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Retrieval and visualization of biological data are essential for understanding complex systems. With the increasing volume of data generated from high-throughput sequencing technologies, effective and optimized data visualization tools have become indispensable. This is particularly relevant in the COVID-19 postpandemic period, where understanding the diversity and interactions of microbial communities (i.e., viral and bacterial) constitutes an important asset to develop and plan suitable interventions.In this chapter, we show the usage and the potentials of ExTaxsI (Exploring Taxonomy Information) tool to retrieve viral biodiversity data stored in National Center for Biotechnology Information (NCBI) databases and create the related visualization. In addition, by integrating different functions and modules, the tool generates relevant types of visualization plots to facilitate the exploration of microbial biodiversity communities useful to deep dive into ecological and taxonomic relationships among different species and identify potential significant targets.Using the Monkeypox virus as a case study, this work points out significant perspectives on biological data visualization, which can be used to gain insights into the ecology, evolution, and pathogenesis of viruses. Accordingly, we show the potentiality of ExTaxsI to organize and describe the available/downloaded data in an easy, simple, and interpretable way allowing the user to interact dynamically with the visualization plots through specific filters, zoom, and explore functions.

Individual and species traits may determine the functional relationships between predators and their prey, with clear consequences for the ecosystem services potentially associated with predation. Bats are well-known potential deliverers of ecosystem services in agroecosystems through predation of pest arthropods that may affect crop production. Here we test the role of individual and prey traits in shaping the dietary habits of the Savi's pipistrelle (Hypsugo savii), a common bat species that frequently occurs in agroecosystems across the Mediterranean area, assessing diet composition and trait-based biases through molecular tools and multivariate testing techniques. DNA metabarcoding identified 173 distinct prey taxa in bats sampled in the Vesuvius National Park (Southern Italy). The diet was strongly dominated by pest insects associated with agricultural areas, which covered 57% of the overall prey diversity, some representing major pests of high economic relevance. The multivariate analyses detected strong relationships between diet composition, sampling date, and individual bat body conditions. Larger bats consumed larger prey items, and prey consumed later in the season (August) were smaller than those eaten in early summer (July) and were typical of less natural habitats. Hypsugo savii is potentially an important pest suppressor in the agroecosystems of the Mediterranean region and the functional relationships between the traits of individual bats and their prey set the basis for a quantitative assessment of the associated ecosystem service. We also remark on the value of bat diet studies in aiding the surveillance of arthropod species relevant to agriculture, human health, and biodiversity conservation.

Habitat fragmentation affects biodiversity, but with unclear effects on pollinators and their interactions with plants in anthropized landscapes. Islands could serve as open air laboratories, suitable to disentangle how land-use alteration impacts pollination ecology. In Maldive islands we investigated how pollinator richness, plant-pollinator interactions and pollination efficiency are influenced by the green area fragmentation (i.e., gardens and semi-natural patches). Moreover, we considered the mediating role of pollinator body size and the plant trait of being invasive in shaping interactions. To do this, we surveyed pollinator insects from 11 islands representing a gradient of green area fragmentation. A DNA metabarcoding approach was adopted to identify the pollen transported by pollinators and characterize the plant-pollinator interactions. We found that intermediate levels of green area fragmentation characterized pollinator communities and increased their species richness, while decreasing interaction network complexity. Invasive plants were more frequently found on pollinator bodies than native or exotic noninvasive ones, indicating a concerningly higher potential for pollen dispersal and reproduction of the former ones. Intriguingly, pollinator body size mediated the effect of landscape alteration on interactions, as only the largest bees expanded the foraging diet in terms of plant richness in the transported pollen at increasing fragmentation. In parallel, the pollination efficiency increased with pollinator species richness in two sentinel plants. This study shows that moderate landscape fragmentation of green areas shapes many aspects of the pollination ecosystem service, where despite interactions being less complex and mediated by pollinator body size, pollinator insect biodiversity and potential plant reproduction are supported.

Large amounts of data from microbiome-related studies have been (and are currently being) deposited on international public databases. These datasets represent a valuable resource for the microbiome research community and could serve future researchers interested in integrating multiple datasets into powerful meta-analyses. However, this huge amount of data lacks harmonization and it is far from being completely exploited in its full potential to build a foundation that places microbiome research at the nexus of many subdisciplines within and beyond biology. Thus, it urges the need for data accessibility and reusability, according to findable, accessible, interoperable and reusable (FAIR) principles, as supported by National Microbiome Data Collaborative and FAIR Microbiome. To tackle the challenge of accelerating discovery and advances in skin microbiome research, we collected, integrated and organized existing microbiome data resources from human skin 16S rRNA amplicon-sequencing experiments. We generated a comprehensive collection of datasets, enriched in metadata, and organized this information into data frames ready to be integrated into microbiome research projects and advanced post-processing analyses, such as data science applications (e.g. machine learning). Furthermore, we have created a data retrieval and curation framework built on three different stages to maximize the retrieval of datasets and metadata associated with them. Lastly, we highlighted some caveats regarding metadata retrieval and suggested ways to improve future metadata submissions. Overall, our work resulted in a curated skin microbiome datasets collection accompanied by a state-of-the-art analysis of the last 10 years of the skin microbiome field. Database URL:  https://github.com/giuliaago/SKIOMEMetadataRetrieval.

Forest ecosystems are fundamental to the conservation of global biodiversity and human wellbeing, hosting high numbers of species worldwide, and providing essential regulatory, provisioning, and cultural ecosystem services. The increasing impact of drivers of environmental changes such as climate change and biological invasions endangers forests, often acting synergically with forest pests, whose effects are exacerbated. Within this context, forest bats may represent key ecosystem service providers by consuming pests such as defoliating and fruit-damaging arthropods, yet their actual role has been so far neglected. Here we pursued the hypothesis that forest bats will be important suppressors of agroforestry pest insects. We analysed the diet of two forest specialists, Barbastella barbastellus and Plecotus auritus, occurring in syntopy in beech forests of Central Italy, by adopting molecular tools. The diet of the two bat species differed significantly: only 52 out of 71 and 69 prey taxa identified in the diet of B. barbastellus and P. auritus, respectively, were shared between the two bat species. Individual bats preyed, on average, upon 10 insect taxa, and pests were especially frequent in the diet of P. auritus (up to 85% of prey eaten by an individual bat). Such pests included both agricultural and forest-damaging insects, some of which pose serious threats to agroforestry systems. This highlights the importance of preserving animal diversity in forests for the benefit of this ecosystem type as well as of the surrounding habitats, such as farmland.

Background Sustainable aquaculture relies on multiple factors, including water quality, fish diets, and farmed fish. Replacing fishmeal (FM) with alternative protein sources is key for improving sustainability in aquaculture and promoting fish health. Indeed, great research efforts have been made to evaluate novel feed formulations, focusing especially on the effects on the fish gut microbiome. Few studies have explored host-environment interactions. In the present study, we evaluated the influence of novel insect-based (Tenebrio molitor) fish diets on the microbiome at the water-fish interface in an engineered rainbow trout (Oncorhynchus mykiss) farming ecosystem. Using 16S rRNA gene metabarcoding, we comprehensively analyzed the microbiomes of water, tank biofilm, fish intestinal mucus, fish cutis, and feed samples.Results Core microbiome analysis revealed the presence of a highly reduced core shared by all sample sources, constituted by Aeromonas spp., in both the control and novel feed test groups. Network analysis showed that samples were clustered based on the sample source, with no significant differences related to the feed formulation tested. Thus, the different diets did not seem to affect the environment (water and tank biofilm) and fish (cutis and intestinal mucus) microbiomes. To disentangle the contribution of feed at a finer scale, we performed a differential abundance analysis and observed differential enrichment/impoverishment in specific taxa, comparing the samples belonging to the control diet group and the insect-based diet group.Conclusions Omic exploration of the water-fish interface exposes patterns that are otherwise undetected. These data demonstrate a link between the environment and fish and show that subtle but significant differences are caused by feed composition. Thus, the research presented here is a step towards positively influencing the aquaculture environment and its microbiome.

The aim of the present study was to compare sedation quality and cardiorespiratory parameters in healthy dogs after intramuscular injection of dexmedetomidine and ketamine with or without methadone. Forty client-owned dogs were randomly divided into two groups and received IM dexmedetomidine (5 µg kg−1) and ketamine (1 mg kg−1), associated (DKM group) or not (DK group) with methadone (0.2 mg kg−1). Sedation, heart rate (HR), respiratory rate (ƒR), mucous membrane and rectal temperature were recorded at baseline (T0) and after 5 (T5), 10 (T10) and 20 (T20) minutes. From T10, cardiac rhythm was monitored with a continuous lead II electrocardiogram. Ease of venous catheter placement, total propofol dose and any apnea episodes were recorded. Sedation was significantly greater in the DKM group, and a significant increase from T5 to T20 within DKM (P = .0002) and DK (P = .008) was also observed. Within each group, HR was significantly lower at all time points compared to baseline. No significant differences between groups were found in the number of arrhythmogenic events (atrioventricular blocks). In both group ƒR decreased over time. The propofol dose required for anesthesia induction was significantly lower (P = .027) in the DKM group. In conclusion, a good level of sedation was achieved in both groups, although this was greater in DKM. Smooth animal-operator interaction and ease of venous catheter placement showed that DK was a useful sedative protocol in healthy patients.

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.

Omics tools offer the opportunity to characterize and trace traditional and industrial fermented foods. Bioinformatics, through machine learning, and other advanced statistical approaches, are able to disentangle fermentation processes and to predict the evolution and metabolic outcomes of a food microbial ecosystem. By assembling microbial artificial consortia, the biotechnological advances will also be able to enhance the nutritional value and organoleptics characteristics of fermented food, preserving, at the same time, the potential of autochthonous microbial consortia and metabolic pathways, which are difficult to reproduce. Preserving the traditional methods contributes to protecting the hidden value of local biodiversity, and exploits its potential in industrial processes with the final aim of guaranteeing food security and safety, even in developing countries.

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.

Background: The increasing availability of multi-omics data is leading to regularly revised estimates of existing biodiversity data. In particular, the molecular data enable novel species to be characterized and the information linked to those already observed to be increased with new genomics data. For this reason, the management and visualization of existing molecular data, and their related metadata, through the implementation of easy-to-use IT tools have become a key point to design future research. The more users are able to access biodiversity-related information, the greater the ability of the scientific community to expand its knowledge in this area. Results: In this article we focus on the development of ExTaxsI (Exploring Taxonomy Information), an IT tool that can retrieve biodiversity data stored in NCBI databases and provide a simple and explorable visualization. We use 3 case studies to show how an efficient organization of the available data can lead to obtaining new information that is fundamental as a starting point for new research. Using this approach highlights the limits in the distribution of data availability, a key factor to consider in the experimental design phase of broad-spectrum studies such as metagenomics. Conclusions: ExTaxsI can easily retrieve molecular data and its metadata with an explorable visualization, with the aim of helping researchers to improve experimental designs and highlight the main gaps in the coverage of available data.

Microbial communities interact with us and affect our health in ways that are only beginning to be understood. Microorganisms have been detected in every ecosystem on Earth, as well as in any built environment that has been investigated. Drinking water sources, drinking water treatment plants and distribution systems provide peculiar microbial ecological niches, dismantling the belief of the “biological simplicity” of drinking water. Nevertheless, drinking water microbiomes are understudied compared to other microbiomes. Recent DNA sequencing and meta-omics advancements allow a deeper understanding of drinking water microbiota. Thus, moving beyond the limits of day-to-day testing for specific pathogenic microbes, new approaches aim at predicting microbiome changes driven by disturbances at the macro-scale and overtime. This will foster an effective and proactive management of water sources, improving the drinking water supply system and the monitoring activities to lower public health risk. Here, we want to give a new angle on drinking water microbiome research. Starting from a selection of 231 scientific publications on this topic, we emphasize the value of biodiversity in drinking water ecosystems and how it can be related with industrialization. We then discuss how microbiome research can support sustainable drinking water management, encouraging collaborations across sectors and involving the society through responsible research and innovation.

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'integraz, 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.

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. 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 work aimed to analyse the microbiota variability of different categories of some insect-based commercial food 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. This microbial signature can be recognized despite the different food processing levels, rearing conditions and selling companies. Furthermore, differences between raw and processed food made of the same insect or similar product produced by different companies was found. These results support the application of HTS analysis for studying the composition of insect-based commercial food products 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.

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 introduct

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.