bioRxivMicrobiologyPreprint
Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis
M. J. Tisza, R. E. Lloyd, K. L. Hoffman, D. P. Smith, M. Rewers, the TEDDY Study Group, S. J. J. Cregeen, J. F. Petrosino
Abstract
Humans are colonized with commensal bacteria soon after birth, and, while this colonization is affected by lifestyle and other factors, bacterial colonization proceeds through well-studied phases. However, less is known about phage communities in early human development due to small study sizes, inability to leverage large databases, and lack of appropriate bioinformatics tools. In this study, whole genome shotgun sequencing data from the TEDDY study, composed of 12,262 longitudinal samples from 887 children in 4 countries, is reanalyzed to assess phage and bacterial dynamics simultaneously. Reads from these samples were mapped to marker genes from both bacteria and a new database of tens of thousands of phage taxa from human microbiomes. We uncover that each child is colonized by hundreds of different phages during the early years, and phages are more transitory than bacteria. Participants samples continually harbor new phage species over time whereas the diversification of bacterial species begins to saturate. Phage data improves the ability for machine learning models to discriminate samples by country. Finally, while phage populations were individual-specific, striking patterns arose from the larger dataset, showing clear trends of ecological succession amongst phages, which correlated well with putative host bacteria. Improved understanding of phage-bacterial relationships may reveal new means by which to shape and modulate the microbiome and its constituents to improve health and reduce disease, particularly in vulnerable populations where antibiotic use and/or other more drastic measures may not be advised.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
- approach: machine learning (classical)
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