The composition of the human gut microbiome matures within the first years of life. It has been hypothesized that microbial compositions in this period can cause immune dysregulations and potentially cause asthma. Here we show, by associating gut microbial composition from 16S rRNA gene amplicon sequencing during the first year of life with subsequent risk of asthma in 690 participants, that 1-year-old children with an immature microbial composition have an increased risk of asthma at age 5 years. This association is only apparent among children born to asthmatic mothers, suggesting that lacking microbial stimulation during the first year of life can trigger their inherited asthma risk. Conversely, adequate maturation of the gut microbiome in this period may protect these pre-disposed children.

BackgroundThere is an immense scientific interest in the human microbiome and its effects on human physiology, health, and disease. A common approach for examining bacterial communities is high-throughput sequencing of 16S rRNA gene hypervariable regions, aggregating sequence-similar amplicons into operational taxonomic units (OTUs). Strategies for detecting differential relative abundance of OTUs between sample conditions include classical statistical approaches as well as a plethora of newer methods, many borrowing from the related field of RNA-seq analysis. This effort is complicated by unique data characteristics, including sparsity, sequencing depth variation, and nonconformity of read counts to theoretical distributions, which is often exacerbated by exploratory and/or unbalanced study designs. Here, we assess the robustness of available methods for (1) inference in differential relative abundance analysis and (2) beta-diversity-based sample separation, using a rigorous benchmarking framework based on large clinical 16S microbiome datasets from different sources.ResultsRunning more than 380,000 full differential relative abundance tests on real datasets with permuted case/control assignments and in silico-spiked OTUs, we identify large differences in method performance on a range of parameters, including false positive rates, sensitivity to sparsity and case/control balances, and spike-in retrieval rate. In large datasets, methods with the highest false positive rates also tend to have the best detection power. For beta-diversity-based sample separation, we show that library size normalization has very little effect and that the distance metric is the most important factor in terms of separation power.ConclusionsOur results, generalizable to datasets from different sequencing platforms, demonstrate how the choice of method considerably affects analysis outcome. Here, we give recommendations for tools that exhibit low false positive rates, have good retrieval power across effect sizes and case/control proportions, and have low sparsity bias. Result output from some commonly used methods should be interpreted with caution. We provide an easily extensible framework for benchmarking of new methods and future microbiome datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0208-8) contains supplementary material, which is available to authorized users.

Both prenatal under- and overnutrition predisposed for abdominal adiposity, apparently by reducing the expandability of subcutaneous adipose tissue and induced differential physiological adaptations to fasting. This study does not suggest that exposure to gestational overnutrition will provide a protective effect against development of hyperglycaemia later in life.

There have been reports of associations between cesarean section delivery and the risk of childhood asthma, potentially mediated through changes in the gut microbiota. We followed 700 children in the Copenhagen Prospective Studies on Asthma in Childhood2010 (COPSAC2010) cohort prospectively from birth. We examined the effects of cesarean section delivery on gut microbial composition by 16S rRNA gene amplicon sequencing during the first year of life. We then explored whether gut microbial perturbations due to delivery mode were associated with a risk of developing asthma in the first 6 years of life. Delivery by cesarean section was accompanied by marked changes in gut microbiota composition at one week and one month of age, but by one year of age only minor differences persisted compared to vaginal delivery. Increased asthma risk was found in children born by cesarean section only if their gut microbiota composition at 1 year of age still retained a cesarean section microbial signature, suggesting that appropriate maturation of the gut microbiota could mitigate against the increased asthma risk associated with gut microbial changes due to cesarean section delivery.

Asthma is believed to arise through early life aberrant immune development in response to environmental exposures that may influence the airway microbiota. Here, we examine the airway microbiota during the first three months of life by 16S rRNA gene amplicon sequencing in the population-based Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) cohort consisting of 700 children monitored for the development of asthma since birth. Microbial diversity and the relative abundances of Veillonella and Prevotella in the airways at age one month are associated with asthma by age 6 years, both individually and with additional taxa in a multivariable model. Higher relative abundance of these bacteria is furthermore associated with an airway immune profile dominated by reduced TNF-α and IL-1β and increased CCL2 and CCL17, which itself is an independent predictor for asthma. These findings suggest a mechanism of microbiota-immune interactions in early infancy that predisposes to childhood asthma.