HighlightsVaginal seeding of cesarean section-born babies naturalizes their microbiota Bacteria from multiple body sites compose the perinatal maternal vaginal microbiome Bacteria typical in vaginal birth engraft different sites of cesarean section-born babies Song et al., Med 2,[1][2][3][4][5][6][7][8][9][10][11][12][13][14]

In this cross-sectional study, we describe the composition and diversity of the gut microbiota among undernourished children living in urban slums of Mumbai, India, and determine how nutritional status, including anthropometric measurements, dietary intakes from complementary foods, feeding practices, and micronutrient concentrations, is associated with their gut microbiota. We collected rectal swabs from children aged 10 to 18 months living in urban slums of Mumbai participating in a randomized controlled feeding trial and conducted 16S rRNA sequencing to determine the composition of the gut microbiota. Across the study cohort, Proteobacteria dominated the gut microbiota at over 80% relative abundance, with Actinobacteria representation at <4%, suggesting immaturity of the gut. Increased microbial α-diversity was associated with current breastfeeding, greater head circumference, higher fat intake, and lower hemoglobin concentration and weight-for-length Z-score. In redundancy analyses, 47% of the variation in Faith’s phylogenetic diversity (Faith’s PD) could be accounted for by age and by iron and polyunsaturated fatty acid intakes. Differences in community structure (β-diversity) of the microbiota were observed among those consuming fats and oils the previous day compared to those not consuming fats and oils the previous day. Our findings suggest that growth, diet, and feeding practices are associated with gut microbiota metrics in undernourished children, whose gut microbiota were comprised mainly of Proteobacteria, a phylum containing many potentially pathogenic taxa. IMPORTANCE The impact of comprehensive nutritional status, defined as growth, nutritional blood biomarkers, dietary intakes, and feeding practices, on the gut microbiome in children living in low-resource settings has remained underreported in microbiome research. Among undernourished children living in urban slums of Mumbai, India, we observed a high relative abundance of Proteobacteria, a phylum including many potentially pathogenic species similar to the composition in preterm infants, suggesting immaturity of the gut, or potentially a high inflammatory burden. We found head circumference, fat and iron intake, and current breastfeeding were positively associated with microbial diversity, while hemoglobin and weight for length were associated with lower diversity. Findings suggest that examining comprehensive nutrition is critical to gain more understanding of how nutrition and the gut microbiota are linked, particularly in vulnerable populations such as children in urban slum settings.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high‐throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high‐dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance‐based beta‐diversity between individuals. Analyses of such between‐subject attributes are not amenable to the predominant within‐subject‐based statistical paradigm, such as t‐tests and linear regression. In this paper, we propose a new approach to model beta‐diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between‐ as well as within‐subject attributes. The new approach not only addresses limitations of current methods for beta‐diversity with cross‐sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.

Differentiating microbial communities among samples is a major objective in biomedicine. Quantifying the effect size of these differences allows researchers to understand the factors most associated with communities and to optimize the design and clinical resources required to address particular research questions. Here, we present Evident, a package for effect size calculations and power analysis on microbiome data and show that Evident scales to large datasets with numerous metadata covariates.