Abstract

BackgroundDifferent feeding regimens in infancy alter the gastrointestinal (gut) microbial environment. The fecal microbiota in turn influences gastrointestinal homeostasis including metabolism, immune function, and extra-/intra-intestinal signaling. Advances in next generation sequencing (NGS) have enhanced our ability to study the gut microbiome of breast-fed (BF) and formula-fed (FF) infants with a data-driven hypothesis approach.MethodsNext generation sequencing libraries were constructed from fecal samples of BF (n=24) and FF (n=10) infants and sequenced on an Illumina HiSeq 2500. Taxonomic classification of the NGS data was performed using the Sunbeam/Kraken pipeline and a functional analysis at the gene level was performed using publicly available algorithms, including BLAST, and custom scripts. Differentially represented genera, genes, and NCBI Clusters of Orthologous Genes (COG) were determined between cohorts using count data and R (statistical packages edgeR and DESeq2).ResultsThirty-nine genera were found to be differentially represented between the BF and FF cohorts (FDR ≤ 0.01) including Parabacteroides, Enterococcus, Haemophilus, Gardnerella, and Staphylococcus. A Welch t-test of the Shannon diversity index for BF and FF samples approached significance (p=0.061). Bray-Curtis and Jaccard distance analyses demonstrated clustering and overlap in each analysis. Sixty COGs were significantly overrepresented and those most significantly represented in BF vs. FF samples showed dichotomy of categories representing gene functions. Over 1,700 genes were found to be differentially represented (abundance) between the BF and FF cohorts.ConclusionsFecal samples analyzed from BF and FF infants demonstrated differences in microbiota genera. The BF cohort includes greater presence of beneficial genus Bifidobacterium. Several genes were identified as present at different abundances between cohorts indicating differences in functional pathways such as cellular defense mechanisms and carbohydrate metabolism influenced by feeding. Confirmation of gene level NGS data via PCR and electrophoresis analysis revealed distinct differences in gene abundances associated with important biologic pathways.

Highlights

  • Dietary content is an important consideration in the longterm development of immunologic, metabolic, and many chronic disorders (Singhal and Lanigan, 2007; Cox et al, 2014; Rodriguez et al, 2015; Clapp et al, 2017; Davis et al, 2017; Davis et al, 2020; Turroni et al, 2020; Sarkar et al, 2021)

  • There were no statistical differences noted in the demographic data (Table 1) between the BF and FF cohort except for delivery method (p-value

  • A larger cohort expands the sensitivity of the analysis from prior work (Di Guglielmo et al, 2019) and allows a more in-depth analysis of Clusters of Orthologous Genes (COG) and gene differences in the gut microbiome between the two cohorts, FF and BF, of young infants

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Summary

Introduction

Dietary content is an important consideration in the longterm development of immunologic, metabolic, and many chronic disorders (Singhal and Lanigan, 2007; Cox et al, 2014; Rodriguez et al, 2015; Clapp et al, 2017; Davis et al, 2017; Davis et al, 2020; Turroni et al, 2020; Sarkar et al, 2021). Analyzing the infant fecal microbiome to understand the effects on the gastrointestinal (gut) microbiota conferred by early feeding/diet could help elucidate the mechanism underlying the development of these phenotypes. Generation sequencing (NGS) is a technique that enables deep probing of both meta-taxonomy of the gut flora as well as the metagenomics signature of the microbiome. Dietary differences may have a long-lasting effect on the gut microbiome by impacting the composition and biological functions of the organisms present. The study attempts to characterize the metagenome composition and differences between BF and FF cohorts. Advances in generation sequencing (NGS) have enhanced our ability to study the gut microbiome of breast-fed (BF) and formula-fed (FF) infants with a data-driven hypothesis approach

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