The gut microbiome–Does stool represent right?

Are all sugars equal? Role of the food source in physiological responses to sugars with an emphasis on fruit and fruit juice

High (free) sugar intakes can increase self-reported energy intake and are associated with unfavourable cardiometabolic health. However, sugar source may modulate the effects of sugars due to several mechanisms including the food matrix. The aim of this review was to assess the current state of evidence in relation to food source effects on the physiological responses to dietary sugars in humans relevant to cardiometabolic health. An additional aim was to review potential mechanisms by which food sources may influence such responses. Evidence from meta-analyses of controlled intervention trials was used to establish the balance of evidence relating to the addition of sugars to the diet from sugar-sweetened beverages, fruit juice, honey and whole fruit on cardiometabolic outcomes. Subsequently, studies which have directly compared whole fruit with fruit juices, or variants of fruit juices, were discussed. In summary, the sources of sugars can impact physiological responses, with differences in glycaemic control, blood pressure, inflammation, and acute appetite. Longer-term effects and mechanisms require further work, but initial evidence implicates physical structure, energy density, fibre, potassium and polyphenol content, as explanations for some of the observed responses.

The effect of in vitro simulated colonic pH gradients on microbial activity and metabolite production using common prebiotics as substrates

BACKGROUND

The interplay between gut microbiota (GM) and the metabolization of dietary components leading to the production of short-chain fatty acids (SCFAs) is affected by a range of factors including colonic pH and carbohydrate source. However, there is still only limited knowledge on how the GM activity and metabolite production in the gastrointestinal tract could be influenced by pH and the pH gradient increases along the colon.

RESULTS

Here we investigate the effect of pH gradients corresponding to levels typically found in the colon on GM composition and metabolite production using substrates inulin, lactose, galactooligosaccharides (GOS), and fructooligosaccharide (FOS) in an in vitro colon setup. We investigated 3 different pH regimes (low, 5.2 increasing to 6.4; medium, 5.6 increasing to 6.8 and high, 6.0 increasing to 7.2) for each fecal inoculum and found that colonic pH gradients significantly influenced in vitro simulated GM structure, but the influence of fecal donor and substrate was more pronounced. Low pH regimes strongly influenced GM with the decreased relative abundance of Bacteroides spp. and increased Bifidobacterium spp. Higher in vitro simulated colonic pH promoted the production of SCFAs in a donor- and substrate-dependent manner. The butyrate producer Butyricimonas was enriched at higher pH conditions, where also butyrate production was increased for inulin. The relative abundance of Phascolarctobacterium, Bacteroides, and Rikenellaceae also increased at higher colonic pH, which was accompanied by increased production of propionate with GOS and FOS as substrates.

CONCLUSIONS

Together, our results show that colonic substrates such as dietary fibres influence GM composition and metabolite production, not only by being selectively utilized by specific microbes, but also because of their SCFA production, which in turn also influences colonic pH and overall GM composition and activity. Our work provides details about the effect of the gradients of rising pH from the proximal to distal colon on fermenting dietary substrates in vitro and highlights the importance of considering pH in GM research.

Gut microbiota in overweight and obesity: crosstalk with adipose tissue

Overweight and obesity are characterized by excessive fat mass accumulation produced when energy intake exceeds energy expenditure. One plausible way to control energy expenditure is to modulate thermogenic pathways in white adipose tissue (WAT) and/or brown adipose tissue (BAT). Among the different environmental factors capable of influencing host metabolism and energy balance, the gut microbiota is now considered a key player. Following pioneering studies showing that mice lacking gut microbes (that is, germ-free mice) or depleted of their gut microbiota (that is, using antibiotics) developed less adipose tissue, numerous studies have investigated the complex interactions existing between gut bacteria, some of their membrane components (that is, lipopolysaccharides), and their metabolites (that is, short-chain fatty acids, endocannabinoids, bile acids, aryl hydrocarbon receptor ligands and tryptophan derivatives) as well as their contribution to the browning and/or beiging of WAT and changes in BAT activity. In this Review, we discuss the general physiology of both WAT and BAT. Subsequently, we introduce how gut bacteria and different microbiota-derived metabolites, their receptors and signalling pathways can regulate the development of adipose tissue and its metabolic capacities. Finally, we describe the key challenges in moving from bench to bedside by presenting specific key examples.

Micro-DeMix: A mixture beta-multinomial model for investigating the fecal microbiome compositions

Extensive research has uncovered the involvement of the human gut microbiome in various facets of human health, including metabolism, nutrition, physiology, and immune function. Researchers often study fecal microbiota as a proxy for understanding the gut microbiome. However, it has been demonstrated that this approach may not suffice to yield a comprehensive understanding of the entire gut microbial community. Emerging research is revealing the heterogeneity of the gut microbiome across different gastrointestinal (GI) locations in both composition and functions. While spatial metagenomics approach has been developed to address these variations in mice, limitations arise when applying it to human-subject research, primarily due to its invasive nature. With these restrictions, we introduce Micro-DeMix, a mixture beta-multinomial model that decomposes the fecal microbiome at compositional level to understand the heterogeneity of the gut microbiome across various GI locations and extract meaningful insights about the biodiversity of the gut microbiome. Moreover, Micro-DeMix facilitates the discovery of differentially abundant microbes between GI regions through a hypothesis testing framework. We utilize the Inflammatory Bowel Disease (IBD) data from the NIH Integrative Human Microbiome Project to demonstrate the effectiveness and efficiency of the proposed Micro-DeMix.

Effects of Synbiotic Lacticaseibacillus paracasei, Bifidobacterium breve, and Prebiotics on the Growth Stimulation of Beneficial Gut Microbiota

The gut microbiota is a complex community of microorganisms that plays a vital role in maintaining overall health, and is comprised of Lactobacillus and Bifidobacterium. The probiotic efficacy and safety of Lacticaseibacillus paracasei and Bifidobacterium breve for consumption were confirmed by in vitro experiments. The survival rate of the probiotics showed a significant decline in in vitro gut tract simulation; however, the survival rate was more than 50%. Also, the probiotics could adhere to Caco-2 cell lines by more than 90%, inhibit the pathogenic growths, deconjugate glycocholic acid and taurodeoxycholic acid through activity of bile salt hydrolase (BSH) proteins, and lower cholesterol levels by over 46%. Regarding safety assessment, L. paracasei and B. breve showed susceptibility to some antibiotics but resistance to vancomycin and were examined as γ-hemolytic strains. Anti-inflammatory properties of B. breve with Caco-2 epithelial cell lines showed the significantly highest value (p < 0.05) for interleukin-10. Furthermore, probiotics and prebiotics (inulin, fructooligosaccharides, and galactooligosaccharides) comprise synbiotics, which have potential effects on the increased abundance of beneficial microbiota, but do not affect the growth of harmful bacteria in feces samples. Moreover, the highest concentration of short chain fatty acid was of acetic acid, followed by propionic and butyric acid.