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Nat Commun. 2014 Aug 14;5:4649. doi: 10.1038/ncomms5649.

Identification of a human neonatal immune-metabolic network associated with bacterial infection.

Author information

1
1] Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK [2] Division of Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UK [3].
2
1] Division of Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UK [2] SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3JD, UK [3].
3
1] Division of Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UK [2] SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3JD, UK.
4
Division of Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UK.
5
1] Division of Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UK [2].
6
1] Fios Genomics Ltd., ETTC, King's Buildings, Edinburgh EH9 3JL, UK [2].
7
1] Animal Bioscience Research Department, AGRIC, Teagasc, Grange, Dunsany, Co. Meath, Ireland [2].
8
Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK.
9
1] MRC Research Laboratories, Atlantic Boulevard, PO Box 273, Fajara, Gambia [2].

Abstract

Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

PMID:
25120092
PMCID:
PMC4143936
DOI:
10.1038/ncomms5649
[Indexed for MEDLINE]
Free PMC Article

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