Effect of Lactobacillus reuteri NCIMB 30351 drops on symptoms of infantile functional gastrointestinal disorders and gut microbiota in early infants: Results from a randomized, placebo-controlled clinical trial.

Infantile functional gastrointestinal disorders, such as colic, constipation, diarrhea, and gastroesophageal reflux (regurgitation), often occur in early infancy and, representing one of the causes of significant parental anxiety, lead to a significant strain on the healthcare resources. In this study, we aimed to evaluate the effects of Lactobacillus reuteri drops (L. reuteri NCIMB 30351) on the symptoms of infantile colic, constipation, diarrhea, and gastroesophageal reflux, as well as on the levels of intestinal microbiota in full-term newborns during the first months of life. A randomized, placebo-controlled, single-masked (blinded), post-marketing clinical study was conducted in two clinical units-Children's City Clinical Hospital of Moscow and Medical Center "St. Andrew's Hospitals-NEBOLIT" from March 2020 to May 2022 in 90 infants aged from 1 to 4 months (mean age (± SD) 12.3 ± 5.09 weeks; 53.3% females, 46.7% males). Patients with colic, regurgitation (single symptom or combination of several symptoms), and constipation or diarrhea were randomly allocated in two parallel arms to receive either 5 drops (2 × 108 colony forming unit) of L. reuteri NCIMB 30351 (n = 60) or masked placebo (n = 30) for 25 consecutive days. Two treatment arms had equal numbers of patients with constipation and diarrhea (n = 30 each). Daily crying times and their duration, evacuations, and regurgitations were recorded in a structured diary. The levels of gut microbiota were analyzed by deep sequencing of bacterial 16S rRNA gene. Infants with colic receiving supplementary L. reuteri NCIMB 30351 for 25 days had significant reduction in the numbers of colic (change from baseline - 6.3 (7.34) vs - 3.0 (7.29) in placebo, P < 0.05) and numbers of crying cases and mean duration of crying (decrease from baseline - 144 (70.7) minutes, lower in the diarrhea subgroup than in constipation infants, compared with - 80 (58.9) in placebo, P < 0.0001), as well as regurgitation numbers (decreased by - 4.8 (2.49) with L. reuteri vs - 3 (7.74) with placebo). We also observed increased numbers of evacuations in infants with constipation (L. reuteri 2.2 (2.4) vs 0.9 (1.06) in placebo, P < 0.05). There was a remarkable reduction of evacuations in infants with diarrhea, while not statistically significant. The analysis of bacterial 16S rRNA gene in the collected samples showed that L. reuteri positively influences the proportions of prevalent species, while it negatively affects both conditionally pathogenic and commensal microbes. Additional in vitro test for formation of Clostridium colonies in the presence of the probiotic demonstrated that L. reuteri effectively inhibits the growth of pathogenic Clostridium species. No adverse events were reported in this study.   Conclusion: The uptake of L. reuteri NCIMB 30351 leads to a significant reduction in the number of regurgitations, feeding-induced constipations, and diarrhea as well as mean daily numbers of crying and crying duration in infants during the first months of life. Our results suggest that L. reuteri NCIMB 30351 represents a safe and effective treatment for colic in newborns.  Trial registration: ClinicalTrials.gov : NCT04262648. What is Known: • Infantile functional gastrointestinal disorders, such as colic, constipation, diarrhea, and gastroesophageal reflux (regurgitation), often occur in early infancy and, represent one of the causes of significant parental anxiety. • A number of studies have shown that both the composition and diversity of the intestinal microbiota play important roles in the development and function of the gastrointestinal tract. What is New: • The uptake of L. reuteri NCIMB 30351 leads to a significant reduction in the number of regurgitations, feeding-induced constipations, and diarrhea as well as mean daily numbers of crying and crying duration in infants during the first months of life. • L. reuteri positively influences the proportions of prevalent species, while it negatively affects both conditionally pathogenic and commensal microbes in gut microbiota.

The hallmarks of dietary intervention-resilient gut microbiome.

Maintaining equilibrium of the gut microbiome is crucial for human health. Diet represents an important and generally accessible natural channel of controlling the nutrients supply to the intestinal microorganisms. Although many studies showed that dietary interventions can specifically modulate gut microbiome composition, further progress of the approach is complicated by interindividual variability of the microbial community response. The reported causes of this variability include the baseline microbiome composition features, but it is unclear whether any of them are intervention-specific. Here, we applied a unified computational framework to investigate the variability of microbiome response measured as beta diversity in eight various dietary interventions using previously published 16S rRNA sequencing datasets. We revealed a number of baseline microbiome features which determine the microbiome response in an intervention-independent manner. One of the most stable associations, reproducible for different interventions and enterotypes, was a negative dependence of the response on the average number of genes per microorganism in the community-an indicator of the community functional redundancy. Meanwhile, many revealed microbiome response determinants were enterotype-specific. In Bact1 and Rum enterotypes, the response was negatively correlated with the baseline abundance of their main drivers. Additionally, we proposed a method for preliminary assessment of the microbiome response. Our study delineats the universal features determining microbiome response to diverse interventions. The proposed approach is promising for understanding the mechanisms of gut microbiome stability and improving the efficacy of personalised microbiome-tailored interventions.

Approximation of a Microbiome Composition Shift by a Change in a Single Balance Between Two Groups of Taxa.

Linking microbiome composition obtained from metagenomic or 16S rRNA sequencing to various factors poses a real challenge. The compositional approach to such data is well described: a so-called isometric log-ratio (ILR) transform provides correct treatment of relative abundances. Most existing compositional methods differ in the particular choice of the transform. Although this choice does not influence the prediction of a model, it determines the subset of balances between groups of microbial taxa subsequently used for interpreting the composition shifts. We propose a method to interpret these shifts independently of the initial choice of ILR coordinates by the nearest single-balance shift. We describe here application of the method to regression, classification, and principal balance analysis of compositional data. Analytical treatment and cross-validation show that the approach provides the least-squares estimate of a single-balance shift associated with a factor with possible adjustment for covariates. As for classification and principal balance analysis, the nearest balance method provides results comparable to other compositional tools. Its advantages are the absence of assumptions about the number of taxa included in the balance and its low computational cost. The method is implemented in the R package NearestBalance. IMPORTANCE The method proposed here extends the range of compositional methods providing interpretation of classical statistical tools applied to data converted to the ILR coordinates. It provides a strictly optimal solution in several special cases. The approach is universally applicable to compositional data of any nature, including microbiome data sets.

VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota.

MOTIVATION: The resistance of bacterial pathogens to antibiotics is one of the most important issues of modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microbiota by means of horizontal gene transfer. Thus, it is important to develop methods of prediction and monitoring of antibiotics resistance in human populations. RESULTS: We present the agent-based VERA model, which allows simulation of the spread of pathogens, including the possible horizontal transfer of resistance determinants from a commensal microbiota community. The model considers the opportunity of residents to stay in the town or in a medical institution, have incorrect self-treatment, treatment with several antibiotics types and transfer and accumulation of resistance determinants from commensal microorganism to a pathogen. In this model, we have also created an assessment of optimum observation frequency of infection spread among the population. Investigating model behavior, we show a number of non-linear dependencies, including the exponential nature of the dependence of the total number of those infected on the average resistance of a pathogen. As the model infection, we chose infection with Shigella spp., though it could be applied to a wide range of other pathogens. AVAILABILITY AND IMPLEMENTATION: Source code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.