Lactobacillus acidophilus DDS-1 Modulates the Gut Microbial Co-Occurrence Networks in Aging Mice

Efficacy of probiotics in pediatric atopic dermatitis: A systematic review and meta-analysis

BACKGROUND

Atopic dermatitis (AD) is a prevailing skin disease in childhood. Several studies have appraised probiotics as a strategy for treating AD. We aimed to assess the validity of probiotics in the treatment of AD in children.

METHODS

We systematically searched the PubMed/MEDLINE, Embase, Scopus, EBSCO, Web of Science and Cochrane library databases for randomized controlled trials (RCTs) that assessed the effect of probiotic treatment on SCORAD value in pediatric patients with AD compared with a placebo group between 1 January 2010 and 1 January 2023. The risk of bias and the certainty of evidence were assessed using Cochrane ROB 2.0.

RESULTS

A total of 10 outcomes from 9 RCTs involving 1000 patients were included. Three of these outcomes were analyzed as dichotomous variables in 373 patients. The other seven were analyzed for continuous variables in 627 patients. A meta-analysis of the random-effect model of the dichotomous variables demonstrated no significant difference between the probiotic and control groups [OR = 1.75, 95% confidence interval (CI) (0.70, 4.35), p = 0.23, I²  = 68%]. A meta-analysis of the random-effect model of continuous variables demonstrated significant differences between the probiotic and control groups [MD = -4.24, 95% CI (-7.78, -0.71), p = 0.002, I²  = 71%]. Subgroup analysis of continuous variables showed that the effects of children's age, treatment duration and probiotic species on the SCORAD value were not statistically significant.

CONCLUSION

Evidence on the improvement effect of probiotics on pediatric patients with AD is limited. This study showed that single-strain probiotic treatment exerts a positive effect on AD. Restricted to the quantity and quality of incorporated studies, these conclusions have yet to be validated by high-quality studies.

Deciphering the potential role of Maca compounds prescription influencing gut microbiota in the management of exercise-induced fatigue by integrative genomic analysis

A growing number of nutraceuticals and cosmeceuticals have been utilized for millennia as anti-fatigue supplements in folk medicine. However, the anti-fatigue mechanism underlying is still far from being clearly explained. The aim of the study is to explore the underlying mechanism of the Maca compound preparation (MCP), a prescription for management of exercise-induced fatigue. In this study, mice weight-loaded swimming test was used to evaluate the anti-fatigue effect of MCP. MCP significantly improved the forelimb grip strength and Rota-rod test in behavioral tests via regulating energy metabolism. 16S rDNA sequencing results showed MCP can regulate the intestinal flora at the genus level by increasing several beneficial bacteria (i.e., Lactobacillus, Akkermansia and etc.), and decreasing the harmful bacteria (i.e., Candidatus_Planktophila and Candidatus_Arthromitus), where notable high relevance was observed between the fatigue-related biomarkers and fecal microbiota. The results of microbial function analysis suggested that MCP might improve exercise-induced fatigue by enhancing energy metabolism, carbohydrate and lipid metabolism and metabolism of terpenoids and polyketides and breakdown of amino acid metabolism. In addition, and H₂O₂-induced oxidative stress model on C2C12 cells was employed to further validate the regulation of MCP on energy metabolisms. MCP pre-treatment significantly reduced intracellular ROS accumulation, and increased glycogen content, ATP generation capacity and mitochondrial membrane potential of skeletal muscle cells, as well as conferred anti-cell necrosis ability. In conclusion, MCP plays a key role in regulating fatigue occurrence in exercising and gut microbiota balance, which may be of particular importance in the case of manual workers or sub-healthy populations.

Species abundance correlations carry limited information about microbial network interactions

Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation mostly coincided with the nature of the strongest pairwise interaction, but this is not necessarily the case. For instance, under rare conditions of identical interaction strength, we found that competitive and exploitative interactions can result in positive as well as negative correlations. Thus, cross-sectional abundance data carry limited information on specific interaction types. Correlations in abundance may hint at interactions but require independent validation.

The bacteria in and on our body (the human microbiome) largely determine how our body functions, and whether we stay healthy or get sick. These bacteria do not live on their own, but interact among each other and with their human host. Finding out which bacteria interact with each other is cumbersome, but patterns of joint occurrence between species might provide a clue to their ecological dependencies. We investigated whether correlations in species abundance can be used for the purpose of ecological network reconstruction. We simulated different bacterial communities with known interactions according to a theoretical population model. After having collected virtual samples from our simulated data, we performed a correlation analysis and then compared the correlation network with our known interaction network. We found that correlations can be informative for underlying interactions, but ecological conclusions should be drawn carefully. An obvious limitation of correlation analysis is that direction of interaction cannot be recovered from co-occurrence data, making correlations insensitive for detection of asymmetric interactions. In addition, we found that competitive and exploitative interactions can induce positive as well as negative correlations. We recommend careful interpretation and validation when inferring networks from cross-sectional abundance data.