TY - JOUR
T1 - Necrotizing enterocolitis is preceded by increased gut bacterial replication, Klebsiella, and fimbriae-encoding bacteria
AU - Olm, Matthew R.
AU - Bhattacharya, Nicholas
AU - Crits-Christoph, Alexander
AU - Firek, Brian A.
AU - Baker, Robyn
AU - Song, Yun S.
AU - Morowitz, Michael J.
AU - Banfield, Jillian F.
N1 - Funding Information:
This research was supported, in part, by the NIH under awards RAI092531A and R01-GM109454, the Alfred P. Sloan Foundation under grant APSF-2012-10-05, and National Science Foundation Graduate Research Fellowships under grant no. DGE 1106400 (to M.O.). The study was approved by the University of Pittsburgh Institutional Review Board (Protocol PRO10090089). This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 OD018174 Instrumentation Grant.
Publisher Copyright:
Copyright © 2019 The Authors, some rights reserved;
PY - 2019/12
Y1 - 2019/12
N2 - Necrotizing enterocolitis (NEC) is a devastating intestinal disease that occurs primarily in premature infants. We performed genome-resolved metagenomic analysis of 1163 fecal samples from premature infants to identify microbial features predictive of NEC. Features considered include genes, bacterial strain types, eukaryotes, bacteriophages, plasmids, and growth rates. A machine learning classifier found that samples collected before NEC diagnosis harbored significantly more Klebsiella, bacteria encoding fimbriae, and bacteria encoding secondary metabolite gene clusters related to quorum sensing and bacteriocin production. Notably, replication rates of all bacteria, especially Enterobacteriaceae, were significantly higher 2 days before NEC diagnosis. The findings uncover biomarkers that could lead to early detection of NEC and targets for microbiome-based therapeutics.
AB - Necrotizing enterocolitis (NEC) is a devastating intestinal disease that occurs primarily in premature infants. We performed genome-resolved metagenomic analysis of 1163 fecal samples from premature infants to identify microbial features predictive of NEC. Features considered include genes, bacterial strain types, eukaryotes, bacteriophages, plasmids, and growth rates. A machine learning classifier found that samples collected before NEC diagnosis harbored significantly more Klebsiella, bacteria encoding fimbriae, and bacteria encoding secondary metabolite gene clusters related to quorum sensing and bacteriocin production. Notably, replication rates of all bacteria, especially Enterobacteriaceae, were significantly higher 2 days before NEC diagnosis. The findings uncover biomarkers that could lead to early detection of NEC and targets for microbiome-based therapeutics.
UR - http://www.scopus.com/inward/record.url?scp=85076638109&partnerID=8YFLogxK
U2 - 10.1126/sciadv.aax5727
DO - 10.1126/sciadv.aax5727
M3 - Article
C2 - 31844663
AN - SCOPUS:85076638109
SN - 2375-2548
VL - 5
JO - Science Advances
JF - Science Advances
IS - 12
M1 - eaax5727
ER -