Fabio Fracchetti
Fabio Fracchetti is co-founder and COO at Microbion. He holds holding a Master Degree in “Agri-Industrial Biotechnology”. He completed a PhD program in “Applied Biotechnology” with a specialization in “Enological and Viticultural Biotechnology” and a strong connection with producers of selected microbial cultures. In 2010 he received the Master Degree in “Biotechnology law” focusing the attention on the juridical bases and case-studies about the patenting of selected microbial cultures.
Sessions
The comprehensive characterization of soil microbiomes is pivotal for understanding ecosystem functions and managing soil health. The aim of the study is to develop a standardized metabarcoding pipeline utilizing universal genetic markers, such as the 16S rRNA and ITS and it is also possible for specific markers that target genes associated with critical metabolic functions. Sequencing is conducted using the MinION platform by Oxford Nanopore Technologies, enabling real-time data acquisition and analysis.
A key component of this pipeline is the creation of a qualified database comprising accurately annotated sequences derived from standardized metabarcoding protocols. This database, enhanced by the custom analysis tools available through the EPI2ME labs platform, serves as a foundational tool for ensuring reproducibility and comparability across studies, addressing the current challenge of data heterogeneity due to varied experimental and analytical methodologies. This database not only enhances the taxonomic resolution but can also potentially enrich the functional profiling of soil microbiomes.
The research incorporates two distinct series of soil samples: one from wheat fields cultivated in Sicily and another from greenhouse-grown tomatoes. These diverse agricultural settings provide two case studies with a broad spectrum of microbial communities, to test the robustness and applicability of the metabarcoding pipeline.
The standardized data from this qualified database can be exploited to train artificial intelligence (AI) models, aiming to identify predictive signatures of soil health and potential for reclamation from pollutants. By applying machine learning techniques to metabarcoding data, this approach promises to uncover novel correlations between microbial communities and soil conditions, potentially leading to innovative strategies for soil management and rehabilitation.
In summary, the establishment of a standardized metabarcoding pipeline and a qualified database on the MinION platform, enriched with data from diverse agricultural soils, represents a significant advancement in soil microbiology. This framework not only enhances our understanding of microbial diversity and function but also offers the possibility to apply AI technologies to predict and improve soil health on a global scale.