Limosilactobacillus reuteri is a microorganism with valuable probiotic qualities that has been widely employed in humans to promote health. It is a well-studied probiotic bacterium that exerts beneficial health effects due to several metabolic mechanisms that enhance the production of anti-inflammatory cytochines and modulate the gut microbiota by the production of antimicrobial molecules, including reuterin. This review provides an overview of the data that support the role of probiotic properties, and the antimicrobial and immunomodulatory effects of some L. reuteri strains in relation to their metabolite production profile on the amelioration of many diseases and disorders. Although the results discussed in this paper are strain dependent, they show that L. reuteri, by different mechanisms and various metabolites, may control body weight and obesity, improve insulin sensitivity and glucose homeostasis, increase gut integrity and immunomodulation, and attenuate hepatic disorders. Gut microbiota modulation by ingesting probiotic L. reuteri strains could be a promising preventative and therapeutic approach against many diseases and disorders.

Obesity is a complex syndrome and is recognized as the ultimate pathway of many chronic diseases. Studies using Akkermansia muciniphila supplementation strategy have proved to be effective for the prevention and treatment of obesity and other metabolic disorders. Although there are studies that support the protective effect of this strategy, the effects on the prevention of obesity on humans are not clear yet and need more investigation. The aim of this study is to investigate the effect of A. muciniphila administration on modulating obesity. This systematic review was generated from articles published within the last 10 years. All articles were in English and included animal subjects. The review relied on the search engines Google Scholar, Pub Med, Web of Science and Medline using the following keywords: A. muciniphila, next-generation probiotic, new-generation probiotic, obesity, fat mass, body fat and lipid profile. The search has revealed 804 articles with relevant key words. After the exclusion of irrelevant articles, 10 studies were selected based on the criteria. These studies were randomized controlled trials that have shown that A. muciniphila modulates obesity by regulating metabolism and energy hemostasis and improving insulin sensitivity and glucose hemostasis. In addition, studies showed this microorganism enhances low grade inflammation by different mechanisms.

Background: Quorum sensing (QS) is a cell-to-cell communication mechanism that occurs between inter- and intra-bacterial species and is regulated by signaling molecules called autoinducers (AIs). It has been suggested that probiotics can exert a QS inhibitory effect through their metabolites. Purpose: To provide an overview of (1) the anti-QS activity of probiotics and its mechanism against foodborne pathogenic and spoilage bacteria; (2) the potential role of the QS of probiotics in gut health; and (3) the impact of microencapsulation on QS. Results: Lactobacillus species have been extensively studied for their anti-QS activity and have been found to effectively disrupt QS in vitro. However, their effectiveness in a food matrix is yet to be determined as they interfere with the AI receptor or its synthesis. QS plays an important role in both the biofilm formation of probiotics and pathogenic bacteria. Moreover, in vitro and animal studies have shown that QS molecules can modulate cytokine responses and gut dysbiosis and maintain intestinal barrier function. In this scenario, microencapsulation was found to enhance AI activity. However, its impact on the anti-QS activity of probiotics and its underlying mechanism remains unclear. Conclusions: Probiotics are potential candidates to block QS activity in foodborne pathogenic and food spoilage bacteria. Microencapsulation increases QS efficacy. However, more research is still needed for the identification of the QS inhibitory metabolites from probiotics and for the elucidation of the anti-QS mechanism of probiotics (microcapsules and free cells) in food and the human gut.

Two strains of γ-aminobutyric acid (GABA) producing bacteria, L. brevis Y1 and L. plantarum LM2, were microencapsulated in sodium alginate with two concentrations (1% and 2%) of monosodium glutamate (MSG) by using vibrating technology. The mix of both species was microencapsulated both in fresh and freeze-dried form. After 0, 1, 2, and 4 weeks of storage at 4 °C in quarter strength Ringer’s solution, the microcapsules were subjected to cell viable counting and sub-cultured in MRS at 37° for 24 h. The MRS cultures were analyzed for the GABA content. The amount of GABA produced per CFU of MRS inoculum was then calculated. Only the 4-week-old microcapsules were used to inoculate a chocolate milk drink with the aim of obtaining a functionalized drink containing viable probiotic cells and GABA after a 1-week incubation at 4 °C. Therefore, the GABA production in chocolate milk per CFU of the probiotic culture after the incubation time was calculated. Results of the GABA analysis by liquid chromatography mass spectrometry of the MRS sub-cultures showed no significant difference (p > 0.05) in GABA yield between 1% and 2% MSG for the microcapsules containing fresh cells. On the contrary, a significant difference (p < 0.05) in productivity along the storage was registered. Microcapsules containing freeze-dried cells showed significant differences (p < 0.05) in GABA yield between 1% and 2% MSG only after 2 and 4 weeks of storage. A significant difference (p < 0.05) in GABA yield between the storage time was found only for the trials with 2% MSG for freeze-dried cells. The synthesis of GABA in chocolate milk significantly decreased (p < 0.05) only for fresh cells when comparing 2% with 1% MSG. In conclusion, a 1-month storage of microcapsules containing both culture forms, fresh and freeze-dried, did not affect GABA production.

Background and Objectives: The gut microbiome composition has emerged as a potential contributor to metabolic health and it is influenced by several factors, such as dietary factors. Individuals with type 1 diabetes (T1D) experience metabolic dysregulation, including alterations in body weight; as a result, the prevalence of overweight/obesity is increasing in this population. Limited research has addressed the role of the gut microbiota on body weight in people with T1D. Our aim is to evaluate the association between BMI and gut microbiome composition in T1D patients, also exploring the relationship between dietary factors and the microbiota. Methods: A cross-sectional study was conducted involving T1D patients (n = 101) of both sexes, aged 18–79 years. Anthropometric parameters were measured, and the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire was administered to evaluate dietary habits. Patients collected stool samples that were analyzed by shotgun metagenomics sequencing for the evaluation of the gut microbiota composition. Associations between BMI, gut microbiome composition, and dietary factors were evaluated by Pearson’s bivariate correlation. Results: BMI was correlated inversely with the Bacilli (r = −0.296, p = 0.004) and Gammaproteobacteria (r = −0.280, p = 0.009) classes and directly with the abundance of the Clostridia class (r = 0.220, p = 0.031) and one of its species Faecalibacterium prausnitzii (r = 0.264, p = 0.010). The presence of these taxa was associated with dietary factors: Bacilli was inversely correlated with the consumption of animal protein (r = −0.242, p = 0.019), monounsaturated fatty acids (r = −0.214, p = 0.038), linolenic acid (r = −0.236, p = 0.022), oleic acid (r = −0.205, p = 0.048), and cholesterol (r = −0.204, p = 0.048); Faecalibacterium prausnitzii was directly associated with the intake of cholesterol (r = 0.218, p = 0.034) and simple sugars (r = 0.226, p = 0.028). Clostridia was correlated directly (r = 0.225, p = 0.027) and Gammaproteobacteria inversely (r = −0.216, p = 0.045) with alcohol intake. Discussion: BMI was associated with the Clostridia, Bacilli, and Gammaproteobacteria classes. These bacteria were related to various dietary factors. Therefore, changes in the gut microbiota could be a possible link between dietary habits and overweight/obesity in people with T1D.

Background: Blood glucose control remains a challenge for type 1 diabetes (T1D) patients. Previous studies have shown an association between gut microbiota composition and T1D pathogenesis. However, little is known about the composition of the gut microbiota and its association with host blood glucose control and diet in people with T1D. Objective: We explored the relationship of gut microbiome composition with blood glucose control and dietary intake in people with T1D. Research design and methods: In a cross-sectional study, a metagenomic shotgun sequencing analysis of the gut microbiome obtained from fecal samples was performed in 101 individuals with T1D. Dietary intakes were assessed by using the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire. Blood glucose control was assessed by continuous glucose monitoring and expressed as time-in-range (TIR), time spent in the blood glucose interval 70–180 mg/dL. Spearman’s correlation analysis was used to determine the correlation between gut microbiota composition, blood glucose control, and dietary intake. Results: TIR correlated positively with the abundance of Bacilli (r = 0.258, p = 0.027) and negatively with the Lachnospiraceae family (r = −0.238, p = 0.024), Mediterraneibacter (r = −0.249, p = 0.034), Coprococcus genus (r = −0.286, p = 0.016), Coprococcus comes (r = −0.257, p = 0.028), and Ruminococcus torques (r = −0.261, p = 0.026). The presence of these taxa was associated with the intake of various foods: Bacilli correlated positively with dairy products (r = 0.307, p = 0.002) and negatively with olive oil (r = −0.207, p = 0.041) and meat products (r = −0.255, p = 0.012). Lachnospiraceae correlated negatively with cereals (r = −0.263, p = 0.009). Mediterraneibacter correlated positively with meat and meat products (r = 0.230, p = 0.023). Ruminococcus torques correlated negatively with fruit intake (r = −0.227, p = 0.025). Discussion: Our findings highlight that gut microbiota composition may be related to blood glucose control in T1D and dietary factors may have a role in this interplay. Further investigations are needed to address whether these findings are causally linked and whether to target these gut microbiota taxa.