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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.

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.

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.

As human activities on our planet persist, causing widespread and irreversible environmental degradation, the need to biomonitor ecosystems has never been more pressing. These circumstances have required a renewal in monitoring techniques, encouraged by necessity to develop more rapid and accurate tools which will support timely observations of ecosystem structure and function. The World Exposition (from now ‘EXPO2015’) hosted in Milan from May to October 2015 was a global event that could be categorized as a mega-event, which can be defined as an acute environmental stressor, possibly generating biodiversity alteration and disturbance.During the six months of EXPO2015, exhibitors from more than 135 countries and 22 million visitors insisted on a 1.1 million square meters area. Faced with such a massive event, we explore the potential of DNA metabarcoding using three molecular markers to improve the understanding of anthropogenic impacts in the area, both considering air and water monitoring. Furthermore, we explore the effectiveness of the taxonomy assignment phase considering different taxonomic levels of analysis and the use of data mining approaches to predict sample origin. Unless the degree of taxa identification still remains open, our results showed that DNA metabarcoding is a powerful genomic-based tool to monitor biodiversity at the microscale, allowing us to capture exact fingerprints of specific event sites and to explore in a comprehensive manner the eukaryotic community alteration. With this work, we aim to disentangle and overcome the crucial issues related to the generalization of DNA metabarcoding in order to support future applications.

Habitat fragmentation is known to affect biodiversity, but the impact on pollinators and their interactions with plants is still unclear in anthropized landscapes. Islands are open-air laboratories for ecological studies with simplified communities and interactions, suitable to disentangle how land-use alteration impacts pollination ecology and its ecosystem service. Here, we used Maldives islands as model systems to investigate how pollinator richness, their mutualistic interactions with plants, and pollination efficiency are shaped by the degree of green area fragmentation (i.e., gardens, parks and semi-natural green covered patches), by considering both community- and species-level responses. To do this, we surveyed pollinators from 11 islands showing a gradient of green area fragmentation. In order to characterize the interactions between plants and pollinators and obtain a novel and comprehensive view of the key ecological dynamics, a DNA metabarcoding approach was adopted to identify the pollen carried by pollinators. We found that green area fragmentation at intermediate levels played positive effects on pollinator richness. However, fragmentation decreased interaction network complexity. Intriguingly, body size mediated the effect of landscape alteration on plant-pollinator interactions, as only the largest bee species expanded the foraging breath in terms of transported pollen richness 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 the ecosystem service of pollination, where in spite of interactions being less complex and mediated by pollinator body size, pollinator biodiversity and potential plant reproduction are supported.

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.

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 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, urges the need for data accessibility and reusability, according to FAIR (Findable, Accessible, Interoperable, and Reusable) 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 analysis, 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.

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.

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.

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.