The tailoring of the skin microbiome is challenging and is a research hotspot in the pathogenesis of immune-mediated inflammatory skin diseases such as acne. Commonly encountered preservatives used as functional ingredients have an impact on the skin microbiota and are known to inhibit the survival of skin commensal bacteria. The selected species is Lactiplantibacillus plantarum, formulated with natural enhancers for topical use (SkinDuo^TM). Ex vivo human skin models were used as a test system to assess the strain viability which was then validated on healthy volunteers. SkinDuo^TM showed increased viability over time for in vitro skin models and a stable viability of over 50% on healthy skin. The strain was tested on human primary sebocytes obtained from sebaceous gland rich areas of facial skin and inoculated with the most abundant bacteria from the skin microbiota. Results on human ex vivo sebaceous gland models with the virulent phylotype of Cutibacterium acnes and Staphylococcus epidermidis present a significant reduction in viability, lipid production, and anti-inflammatory markers. We have developed an innovative anti-acne serum with L. plantarum that mimics the over-production of lipids, anti-inflammatory properties, and improves acne-disease skin models. Based on these results, we suggest that SkinDuo^TM may be introduced as an acne-mitigating agent.

We examined the concordance between vascular plants and macrofungi (grouped into trophic groups) in Mediterranean forest habitats (central Italy). Our goal was to test how consistently plant and fungi groups classify plots in a broadleaf deciduous forest dominated by Quercus cerris. Our hypothesis was that groups of plants can be used as surrogates for the classification of macrofungal communities. The test of concordance comprised two steps: 1) the plant species data sets were subjected to cluster analysis, to obtain three classifications based on presence of all plants, presence and frequency of only woody species; 2) Multiple Response Permutation Procedures (MRPP) was used to test the performance of each plant classification applied to the fungi data sets. The sample scores along the first PCA axis was used to investigate the relationships between compositional patterns. In the concordance analysis, the classification of only woody plants provided better results than the classification obtained with herbaceous and woody plants together. Cross-tests gave the best results when the “woody plants” classification was applied to ectomycorrhizal fungi (EMF) and, to a certain extent, to humicolous saprotrophs (Sh). The ordination analysis indicated that the frequency of woody plants follows a similar spatial distribution to EMF and Sh fungal groups and is therefore expected to discriminate the same environmental gradients. Many EMF exhibit preferences for few (one or two) hosts. Significant associations were found among a greater number of EMF and woody plant species. Woody plants such as Sorbus domestica and Prunus spinosa appear to be associated with many EMF. The combination of a high frequency of Fraxinus oxycarpa and Quercus petraea seems to promote distinct assemblages of EMF and Sh fungi. Then, characteristic assemblages of fungi were found in association with certain tree and shrub combinations.

Deadwood is involved in several important ecological roles, being the fundamental habitat of wood-decay fungi. At the same time, this polyphyletic group of fungi is the principal agent of wood decomposition, regulating the carbon cycle and the food resource for many other organisms. It is known that the diversity and community composition of wood-decay fungi are related to the tree species, decay stage and size of the woody debris in which they are living. Nevertheless, there is a lack of information on Mediterranean Quercus cerris L. forests. In response, we explored how wood-decay fungi colonize different woody types and how the productivity, richness and community composition of these fungi is influenced by the decay stage and size of the deadwood. Our results indicate that the studied groups, i.e., Ascomycetes, Corticioids, Polyporoids and Heterobasidiomycetes responded differently to the woody debris classes. Moreover, we note the high importance of smaller and soft-decayed woody debris for community composition and richness, hosting a great number of species, in addition to the positive effect of the heterogeneity of the woody debris size for wood-decay fungi productivity.

The human respiratory tract is colonized by a complex microbial community that helps maintain respiratory health and plays a crucial role in defending the host from infections. Respiratory viruses have been demonstrated to alter microbiota composition, resulting in opportunistic species expansion, and increasing the disease severity and host susceptibility to bacterial co-infections. This study aims to examine the compositional differences in the nasal microbiota between SARS-CoV-2-infected and non-infected patients. We conducted Oxford Nanopore full-length 16S rRNA sequencing on nasal swabs from 94 COVID-19 negative and 85 COVID-19 positive patients collected during the SARS-CoV-2 pandemic in Malta. Our analysis identified significant alpha and beta diversity differences in the nasal microbiota composition among our study groups. We observed a trend toward decreased microbial richness and evenness in the COVID-Positive cohort with and increased abundance of common nasal opportunistic species including Citrobacter koseri, Dolosigranulum pigrum, Haemophilus influenzae, Klebsiella pneumoniae, and Moraxella catarrhalis. The findings from this study are in line with previously published papers identifying key alterations in the nasal microbiota composition associated with SARS-CoV-2 infection. Understanding these microbiome-driven mechanisms could present novel prognostic markers or offer new approaches for disease prevention and treatment.

The SARS-CoV-2 pandemic has led to an urgent need for effective and rapid diagnostic tools. In the present study, we have evaluated the predictive diagnostic potential of nasal microbiota by analyzing microbial community structures at different taxonomic level resolutions—species, genus, family, order, class and phylum—using Random Forest modelling. A total of 179 nasal swabs from COVID-19-positive (n = 85) and COVID-19-negative (n = 94) individuals were sequenced using a full-length 16S rRNA sequencing (Oxford Nanopore) approach. During each iteration of the Random Forest model, the dataset was randomly split into a training set (70%) and a testing set (30%). Model performance improved with finer taxonomic resolution, achieving the highest accuracy at the Species level (AUROC = 0.821 ± 0.059) and a sensitivity of 55.6% at a specificity threshold of 90%. A progressive decline in AUROC and sensitivity was observed at broader taxonomic levels. Furthermore, Beta diversity analysis supported that microbial community structures are more distinct between COVID-19-positive and COVID-19-negative groups at finer taxonomic levels. These findings highlight the potential role of nasal microbiota profiling as a secondary diagnostic tool for COVID-19, particularly at species- and genus-level classification, and underscore the importance of high taxonomic resolution in microbiome-based diagnostics. However, limited by an uneven sample distribution and the lack of medical evaluations, further large-scale studies are needed before the nasal microbiota can be implemented in the clinical diagnostics of COVID-19.