Microbiological Analysis
Within the framework of the "MAGIC Service Centre" project, Next Generation Sequencing (NGS) techniques applied to the assessment of biodiversity and the distribution of environmental microflora in the conservation of historical, artistic, and library heritage represent an essential tool for the diagnostic analysis and preservation of ancient volumes. These techniques enable an innovative approach that avoids invasive methods that could potentially damage the artefacts.
This type of analysis can provide crucial information on the biological agents involved in deterioration, including the activity of fungi, bacteria, and other microorganisms that may compromise the preservation of materials, thereby making it possible to plan corrective actions based on the reconstruction of sources and pathways of environmental contamination. The object of the analysis was the corpus of incunabula and sixteenth-century books from the Torraca Collection, preserved at the Library of the Accademia Pontaniana in Naples.
Preliminary Study and Protocol Development
A review of the scientific literature was conducted in order to identify and establish the most appropriate methodologies for DNA sampling, extraction, and analysis. Specific genetic regions were selected for the characterisation of bacteria (16S rRNA) and fungi (ITS).
Sampling of the Volumes
Six sampling cycles were carried out on incunabula and sixteenth-century books, preceded by a visual assessment of the main signs of deterioration, including stains, mould, holes, and yellowing. Three different sampling substrates were compared: nitrocellulose, nylon, and Whatman paper. Nitrocellulose showed the best performance in terms of the quality of the genetic material collected.
DNA Extraction and Assessment
The DNA extracted from the samples underwent purification procedures based on phenol-chloroform extraction, followed by optimisation of the protocol to improve the yield and quality of the genetic material. The concentration and purity of the samples were assessed by spectrophotometric analysis.
PCR Amplification
The extracted DNA was amplified by PCR using specific primers targeting the bacterial 16S rRNA region and the fungal ITS region. Following initially weak amplification, the experimental conditions were optimised and a nested PCR approach was introduced, which made it possible to obtain sufficiently concentrated and specific products for subsequent analyses.
Sequencing and Identification
The amplified products were purified and subjected to sequencing. The sequences obtained were compared with reference databases using BLAST, allowing the main microbial species present in the samples to be identified.
Interpretation of the Results
The analysis revealed the presence of fungal species belonging to the genus Vishniacozyma and bacteria belonging to the genera Romboutsia and Clostridium, as well as Peptostreptococcaceae bacterium.
The microbial profile identified, characterised by environmental basidiomycetous yeasts (Vishniacozyma victoriae, V. carnescens) and anaerobic/spore-forming bacteria (Peptostreptococcaceae bacterium, Romboutsia sp., Clostridium sp.), is not indicative of active biodeterioration, but rather describes a microbial ecosystem compatible with long-term storage in cold, relatively undisturbed, and non-air-conditioned environments. The fungi and bacteria detected do not currently pose a direct threat to the preservation of the incunabula, but they indicate that such a risk could arise if this equilibrium were disrupted.
A more effective management of the microclimate is therefore advisable in order to prevent rapid fluctuations and maintain stable temperature and relative humidity levels. In addition, dust management and periodic, non-invasive cleaning should be implemented in a protected yet adequately ventilated environment.
Microbiome analysis, even when it does not reveal an emergency situation, is a fundamental tool as an early risk indicator. It can help distinguish historical microbial colonisation from more recent colonisation and contribute to predicting how and when the associated risk may increase.