation of artworks is undeniable, but at the same time, some of them may be applied for the efficient biotreatment of cultural heritage assets. Interventions with microorganisms have been proven to be useful in restoration of artworks, when classical chemical and mechanical methods fail or produce poor or short-term effects. The path to understanding the impact of microbes on historical objects relies mostly on multidisciplinary approaches, combining novel meta-omic technologies with classical cultivation experiments, and physico-chemical characterization of artworks. In particular, the development of metabolomic- and metatranscriptomic-based analyses associated with metagenomic studies may significantly increase our understanding of the microbial processes occurring on different materials and under various environmental conditions. Moreover, the progress in environmental microbiology and biotechnology may enable more effective application of microorganisms in the biotreatment of historical objects, creating an alternative to highly invasive chemical and mechanical methods.

There is no standardized protocol for the assessment of microbial air contamination in museums and other cultural heritage sites. Therefore, most museums conduct such assessments based on their own guidelines or good practices. Usually, microbial air contamination is assessed using only classical microbiology methods with the application of a single growth medium. Therefore, this medium should be carefully selected to limit any selective cultivation bias. Metabarcoding, i.e., a next-generation sequencing (NGS)-based method, combined with classical microbiological culturing was used to assess the effectiveness of various media applications in microbiological screening at the Museum of King John III’s Palace at Wilanow (Warsaw, Poland). The obtained results indicated that when using a classical microbiology approach to assess the microbial air contamination at the museum, the selection of a proper growth medium was critical. It was shown that the use of rich media (commonly applied by museum conservators) introduced significant bias by severely underreporting putative human pathogens and the bacterial species involved in biodeterioration. Therefore, we recommend the use of other media, such as Frazier or Reasoner’s 2A (R2A) medium, as they could yield more diverse communities and recovered the highest number of genera containing human pathogens, which may be suitable for public health assessments.

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.

Heavy metal pollution poses a major environmental challenge, with microbial resistance to heavy metals offering potential solutions through bioremediation. Additionally, the presence and diversity of microbial metal resistance genes (MMRGs) could contribute to an ecosystem’s ability to adapt and recover from heavy metal contamination by maintaining essential microbial functions and promoting the cycling of nutrients under stress conditions. Thus, MMRGs may serve not only as markers of contamination but also as indicators of an ecosystem’s self-purification capacity and resilience to environmental disturbances. Here we present MetHMMDB, a database containing 254 profile Hidden Markov Models representing 121 MMRGs. Unlike traditional sequence-based resources, MetHMMDB relies on HMMs to improve detection sensitivity and functional specificity across microbial communities. Created through iterative database searches, sequence clustering, structural prediction, and manual annotation, MetHMMDB emphasizes functional annotation rather than gene classification. The database outperforms sequence-based approaches, identifying over twice as many MMRGs in metagenomic datasets, including those from extreme environments. Analysis of agricultural soil revealed distinct resistance profiles correlating with soil quality. MetHMMDB advances our understanding of microbial adaptation to heavy metal contamination while supporting environmental management strategies through improved identification and characterization of metal resistance mechanisms. Database URL: https://github.com/Haelmorn/MetHMMDB .

Plasmids are one of the key drivers of microbial adaptation and evolution. However, their diversity and role in adaptation, especially in extreme environments, remains largely unexplored. In this study, we aimed to identify, characterize and compare plasmid sequences originating from samples collected from deep-sea hydrothermal vents located in Arctic Mid-Ocean Ridges. To achieve this, we employed, and benchmarked three recently developed plasmid identification tools - PlasX, GeNomad and PLASMe – on metagenomic data from this unique ecosystem. To date, this is the first direct comparison of these computational methods in the context of data from extreme environments. Upon recovery of plasmid contigs, we performed a multi-approach analysis, focusing on identifying taxonomic and functional biases within datasets originating from each tool. Next, we implemented a majority voting system to identify high-confidence plasmid contigs, enhancing the reliability of our findings. By analyzing the consensus plasmid sequences, we gained insights into their diversity, ecological roles, and adaptive significance. Within the high-confidence sequences, we identified a high abundance of Pseudomonadota and Campylobacterota, as well as multiple toxin-antitoxin systems. Our findings ensure a deeper understanding of how plasmids contribute to shaping microbial communities living under extreme conditions of hydrothermal vents, potentially uncovering novel adaptive mechanisms.