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Gli approcci metagenomici hanno cambiato il modo di studiare la biologia e la biodiversità in diversi campi. In particolare, il progresso tecnologico ci consente di determinare la composizione tassonomica dei campioni e di studiare la biodiversità in ambienti molto diversi. Al giorno d'oggi, il DNA metabarcoding è una procedura standard, applicata in un'ampia gamma di settori, dalla salute umana all'ecologia, alle applicazioni industriali. Negli ultimi anni, il DNA metabarcoding applicato al 16S rRNA è stato ampiamente utilizzato per studiare la comunità batterica, portando ad analisi di routine che hanno creato enormi quantità di dati, consentendo ai ricercatori di sviluppare strategie data-driven per rispondere a domande biologiche complesse. Inoltre, il DNA metabarcoding può essere utilizzato anche per studiare Piante, Animali o Funghi, grazie allo sviluppo di diversi marcatori molecolari. In entrambi i casi, considerando l'enorme quantità di dati prodotti dai ricercatori e disponibili nelle banche dati, una prospettiva di ‘data mining’ nella gestione e nell'esplorazione dei dati di DNA metabarcoding potrebbe essere utile per identificare nuovi pattern ed estrarre maggiori informazioni dai dati. Nella mia tesi di dottorato, mi sono focalizzata su una prospettiva incentrata sui dati di DNA metabarcoding, toccando quattro punti principali che possono potenziare e migliorare le strategie attuali: i) considerare le informazioni molecolari ottenute dal sequenziamento high-throughput del DNA (HTS) e disponibili in archivi pubblici, ii ) migliorare la fase di assegnazione della tassonomia, iii) studiare nuovi metodi per la ricostruzione di pattern di biodiversità e iv) utilizzare dati già prodotti come risorsa preziosa per la ricerca. Questi quattro punti possono migliorare a diversi livelli le potenzialità delle applicazioni di tecniche fondate sul DNA metabarcoding, aprendo la strada a procedure di standardizzazione per marcatori meno diffusi e all'integrazione di nuove strategie di data mining e riutilizzo di dati di DNA metabarcoding. Metagenomic approaches have changed the way to study biology and biodiversity in several fields. In particular, technology advancement enables us to determine taxa composition and to study complex biodiversity patterns in very different environments. Nowadays, DNA metabarcoding is a standard procedure, applied on a wide range of fields, from human health to ecology, to industry applications. In the last few years, 16S rRNA metabarcoding was widely used to study the bacterial community, leading to routine analysis which created huge amounts of data, bringing researchers to develop data mining strategies in order to answer complex biological questions. On the other hand, DNA metabarcoding can be applied also to study Plants, Animals or Fungi, as very different molecular markers have been identified. In both cases, considering the huge amount of data produced by researchers and available in repositories, a data-driven perspective in managing and exploring DNA metabarcoding data could be useful to collect hidden information and potentially determine undiscovered aspects. In this PhD dissertation, I focused the attention on a data-centered perspective of DNA metabarcoding data, touching four main points that can enhance and ameliorate the current strategies: i) consider the molecular information obtained from high-throughput DNA sequencing (HTS) and available in public repositories, ii) enhance taxonomy assignment step, iii) investigate new methods for pattern reconstruction and iv) use data as a valuable resource for research. These four steps can enhance at different levels the potentials of DNA metabarcoding applications, paving the way for standardization procedures for uncommon markers and the integration of new data mining and data reuse strategies of metabarcoding data.

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

Medicinal plants have been widely used in traditional medicine due to their therapeutic properties. Although they are mostly used as herbal infusion and tincture, employment as ingredients of food supplements is increasing. However, fraud and adulteration are widespread issues. In our study, we aimed at evaluating DNA metabarcoding as a tool to identify product composition. In order to accomplish this, we analyzed fifteen commercial products with DNA metabarcoding, using two barcode regions: psbA-trnH and ITS2. Results showed that on average, 70% (44–100) of the declared ingredients have been identified. The ITS2 marker appears to identify more species (n = 60) than psbA-trnH (n = 35), with an ingredients’ identification rate of 52% versus 45%, respectively. Some species are identified only by one marker rather than the other. Additionally, in order to evaluate the quantitative ability of high-throughput sequencing (HTS) to compare the plant component to the corresponding assigned sequences, in the laboratory, we created six mock mixtures of plants starting both from biomass and gDNA. Our analysis also supports the application of DNA metabarcoding for a relative quantitative analysis. These results move towards the application of HTS analysis for studying the composition of herbal teas for medicinal plants’ traceability and quality control.

Sub-Saharan African crop production largely relies on smallholder farms, located both in urban and agricultural landscapes. In this context, the investigation of plant and pollinator diversity and their interactions is of primary importance since both these factors are threatened by land use intensification and the consequent loss of natural habitats. In this study, we evaluated for the first time how plant and pollinator insect assemblages and interactions in Sub-Saharan farming conditions are shaped by land use intensification. To do that, we complemented biodiversity field surveys in Northern Tanzania with a modern DNA metabarcoding approach to characterize the foraged plants and thus built networks describing plant-pollinator interactions at the individual insect level. Moreover, we coupled this information with quantitative traits of landscape composition and floral availability surrounding each farm. We found that pollinator richness decreased with increasing impervious and agricultural cover in the landscape, whereas the flower density at each farm correlated with pollinator richness. The intensification of agricultural land use and urbanization correlated with a higher foraging niche overlap among pollinators due to convergence of individuals’ flower visiting strategies. Furthermore, within farms, the higher availability of floral resources drove lower niche overlap among individuals, while a greater flower visitors abundance shaped higher generalization at the networks level (H2′), possibly due to increased competition. These mechanistic understandings leading to individuals’ foraging niche overlap and generalism at the network level, could imply stability of interactions and of the pollination ecosystem service. Our integrative survey proved that plant-pollinator systems are largely affected by land use intensification and by local factors in smallholder farms of Sub-Saharan Africa. Thus, policies promoting nature-based solutions, among which the introduction of more pollinator-friendly practices by smallholder farmers, could be effective in mitigating the intensification of both urban and rural landscapes in this region, as well as in similar Sub-Saharan contexts.

One of the main goals of the quality control evaluation is to identify contaminants in raw material, or contamination after a food is processed and before it is placed on the market. During the treatment processes, contamination, both accidental and economically motivated, can generate incongruence between declared and real composition. In our study, we evaluated if DNA metabarcoding is a suitable tool for unveiling the composition of processed food, when it contains small trace amounts. We tested this method on different types of commercial plant products by using tnrL marker and we applied amplicon-based high-throughput sequencing techniques to identify plant components in different food products. Our results showed that DNA metabarcoding can be an effective approach for food traceability in different type of processed food. Indeed, the vast majority of our samples, we identified the species composition as the labels reported. Although some critical issues still exist, mostly deriving from the starting composition (i.e., variable complexity in taxa composition) of the sample itself and the different processing level (i.e., high or low DNA degradation), our data confirmed the potential of the DNA metabarcoding approach also in quantitative analyses for food composition quality control.

Traceability, quality and safety of edible insects are important both for the producers and the consumers. Today, alongside the burst of edible insects in western countries, we are facing a gap of knowledge of insect microbiota associated with the microbial ecosystems of insect-based products. Recent studies suggest that the insect microbiota can vary between insect species and that can be shaped by additional factors, such as rearing conditions. Also, the production processes of raw materials (i.e. insect flour) into final food products can affect the insect microbiota too. This has consequences for the evaluation of food safety and food traceability. In this context, High-Throughput DNA Sequencing (HTS) techniques can give insight into the carryover of insect microbiota into final food products. In this study, we investigated the microbiota composition of insect-based commercial food products, applying HTS techniques coupled with bioinformatic analysis. The aim of this work was to analyse the microbiota variability of different categories of insect-based products made of A. domesticus (house cricket), T. molitor (mealworm beetle), and A. diaperinus (lesser mealworm or litter beetle), including commercial raw materials and processed food items, purchased via e-commerce from different companies. Our data revealed that samples cluster per insect species based on microbiota profile and preliminary results suggested that a small number of prevalent bacteria formed a “core microbiota” characterizing the products depending on the insect, suggesting that a resident microbiota is conserved. This microbial signature can be recognized despite the different food processing levels, rearing conditions selling companies. We showed that differences exist when comparing raw vs processed food made of the same insect, or similar products produced by different companies as well, laying the groundwork for further analyses. These results support the application of HTS analysis for studying the composition of processed insect food in a wider perspective, for food traceability and food quality control.

In the context of novel foods, a category for which the market demand is increasing worldwide, the consumption of edible insects and related insect-based products is expected to grow in the next years. Insects represent an important source of energy for the human diet but there is a lack of scientific knowledge about their processing to ensure safe food items to the consumer. In this study we adopted a combined DNA-based approach to verify the identity of the declared species in five categories of commercial insect-based products (mt COI DNA barcoding) and to characterize plant declared ingredients or contaminants (nu ITS2 DNA metabarcoding) with particular attention to putative elements of allergenic concern belonging, for example to the insect rearing substrate. Moreover, the same approach has been used to assess its sensitivity to cases of contamination and counterfeits to insect flour with low cost (and potentially allergenic) vegetable flours like wheat and soybean. Results show the success of insect DNA barcoding authentication even for highly processed products. Furthermore, the DNA metabarcoding analysis revealed a high efficacy as a screening method to identify both plant ingredients and vegetal traces belonging to insect farming or possible adulteration events, also acting as an early warning strategy for the occurrence of allergens of human concern. This approach could support the development of new risk assessment procedures for novel foods by regulatory authorities to ensure their quality, safety, and acceptance which will become more required in order to face the challenge of feeding the world population in the next decades.