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.
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.
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.
Summary 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.
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