Back

Strain-level characterization of health-associated bacterial consortia that colonize the human gut during infancy

Minot, S.; Mayer-Blackwell, K.; Fiore-Gartland, A.; Johnson, A.; Self, S.; Bhatti, P.; Yao, L.; Liu, L.; Sun, X.; Jinfa, Y.; Kublin, J.

2023-12-18 gastroenterology
10.1101/2023.12.16.23300077 medRxiv
Show abstract

BackgroundThe human gut microbiome develops rapidly during infancy, a key window of development coinciding with maturation of the adaptive immune system. However, little is known of the microbiome growth dynamics over the first few months of life and whether there are any generalizable patterns across human populations. We performed metagenomic sequencing on stool samples (n=94) from a cohort of infants (n=15) at monthly intervals in the first six months of life, augmenting our dataset with seven published studies for a total of 4,441 metagenomes from 1,162 infants. ResultsStrain-level de novo analysis was used to identify 592 of the most abundant organisms in the infant gut microbiome. Previously unrecognized consortia were identified which exhibited highly correlated abundances across samples and were composed of diverse species spanning multiple genera. Analysis of a cohort of infants with cystic fibrosis identified one such novel consortium of diverse Enterobacterales which was positively correlated with weight gain. While all studies showed an increased community stability during the first year of life, microbial dynamics varied widely in the first few months of life, both by study and by individual. ConclusionBy augmenting published metagenomic datasets with data from a newly established cohort we were able to identify novel groups of organisms that are correlated with measures of robust human development. We hypothesize that the presence of these groups may impact human health in aggregate in ways that individual species may not in isolation.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.