Moossavi, S. | bioRxiv Science

bioRxiv2025

Human milk components interact with infant genomics to modulate gut microbiota, childhood asthma and atopy

The benefits of breastfeeding are well established; however, the mechanisms by which human milk components (HMCs) impact childrens long-term health remain poorly understood. We leveraged datasets from the CHILD Cohort Study to explore how exposure to variable HMCs-- including oligosaccharides (HMOs), fatty acids (HMFAs), and microbiota (HMM)--may influence infants gut microbiota and risk of childhood asthma and atopy. We identified HMCs (e.g., HMO lacto-N-fucopentaose III and HMFA linoleic acid) associated with gut microbes and microbial networks implicated in atopic diseases. Additionally, we determined that HMCs (e.g., HMM Pseudomonas oryzihabitans) interact with infants polygenic risk scores (PRSs) to influence these gut microbial features. Integration of HMCs, gut microbiota, and disease-associated PRSs into an unsupervised machine-learning model that clustered two groups of infants with differing disease prevalence. Our findings suggest that HMCs influence childhood asthma and atopy through modifications to the gut microbiota and modulated by interactions with infant genomics.

genomics↗

bioRxiv2020

Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

BackgroundQuality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established. ResultsIn this study we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: 1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; 2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and 3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms, and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our reslults in each batch before merging them for downstream analysis. ConclusionThis study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field.

microbiology↗