Correction to: Piphillin predicts metagenomic composition and dynamics from DADA2- corrected 16S rDNA sequences

The digestive tract sections of the sea cucumber Isostichopus badionotus reveal differences in composition, diversity, and functionality of the gut microbiota

For the first time, this study analyses the composition and diversity of the gut microbiota of Isostichopus badionotus in captivity, using high-throughput 16S rRNA sequencing, and predicts the metagenomic functions of the microbiota. The results revealed a different composition of the gut microbiota for the foregut (FG) and midgut (MG) compared to the hindgut (HG), with a predominance of Proteobacteria, followed by Actinobacteria, Bacteroidetes, and Firmicutes. The FG and MG demonstrated a greater bacterial diversity compared to the HG. In addition, a complex network of interactions was observed at the genus level and identified some strains with probiotic and bioremediation potentials, such as Acinetobacter, Ruegeria, Streptococcus, Lactobacillus, Pseudomonas, Enterobacter, Aeromonas, Rhodopseudomonas, Agarivorans, Bacillus, Enterococcus, Micrococcus, Bifidobacterium, and Shewanella. Predicting metabolic pathways revealed that the bacterial composition in each section of the intestine participates in different physiological processes such as metabolism, genetic and environmental information processing, organismal systems, and cellular processes. Understanding and manipulating microbe--host-environment interactions and their associated functional capacity could substantially contribute to achieving more sustainable aquaculture systems for I. badionotus.

Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases

The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by “shotgun” metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)—high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Mothers of Preterm Infants Have Individualized Breast Milk Microbiota that Changes Temporally Based on Maternal Characteristics

Mother's milk contains complex microbial communities thought to be important for colonizing a preterm infant's gastrointestinal tract. However, little is known about the microbiota in the preterm mother's milk and factors influencing its composition. We characterized the temporal dynamics of microbial communities in 490 breast milk samples from 86 mothers of preterm infants (born <1,250g) over the first 8 weeks postpartum. Highly individualized microbial communities were identified in each mother's milk that changed temporally with notable alterations in predicted microbial functions. However, pre-pregnancy BMI, delivery mode, and antibiotics were associated with changes in these microbial dynamics. Individual classes of antibiotics and their duration of exposure during prenatal and postpartum periods showed unique relationships with microbial taxa abundance and diversity in mother's milk. These results highlight the temporal complexity of the preterm mother's milk microbiota and its relationship with maternal characteristics as well as the importance of discussing antibiotic stewardship for mothers.