Cobalamin deficiency is believed to be related to disturbances in cell division, neuropathy, nervous system disease and pernicious anemia. Elderly people are usually advised to supplement their diets with cobalamin. As cobalamin has several forms, the effects of different forms of cobalamin on gut microbiota were investigated in this study. After 7 days of supplementation, methylcobalamin had reduced the diversity of gut microbiota compared to that in the control and cyanocobalamin groups. After supplementation with methylcobalamin, the percentage of Acinetobacter spp. had increased to 45.54%, while the percentages of Bacteroides spp., Enterobacteriaceae spp. and Ruminococcaceae spp. had declined to 11.15, 9.34, and 2.69%, respectively. However, cyanocobalamin had different influences on these bacteria. Both cobalamins increased the amount of short-chain fatty acids, particularly butyrate and propionic acid. The cyanocobalamin group showed increased activity of cellulase compared with that in the other two groups. According to CCA and PICRUSt analysis, methylcobalamin had a positive correlation with Pseudomonas bacteria, propionic acid, and butyrate. Methylcobalamin promoted lipid, terpenoid, and polyketide metabolism by gut bacteria, promoted the degradation of exogenous substances, and inhibited the synthesis of transcription factors and secondary metabolites. Our results indicate that the various forms of cobalamin in the food industry should be monitored and regulated, because the two types of cobalamin had different effects on the gut microbiome and on microbial metabolism, although they have equal bio-activity in humans. Given the effects of methylcobalamin on gut microbiota and microbial metabolism, methylcobalamin supplementation should be suggested as the first option.
We propose a high-order finite difference weighted ENO (WENO) method for the ideal magnetohydrodynamics (MHD) equations. The proposed method is single-stage (i.e., it has no internal stages to store), single-step (i.e., it has no time history that needs to be stored), maintains a discrete divergence-free condition on the magnetic field, and has the capacity to preserve the positivity of the density and pressure. To accomplish this, we use a Taylor discretization of the Picard integral formulation (PIF) of the finite difference WENO method proposed in [SINUM, 53 (2015[SINUM, 53 ( ), pp. 1833[SINUM, 53 ( -1856, where the focus is on a high-order discretization of the fluxes (as opposed to the conserved variables). We use the version where fluxes are expanded to third-order accuracy in time, and for the fluid variables space is discretized using the classical fifth-order finite difference WENO discretization. We use constrained transport in order to obtain divergence-free magnetic fields, which means that we simultaneously evolve the magnetohydrodynamic (that has an evolution equation for the magnetic field) and magnetic potential equations alongside each other, and set the magnetic field to be the (discrete) curl of the magnetic potential after each time step. In this work, we compute these derivatives to fourth-order accuracy. In order to retain a single-stage, single-step method, we develop a novel Lax-Wendroff discretization for the evolution of the magnetic potential, where we start with technology used for Hamilton-Jacobi equations in order to construct a non-oscillatory magnetic field. The end result is an algorithm that is similar to our previous work [JCP, 268, (2014), pp. 302-325], but this time the time stepping is replaced through a Taylor method with the addition of a positivitypreserving limiter. Finally, positivity preservation is realized by introducing a parameterized flux limiter that considers a linear combination of high and low-order numerical fluxes. The choice of the free parameter is then given in such a way that the fluxes are limited towards the low-order solver until positivity is attained. Given the lack of additional degrees of freedom in the system, this positivity limiter lacks energy conservation where the limiter turns on. However, this ingredient can be dropped for problems where the pressure does not become negative. We present two and three dimensional numerical results for several standard test problems including a smooth Alfvén wave (to verify formal order of accuracy), shock tube problems (to test the shock-capturing ability of the scheme), Orszag-Tang, and cloud shock interactions. These results assert the robustness and verify the high-order of accuracy of the proposed scheme.
Background Xylitol, a white or transparent polyol or sugar alcohol, is digestible by colonic microorganisms and promotes the proliferation of beneficial bacteria and the production of short-chain fatty acids (SCFAs), but the mechanism underlying these effects remains unknown. We studied mice fed with 0%, 2% (2.17 g/kg/day), or 5% (5.42 g/kg/day) (weight/weight) xylitol in their chow for 3 months. In addition to the in vivo digestion experiments in mice, 3% (weight/volume) (0.27 g/kg/day for a human being) xylitol was added to a colon simulation system (CDMN) for 7 days. We performed 16S rRNA sequencing, beneficial metabolism biomarker quantification, metabolome, and metatranscriptome analyses to investigate the prebiotic mechanism of xylitol. The representative bacteria related to xylitol digestion were selected for single cultivation and co-culture of two and three bacteria to explore the microbial digestion and utilization of xylitol in media with glucose, xylitol, mixed carbon sources, or no-carbon sources. Besides, the mechanisms underlying the shift in the microbial composition and SCFAs were explored in molecular contexts. Results In both in vivo and in vitro experiments, we found that xylitol did not significantly influence the structure of the gut microbiome. However, it increased all SCFAs, especially propionate in the lumen and butyrate in the mucosa, with a shift in its corresponding bacteria in vitro. Cross-feeding, a relationship in which one organism consumes metabolites excreted by the other, was observed among Lactobacillus reuteri, Bacteroides fragilis, and Escherichia coli in the utilization of xylitol. At the molecular level, we revealed that xylitol dehydrogenase (EC 1.1.1.14), xylulokinase (EC 2.7.1.17), and xylulose phosphate isomerase (EC 5.1.3.1) were key enzymes in xylitol metabolism and were present in Bacteroides and Lachnospiraceae. Therefore, they are considered keystone bacteria in xylitol digestion. Also, xylitol affected the metabolic pathway of propionate, significantly promoting the transcription of phosphate acetyltransferase (EC 2.3.1.8) in Bifidobacterium and increasing the production of propionate. Conclusions Our results revealed that those key enzymes for xylitol digestion from different bacteria can together support the growth of micro-ecology, but they also enhanced the concentration of propionate, which lowered pH to restrict relative amounts of Escherichia and Staphylococcus. Based on the cross-feeding and competition among those bacteria, xylitol can dynamically balance proportions of the gut microbiome to promote enzymes related to xylitol metabolism and SCFAs.
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