Characterization of Gut Microbiome Composition in Patients with Triple-Negative Breast Cancer Treated with Neoadjuvant Chemotherapy.

INTRODUCTION: Patients with triple-negative breast cancer (TNBC) achieving a pathological complete response (pCR) after neoadjuvant chemotherapy have a better event-free survival. The role of gut microbiome in early TNBC is underexplored. METHODS: Microbiome was analyzed by 16SrRNA sequencing. RESULTS: Twenty-five patients with TNBC treated with neoadjuvant anthracycline/taxane-based chemotherapy were included. Fifty-six percent achieved a pCR. Fecal samples were collected before (t0), at 1 (t1), and 8 weeks (t2) from chemotherapy. Overall, 68/75 samples (90.7%) were suitable for microbiome analysis. At t0, pCR group showed a significantly higher α-diversity as compared with no-pCR, (P = .049). The PERMANOVA test on ß-diversity highlighted a significant difference in terms of BMI (P = 0.039). Among patients with available matched samples at t0 and t1, no significant variation in microbiome composition was reported over time. CONCLUSIONS: Fecal microbiome analysis in early TNBC is feasible and deserves further investigation in order to unravel its complex correlation with immunity and cancer.

Machine Learning and Canine Chronic Enteropathies: A New Approach to Investigate FMT Effects.

Fecal microbiota transplantation (FMT) represents a very promising approach to decreasing disease activity in canine chronic enteropathies (CE). However, the relationship between remission mechanisms and microbiome changes has not been elucidated yet. The main objective of this study was to report the clinical effects of oral freeze-dried FMT in CE dogs, comparing the fecal microbiomes of three groups: pre-FMT CE-affected dogs, post-FMT dogs, and healthy dogs. Diversity analysis, differential abundance analysis, and machine learning algorithms were applied to investigate the differences in microbiome composition between healthy and pre-FMT samples, while Canine Chronic Enteropathy Clinical Activity Index (CCECAI) changes and microbial diversity metrics were used to evaluate FMT effects. In the healthy/pre-FMT comparison, significant differences were noted in alpha and beta diversity and a list of differentially abundant taxa was identified, while machine learning algorithms predicted sample categories with 0.97 (random forest) and 0.87 (sPLS-DA) accuracy. Clinical signs of improvement were observed in 74% (20/27) of CE-affected dogs, together with a statistically significant decrease in CCECAI (median value from 5 to 2 median). Alpha and beta diversity variations between pre- and post-FMT were observed for each receiver, with a high heterogeneity in the response. This highlighted the necessity for further research on a larger dataset that could identify different healing patterns of microbiome changes.

Characterization of intestinal microbiota in normal weight and overweight Border Collie and Labrador Retriever dogs.

Obesity in dogs is an emerging issue that affects canine health and well-being. Its development is ascribed to several factors, including genetic predisposition and dietary management, and recent evidence suggests that intestinal microbiota may be involved as well. Previous works have shown obesity to be linked to significant changes in gut microbiota composition in humans and mice, but only limited information is available on the role played by canine gut microbiota. The aim of this exploratory study was to investigate whether composition of canine faecal microbiota may be influenced by overweight condition and breed. All the enrolled companion dogs were young adults, intact, healthy, and fed commercial extruded pet food; none had received antibiotics, probiotics or immunosuppressant drugs in the previous six months. Labrador Retriever (LR) and Border Collie (BC) were chosen as reference breeds and Body Condition Score (BCS) on a 9-point scale as reference method for evaluating body fat. The faecal microbial communities of 15 lean (BCS 4-5/9; 7 LRs and 8 BCs) and 14 overweight (BCS > 5/9; 8 LRs and 6 BCs) family dogs were analysed using 16S rRNA gene sequencing. Moreover, for each dog, the daily intake of energy (kcal/d) and dietary macronutrients (g/d) were calculated according to an accurate feeding history collection. Firmicutes and Bacteroidetes resulted the predominant phyla (51.5 ± 10.0% and 33.4 ± 8.5%, respectively) in all dogs. Bioinformatic and statistical analysis revealed that no bacterial taxon differed significantly based on body condition, except for genus Allisonella (p < 0.05); BC gut microbiota was richer (p < 0.05) in bacteria belonging to phyla Actinobacteria (family Coriobacteriaceae in particular) and Firmicutes (Allobaculum and Roseburia genera). No remarkable differences were recorded either for diversity indices (i.e., alpha diversity, p > 0.10) or for divergence within the sample set (i.e., beta diversity, p > 0.05). PERMANOVA tests performed on single factors demonstrated the tendency of dietary protein to influence the recruited dogs' microbiota beta-diversity at amplicon sequence variant level (p = 0.08). In conclusion, the faecal microbiota of dogs involved in this exploratory study showed no major variations based on body condition. However, our findings suggested that certain bacterial taxa previously acknowledged in obesity-related studies may be detected in dissimilar amounts depending on canine breed.

A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach.

Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups' separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon index: p-values: active UC vs HCs = 0.0005; active UC vs inactive UC = 0.0273; HCs vs inactive UC = 0.0260). In particular, patients with active UC showed the lowest values, followed by patients with inactive UC, and HCs. At species level, we found high levels of Bifidobacterium adolescentis and Haemophilus parainfluenzae in inactive UC and active UC, respectively. A specific microbiota profile was found for each group and was confirmed with sparse partial least squares discriminant analysis, a machine learning-supervised approach. The latter allowed us to observe a perfect class prediction and group separation using the complete information (full Operational Taxonomic Unit table), with a minimal loss in performance when using only 5% of features. A machine learning approach to 16S rRNA data identifies a bacterial signature characterizing different degrees of disease activity in UC. Follow-up studies will clarify whether such microbiota profiling are useful for diagnosis and management.

Beware to ignore the rare: how imputing zero-values can improve the quality of 16S rRNA gene studies results.

BACKGROUND: 16S rRNA-gene sequencing is a valuable approach to characterize the taxonomic content of the whole bacterial population inhabiting a metabolic and spatial niche, providing an important opportunity to study bacteria and their role in many health and environmental mechanisms. The analysis of data produced by amplicon sequencing, however, brings very specific methodological issues that need to be properly addressed to obtain reliable biological conclusions. Among these, 16S count data tend to be very sparse, with many null values reflecting species that are present but got unobserved due to the multiplexing constraints. However, current data workflows do not consider a step in which the information about unobserved species is recovered. RESULTS: In this work, we evaluate for the first time the effects of introducing in the 16S data workflow a new preprocessing step, zero-imputation, to recover this lost information. Due to the lack of published zero-imputation methods specifically designed for 16S count data, we considered a set of zero-imputation strategies available for other frameworks, and benchmarked them using in silico 16S count data reflecting different experimental designs. Additionally, we assessed the effect of combining zero-imputation and normalization, i.e. the only preprocessing step in current 16S workflow. Overall, we benchmarked 35 16S preprocessing pipelines assessing their ability to handle data sparsity, identify species presence/absence, recovery sample proportional abundance distributions, and improve typical downstream analyses such as computation of alpha and beta diversity indices and differential abundance analysis. CONCLUSIONS: The results clearly show that 16S data analysis greatly benefits from a properly-performed zero-imputation step, despite the choice of the right zero-imputation method having a pivotal role. In addition, we identify a set of best-performing pipelines that could be a valuable indication for data analysts.

Faecal Microbiome Transplantation as a Solution to Chronic Enteropathies in Dogs: A Case Study of Beneficial Microbial Evolution.

Chronic enteropathies (CE) are gastrointestinal diseases that afflict about one in five dogs in Europe. Conventional therapeutic approaches include dietary intervention, pharmacological treatment and probiotic supplements. The patient response can be highly variable and the interventions are often not resolutive. Moreover, the therapeutic strategy is usually planned (and gradually corrected) based on the patient's response to empirical treatment, with few indirect gut health indicators useful to drive clinicians' decisions. The ever-diminishing cost of high-throughput sequencing (HTS) allows clinicians to directly follow and characterise the evolution of the whole gut microbial community in order to highlight possible weaknesses. In this framework, faecal microbiome transplantation (FMT) is emerging as a feasible solution to CE, based on the implant of a balanced, eubiotic microbial community from a healthy donor to a dysbiotic patient. In this study, we report the promising results of FMT carried out in a 9-year-old dog suffering from CE for the last 3 years. The patient underwent a two-cycle oral treatment of FMT and the microbiota evolution was monitored by 16S rRNA gene sequencing both prior to FMT and after the two administrations. We evaluated the variation of microbial composition by calculating three different alpha diversity indices and compared the patient and donor data to a healthy control population of 94 dogs. After FMT, the patient's microbiome and clinical parameters gradually shifted to values similar to those observed in healthy dogs. Symptoms disappeared during a follow-up period of six months after the second FMT. We believe that this study opens the door for potential applications of FMT in clinical veterinary practice and highlights the need to improve our knowledge on this relevant topic.

The Interplay between Campylobacter and the Caecal Microbial Community of Commercial Broiler Chickens over Time.

Campylobacter is the most frequent foodborne zoonotic bacteria worldwide, with chicken meat being overwhelmingly the most important reservoir for human infections. Control measures implemented at the farm level (i.e., biosecurity or vaccination), which have been successfully applied to limit other pathogens, such as Salmonella, have not been effective in reducing Campylobacter occurrence. Thus, new approaches are needed to fully understand the ecological interactions of Campylobacter with host animals to effectively comprehend its epidemiology. The objective of this study was to analyse longitudinally the gut microbiota composition of Campylobacter-infected and non-infected farms to identify any difference that could potentially be indicative of gut colonization by Campylobacter spp. Differences in the colonization rate and timing were observed at the farms that became positive for Campylobacter jejuni over the investigated time points, even though in positive tests, the occurrence of Campylobacter jejuni gut colonization was not observed before the second week of the life of the birds. Significant differences were observed in the abundances of specific bacterial taxa between the microbiota of individuals belonging to farms that became Campylobacter positive during the study and those who remained negative with particular reference to Bacteroidales and Clostridiales, respectively. Moreover, Campylobacter colonization dramatically influenced the microbiota richness, although to a different extent depending on the infection timing. Finally, a key role of Faecalibacterium and Lactobacillus genera on the Campylobacter microbial network was observed. Understanding the ecology of the Campylobacter interaction with host microbiota during infection could support novel approaches for broiler microbial barrier restoration. Therefore, evidence obtained through this study can be used to identify options to reduce the incidence of infection at a primary production level based on the targeted influence of the intestinal microbiota, thus helping develop new control strategies in order to mitigate the risk of human exposure to Campylobacter by chicken meat consumption.

Modeling Microbial Community Networks: Methods and Tools.

In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities' organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.

Effect of sterilization on the canine vaginal microbiota: a pilot study.

BACKGROUND: Surgical sterilization is the most effective method of contraception for dogs. It also prevents pyometra and reduces the risk of mammary tumour development. However, this procedure also has negative effects, such as urinary incontinence. Steroid hormone deprivation following gonadectomy could also affect canine vaginal mucosa conditions and the microbial community colonizing the vaginal tract. This hypothesis was tested by comparing the vaginal cytology and microbial community of two groups of bitches, including 11 in anoestrus and 10 sterilized bitches (post-pubertal sterilization in the last 4 years). Bacteria were identified through metataxonomic analysis, amplifying the V3-V4 regions of 16S rRNA gene, and culturing methods. RESULTS: Vaginal mucosa cytology was suggestive of dystrophic conditions in sterilized bitches, whereas a typical anoestrus pattern with parabasal and intermediate cells was appreciable in anoestrous animals. Metataxonomic analysis revealed large inter-individual variability. Salmonella, Mycoplasma and Staphylococcus were present in moderate quantities in almost all the samples in both groups. Mollicutes (class level) and Tenericutes (phylum level) were commonly present in moderate quantities in anoestrus samples, whereas these microbes were present at high levels in a single sample from the sterilized group. Based on culturing, a higher number of different species were isolated from the anoestrous bitches, and Mycoplasma canis was exclusively identified in an anoestrous bitch. Staphylococcus spp. was the most frequently isolated genus in both groups, followed by Streptococcus spp., and, among gram-negative bacteria, Escherichia spp. and Haemophilus spp. A comparison of the numbers of the most frequently isolated genera of bacteria from vaginal cultures of bitches revealed that Pasteurella and Proteus were the most frequently identified in sterilized animals based on metataxonomic analysis (p-value = 0.0497 and 0.0382, respectively), whereas Streptococcus was significantly and most frequently isolated from anoestrous bitches using culture methods (p value = 0.0436). CONCLUSIONS: In this preliminary investigation, no global patterns of the vaginal bacteria community were noted that characterized the condition of the bitches; however, cytology suggested local modifications. Sterilization after puberty caused minimal alterations in the vaginal microbial community of bitches within 4 years after surgery.

SPARSim single cell: a count data simulator for scRNA-seq data.

MOTIVATION: Single cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data. RESULTS: In this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data. AVAILABILITY AND IMPLEMENTATION: SPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

metaSPARSim: a 16S rRNA gene sequencing count data simulator.

BACKGROUND: In the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data. RESULTS: In this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim. CONCLUSION: metaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

Evaluation and quantification of antimicrobial residues and antimicrobial resistance genes in two Italian swine farms.

Antimicrobial resistance genes (ARGs) are considered emerging environmental pollutants, posing potential risks for human and animal health: the misuse of antimicrobials in food-producing animals could favour the maintenance and spread of resistances in bacteria. The occurrence of ARGs in Italian swine farming - which has specific characteristics - was investigated in order to explore resistance spread dynamics. Two farrow-to-finish pig farms were longitudinally monitored: faecal samples from animals and environmental samples were collected. DNA was extracted and tetA, ermB, qnrS and mcr1 ARGs were analysed by qPCR for their ability to confer resistance to highly or critically important antimicrobials (CIAs). Moreover, 16SrDNA gene was analysed to assess bacterial abundance. ermB and tetA genes were found in animal samples and manure samples. On the contrary, mcr1 was exclusively found in weaners, while qnrS occurred in all animal categories but sows and finishers. Among the analysed genes, ermB and tetA showed the highest absolute and relative abundances. Our results indicate that ermB and tetA ARGs are widely disseminated in the explored farms, suggesting efficient maintenance among bacteria and persistence in the environment. Interestingly, the presence of qnrS and mcr1, limited to just a few animal categories, highlights inefficient dissemination of these genes in the farm environment, in particular for mcr1, a stable plasmid gene conferring resistance to the last-resort antimicrobial, colistin. Paying close attention only to the finishing phase would have hampered the discovery of resistances to CIAs at farm level, which we instead identified thanks to an intensive longitudinal monitoring programme.

Assessing antimicrobial resistance gene load in vegan, vegetarian and omnivore human gut microbiota.

Massive antimicrobial use in animal farming is considered as the greatest contributor to the presence of antimicrobial-resistant bacteria (ARB) in food of animal origin. Nevertheless, sewage from treated animals may impact on vegetables grown on fertilised fields, but it is largely unknown whether and to what extent ARB are transferred to vegetables and the human gut. It could be hypothesised that food of animal and vegetal origin have a different role in ARB transfer to the human gut and that different diets could be characterised by different antimicrobial resistance gene (ARG) loads. This study included three groups comprising vegans (n = 26), vegetarians (n = 32) and omnivores (n = 43). Metadata regarding food consumption and anthropometric parameters were collected. Gut microbial communities were investigated by 16S rDNA analysis. Four ARGs (sul2, tetA, blaTEM and strB) were quantified by qPCR. The results showed a lower total load of investigated ARGs in vegan diet (pairwise comparison adjusted results: omnivorous-vegan, P = 0.0119; omnivorous-vegetarian, P = 0.7416; and vegan-vegetarian, P = 0.0119). No significant differences in abundance of each gene separately were found between the three groups. Neither the amount of animal protein nor the occurrence of ARGs was significant in explaining differences in the gut microbial community of individuals, and a large proportion of the differences between community composition (PERMANOVA, 46.87%) was not explained by the analysed variables. The results support the role of omnivorous and vegetarian diets in accumulating ARGs, suggesting a possible role for animal-derived food consumption.

Assessing the Influence of Vegan, Vegetarian and Omnivore Oriented Westernized Dietary Styles on Human Gut Microbiota: A Cross Sectional Study.

Diet and lifestyle have a strong influence on gut microbiota, which in turn has important implications on a variety of health-related aspects. Despite great advances in the field, it remains unclear to which extent the composition of the gut microbiota is modulated by the intake of animal derived products, compared to a vegetable based diet. Here the specific impact of vegan, vegetarian, and omnivore feeding type on the composition of gut microbiota of 101 adults was investigated among groups homogeneous for variables known to have a role in modulating gut microbial composition such as age, anthropometric variables, ethnicity, and geographic area. The results displayed a picture where the three different dietetic profiles could be well distinguished on the basis of participant's dietetic regimen. Regarding the gut microbiota; vegetarians had a significantly greater richness compared to omnivorous. Moreover, counts of Bacteroidetes related operational taxonomic units (OTUs) were greater in vegans and vegetarians compared to omnivores. Interestingly considering the whole bacterial community composition the three cohorts were unexpectedly similar, which is probably due to their common intake in terms of nutrients rather than food, e.g., high fat content and reduced protein and carbohydrate intake. This finding suggests that fundamental nutritional choices such as vegan, vegetarian, or omnivore do influence the microbiota but do not allow to infer conclusions on gut microbial composition, and suggested the possibility for a preferential impact of other variables, probably related to the general life style on shaping human gut microbial community in spite of dietary influence. Consequently, research were individuals are categorized on the basis of their claimed feeding types is of limited use for scientific studies, since it appears to be oversimplified.

Bovine meat versus pork in Toxoplasma gondii transmission in Italy: A quantitative risk assessment model.

Toxoplasma gondii is a widespread zoonotic parasite with a high seroprevalence in the human population and the ability to infect almost all warm blooded animals. Humans can acquire toxoplasmosis from different transmission routes and food plays a critical role. Within the food category, meat is of utmost importance, as it may contain bradyzoites inside tissue cysts, which can potentially cause infection after ingestion if parasites are not inactivated through freezing or cooking before consumption. In Italy, the most commonly consumed meat-producing animal species are bovines and pigs. However, T. gondii prevalence and consumption habits for meat of these animal species are very different. There is debate within the scientific community concerning which of these animal species is the main source of meat-derived human toxoplasmosis. The aim of this work was to build a quantitative risk assessment model to estimate the yearly probability of acquiring toxoplasmosis infection due to consumption of bovine meat and pork (excluding cured products) in Italy, taking into account the different eating habits. The model was fitted with data obtained from the literature regarding: bradyzoite concentrations, portion size, dose-response relation, prevalence of T. gondii in bovines and swine, meat consumption and meat preparation habits. Alternative handling scenarios were considered. The model estimated the risk per year of acquiring T. gondii infection in Italy from bovine and swine meat to be 0.034% and 0.019%, respectively. Results suggest that, due to existing eating habits, bovine meat can be a not negligible source of toxoplasmosis in Italy.

Effectiveness of Washing Procedures in Reducing Salmonella enterica and Listeria monocytogenes on a Raw Leafy Green Vegetable (Eruca vesicaria).

Vegetables are an important source of nutrients, but they can host a large microbial population, particularly bacteria. Foodborne pathogens can contaminate raw vegetables at any stage of their production process with a potential for human infection. Appropriate washing can mitigate the risk of foodborne illness consequent to vegetable consumption by reducing pathogen levels, but few data are available to assess the efficacy of different practices. In the present work, six different washing methods, in the presence or absence of sanitisers (peracetic acid and percitric acid, sodium bicarbonate, sodium hypochlorite) and vinegar, were tested for their effectiveness in reducing Salmonella and Listeria counts after artificial contamination of raw rocket (Eruca vesicaria). Results showed that washing with sodium hypochlorite (200 mg/L) was the only method able to produce a significant 2 Log reduction of Salmonella counts, but only in the case of high initial contamination (7 Log CFU/g), suggesting potential harmful effects for consumers could occur. In the case of Listeria monocytogenes, all the examined washing methods were effective, with 200 mg/L sodium hypochlorite solution and a solution of peracetic and percitric acids displaying the best performances (2 and 1.5 Log reductions, respectively). This highlights the importance of targeting consumers on fit for purpose and safe washing practices to circumvent vegetable contamination by foodborne pathogens.

Testing nano-silver food packaging to evaluate silver migration and food spoilage bacteria on chicken meat.

Migration of nanomaterials from food containers into food is a matter of concern because of the potential risk for exposed consumers. The aims of this study were to evaluate silver migration from a commercially available food packaging containing silver nanoparticles into a real food matrix (chicken meat) under plausible domestic storage conditions and to test the contribution of such packaging to limit food spoilage bacteria proliferation. Chemical analysis revealed the absence of silver in chicken meatballs under the experimental conditions in compliance with current European Union legislation, which establishes a maximum level of 0.010 mg kg(-1) for the migration of non-authorised substances through a functional barrier (Commission Regulation (EU) No. 10/2011). On the other hand, microbiological tests (total microbial count, Pseudomonas spp. and Enterobacteriaceae) showed no relevant difference in the tested bacteria levels between meatballs stored in silver-nanoparticle plastic bags or control bags. This study shows the importance of testing food packaging not only to verify potential silver migration as an indicator of potential nanoparticle migration, but also to evaluate the benefits in terms of food preservation so as to avoid unjustified usage of silver nanoparticles and possible negative impacts on the environment.

Silver As Antibacterial toward Listeria monocytogenes.

Listeria monocytogenes is a serious foodborne pathogen that can contaminate food during processing and can grow during food shelf-life. New types of safe and effective food contact materials embedding antimicrobial agents, like silver, can play an important role in the food industry. The present work aimed at evaluating the in vitro growth kinetics of different strains of L. monocytogenes in the presence of silver, both in its ionic and nano form. The antimicrobial effect was determined by assaying the number of culturable bacterial cells, which formed colonies after incubation in the presence of silver nanoparticles (AgNPs) or silver nitrate (AgNO3). Ionic release experiments were performed in parallel. A different reduction of bacterial viability between silver ionic and nano forms was observed, with a time delayed effect exerted by AgNPs. An association between antimicrobial activity and ions concentration was shown by both silver chemical forms, suggesting the major role of ions in the antimicrobial mode of action.

Possible influence of natural events on heavy metals exposure from shellfish consumption: a case study in the north-East of Italy.

The objective of this study was the estimation of the exposure over time to heavy metals (cadmium, mercury, and lead) due to shellfish consumption in the Veneto Region, Italy. Shellfish consumption was investigated by a food frequency consumption survey. Altogether, 1949 households, stratified into the five most populated areas of the Veneto Region, were involved in the study. Exposure estimation to heavy metals was carried out taking into account the level of metal measured in samples of Manila clams (Ruditapes philippinarum) and grooved carpet shell (Ruditapes decussatus), collected in the frame of the monitoring activities of mollusk production areas of Veneto Region, between January 2007 and December 2012. A general high contribution of the considered shellfish to the Tolerable Weekly Intake was noticed in the case of cadmium, especially in 2011, when a considerable increase in cadmium intake was estimated. This was probably due to a heavy rainfall event that triggered catastrophic flooding with high impact on shellfish capture areas in November 2010. The results strongly emphasize the importance of dealing with food safety in a holistic way, taking into account the potential impact of extraordinary natural events on food chain contamination, in order to identify food hazards at an early stage, before developing into a real risk for consumers.