Scientific Frontiers - Strain level pipeline classification - BaseClear

Development & application of a pipeline for strain level classification of the Infant Microbiome using metagenome data​

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Background

At birth, the exposure to specific microbial strains shapes the infants´ microbiome and the metabolic function as we age. Human milk supports this early microbial ecosystem through probiotic strains and prebiotic compounds. When breastfeeding is not possible, infant formulae try to support the infants´ microbiome. Strain specificity is key in probiotic effects in the early ‘window of opportunity’ - an important time frame for the immune training in infants.

Objective

Validation of a computational shotgun metagenomics pipeline for quantifying abundance and strain-level classification specifically within the infant microbiome by using a curated infant database.

Method

The effects of an infant formula, containing probiotic strains, was studied using an ex-vivo gut model (SHIME®) inoculated with infant donor-microbiomes. Each of the setups was supplemented with infant formula including 1 or both probiotics as well as hydrolyzed or non-hydrolyzed protein to study the effects on the donor microbiome, probiotic colonization and metabolic activity. To expand the microbiome analysis beyond the genus level, typical for 16S data, shotgun metagenomic data was used to perform strain-level analyses using the bioinformatics pipeline presented here.

Results

Infant milk formula based on hydrolyzed protein indicated effects on the overall abundance of the Bifidobacterium species as well as the probiotic strains in vitro, highlighting the importance of the formula matrix. The abundance of the probiotic strains was increased on a strain specific level.

Conclusions

Database optimization with validation and bioinformatic tools comparison led to the increase of accurate and meaningful strain identification in the infant metagenome datasets.