Effect of different doses of camelina cake inclusion as a substitute of dietary soyabean meal on growth performance and gut health of weaned pigs.

Camelina cake (CAM) is a co-product proposed as an alternative protein source; however, piglet data are still limited. This study aimed to evaluate the effect of different doses of CAM in substitution of soyabean meal on the growth, health and gut health of weaned pigs. At 14 d post-weaning (d0), sixty-four piglets were assigned either to a standard diet or to a diet with 4 %, 8 % or 12 % of CAM. Piglets were weighed weekly. At d7 and d28, faeces were collected for microbiota and polyamine and blood for reactive oxygen metabolites (ROM) and thyroxine analysis. At d28, pigs were slaughtered, organs were weighed, pH was recorded on gut, colon was analysed for volatile fatty acids (VFA) and jejunum was used for morphological and gene expression analysis. Data analysis was carried out using a mixed model including diet, pen and litter as factors; linear and quadratic contrasts were tested. CAM linearly reduced the average daily gain from d0-d7, d0-d14, d0-d21 and d0-d28 (P ≤ 0·01). From d0-d7 increasing CAM linearly decreased feed intake (P = 0·04) and increased linearly the feed to gain (P = 0·004). CAM increased linearly the liver weight (P < 0·0001) and affected the cadaverine (P < 0·001). The diet did not affect the ROM, thyroxine, intestinal pH, VFA and morphology. All doses of CAM increased the α diversity indices at d28 (P < 0·05). CAM at 4 % promoted the abundance of Butyricicoccaceae_UCG-008. Feeding with CAM enhanced resilience in the gut microbiome and can be evaluated as a potential alternative protein source with dose-dependent limitations on piglet growth performance.

Clinical evaluation and microbiota analysis in 9 dogs with antibiotic-responsive enteropathy: A prospective comparison study.

BACKGROUND: Antibiotic-responsive enteropathy (ARE) is diagnosed by excluding other causes of diarrhea and when there is a short-term response to administration of antibiotics. OBJECTIVES: To characterize the gut microbiota and clinical trend of dogs with suspected ARE and to evaluate the variation in microbiota before (T0), after 30 days (T30) of tylosin treatment, and 30 days after discontinuation of treatment (T60). A further objective was to evaluate whether changes in gut microbiota are related to relapses of diarrhea when the therapy is tapered. ANIMALS: Study sample (group A) was composed of 15 dogs with chronic diarrhea, group B was composed of 15 healthy dogs. Group A was given tylosin for 30 days. METHODS: A multicentric prospective study. Clinical Indexes, fecal score, and samples for microbiota analysis were collected at T0, T30, and T60 in group A and T0 and T30 in group B. The gut microbiota was analyzed via 16S ribosomal RNA gene. Qiime2 version 2020.2 was used to perform bioinformatic analyses, and Alpha- and Beta-diversity were computed. RESULTS: Diarrhea recurred after T30 in 9 of 14 dogs, which were classified as affected by ARE. At T0, a difference was noted in the beta-diversity between groups (Bray Curtis metric P = .006). A T0-T30 difference in alpha-diversity was noted in group A (Shannon index P = .001, Faith PD P = .007). CONCLUSIONS AND CLINICAL IMPORTANCE: Although tylosin influences the microbiota of dogs with ARE, we failed to find any specific characteristic in the microbiota of dogs with ARE.

Effect of 30 days of ketogenic Mediterranean diet with phytoextracts on athletes' gut microbiome composition.

Background: Recent research suggest that gut microbiome may play a fundamental role in athlete's health and performance. Interestingly, nutrition can affect athletic performance by influencing the gut microbiome composition. Among different dietary patterns, ketogenic diet represents an efficient nutritional approach to get adequate body composition in athletes, however, some concerns have been raised about its potential detrimental effect on gut microbiome. To the best of our knowledge, only one study investigated the effect of ketogenic diet on the gut microbiome in athletes (elite race walkers), whilst no studies are available in a model of mixed endurance/power sport such as soccer. This study aimed to investigate the influence of a ketogenic Mediterranean diet with phytoextracts (KEMEPHY) diet on gut microbiome composition in a cohort of semi-professional soccer players. Methods: 16 male soccer players were randomly assigned to KEMEPHY diet (KDP n = 8) or western diet (WD n = 8). Body composition, performance measurements and gut microbiome composition were measured before and after 30 days of intervention by 16S rRNA amplicon sequencing. Alpha-diversity measures and PERMANOVA was used to investigate pre-post differences in the relative abundance of all taxonomic levels (from phylum to genus) and Spearman's correlations was used to investigate associations between microbial composition and macronutrient intake. Linear discriminant analysis was also performed at the different taxonomic levels on the post-intervention data. Results: No differences were found between pre and post- dietary intervention for microbial community diversity: no significant effects of time (p = 0.056, ES = 0.486 and p = 0.129, ES = 0.388, respectively for OTUs number and Shannon's ENS), group (p = 0.317, ES = 0.180 and p = 0.809, ES = 0.047) or time × group (p = 0.999, ES = 0.01 and p = 0.230, ES = 0.315). Post-hoc paired Wilcoxon test showed a significant time × group effect for Actinobacteriota (p = 0.021, ES = 0.578), which increased in the WD group (median pre: 1.7%; median post: 2.3%) and decreased in the KEMEPHY group (median pre: 4.3%; median post: 1.7%). At genus level, the linear discriminant analysis in the post intervention differentiated the two groups for Bifidobacterium genus (pertaining to the Actinobacteria phylum), Butyricicoccus and Acidaminococcus genera, all more abundant in the WD group, and for Clostridia UCG-014 (order, family, and genus), Butyricimonas, Odoribacterter genera (pertaining to the Marinifilaceae family), and Ruminococcus genus, all more abundant in the KEMEPHY group. Conclusions: Our results demonstrate that 30 days of KEMEPHY intervention, in contrast with previous research on ketogenic diet and gut microbiome, do not modify the overall composition of gut microbiome in a cohort of athletes. KEMEPHY dietary pattern may represent an alternative and safety tool for maintaining and/or regulating the composition of gut microbiome in athletes practicing regular exercise. Due to the fact that not all ketogenic diets are equal, we hypothesized that each version of ketogenic diet, with different kind of nutrients or macronutrients partitioning, may differently affect the human gut microbiome.

Salivary microbiota composition may discriminate between patients with eosinophilic oesophagitis (EoE) and non-EoE subjects.

BACKGROUND: Data on the role of the microbiome in adult patients with eosinophilic oesophagitis (EoE) are limited. AIMS: To prospectively collect and characterise the salivary, oesophageal and gastric microbiome in patients with EoE, further correlating the findings with disease activity. METHODS: Adult patients with symptoms of oesophageal dysfunction undergoing upper endoscopy were consecutively enrolled. Patients were classified as EoE patients, in case of more than 15 eosinophils per high-power field, or non-EoE controls, in case of lack of eosinophilic infiltration. Before and during endoscopy, saliva, oesophageal and gastric fundus biopsies were collected. Microbiota assessment was performed by 16 s rRNA analysis. A Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was implemented to identify biomarkers. RESULTS: Saliva samples were collected from 29 EoE patients and 20 non-EoE controls;, biopsies from 25 EoE and 5 non-EoE controls. In saliva samples, 23 Amplicon Sequence Variants (ASVs) were positively associated with EoE and 27 ASVs with controls, making it possible to discriminate between EoE and non-EoE patients with a classification error (CE) of 24%. In a validation cohort, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of this model were 78.6%, 80%, 75%, 80% and 60%, respectively. Moreover, the analysis of oesophageal microbiota samples observed a clear microbial pattern able to discriminate between active and inactive EoE (CE = 8%). CONCLUSION: Our preliminary data suggest that salivary metabarcoding analysis in combination with machine learning approaches could become a valid, cheap, non-invasive test to segregate between EoE and non-EoE patients.

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.

Lactic Acid Bacteria Adjunct Cultures Exert a Mitigation Effect against Spoilage Microbiota in Fresh Cheese.

Lactic acid bacteria (LAB) have a strong mitigation potential as adjunct cultures to inhibit undesirable bacteria in fermented foods. In fresh cheese with low salt concentration, spoilage and pathogenic bacteria can affect the shelf life with smear on the surface and packaging blowing. In this work, we studied the spoilage microbiota of an Italian fresh cheese to find tailor-made protective cultures for its shelf life improvement. On 14-tested LAB, three of them, namely Lacticaseibacillus rhamnosus LRH05, Latilactobacillus sakei LSK04, and Carnobacterium maltaromaticum CNB06 were the most effective in inhibiting Gram-negative bacteria. These cultures were assessed by the cultivation-dependent and DNA metabarcoding approach using in vitro experiments and industrial trials. Soft cheese with and without adjunct cultures were prepared and stored at 8 and 14 °C until the end of the shelf life in modified atmosphere packaging. Data demonstrated that the use of adjunct cultures reduce and/or modulate the growth of spoilage microbiota at both temperatures. Particularly, during industrial experiments, C. maltaromaticum CNB06 and Lcb. rhamnosus RH05 lowered psychrotrophic bacteria of almost 3 Log CFU/g in a 5-week stored cheese. On the contrary, Llb. sakei LSK04 was able to colonize the cheese but it was not a good candidate for its inhibition capacity. The combined approach applied in this work allowed to evaluate the protective potential of LAB strains against Gram-negative communities.

A Multi-Omics Approach to Evaluate the Quality of Milk Whey Used in Ricotta Cheese Production.

In the past, milk whey was only a by-product of cheese production, but currently, it has a high commercial value for use in the food industries. However, the regulation of whey management (i.e., storage and hygienic properties) has not been updated, and as a consequence, its microbiological quality is very challenging for food safety. The Next Generation Sequencing (NGS) technique was applied to several whey samples used for Ricotta production to evaluate the microbial community composition in depth using both RNA and DNA as templates for NGS library construction. Whey samples demonstrating a high microbial and aerobic spore load contained mostly Firmicutes; although variable, some samples contained a relevant amount of Gammaproteobacteria. Several lots of whey acquired as raw material for Ricotta production presented defective organoleptic properties. To define the volatile compounds in normal and defective whey samples, a headspace gas chromatography/mass spectrometry (GC/MS) analysis was conducted. The statistical analysis demonstrated that different microbial communities resulted from DNA or cDNA library sequencing, and distinguishable microbiota composed the communities contained in the organoleptic-defective whey samples.