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

Claire L. Smith, Paul Dickinson, Thorsten Forster, Marie Craigon, Alan Ross, Mizanur R. Khondoker, Rebecca France, Alasdair Ivens, David J. Lynn, Judith Orme, Allan Jackson, Paul Lacaze, Katie L. Flanagan, Benjamin J. Stenson and Peter Ghazal ()
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Claire L. Smith: Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh
Paul Dickinson: Edinburgh Infectious Diseases, University of Edinburgh
Thorsten Forster: Edinburgh Infectious Diseases, University of Edinburgh
Marie Craigon: Edinburgh Infectious Diseases, University of Edinburgh
Alan Ross: Edinburgh Infectious Diseases, University of Edinburgh
Mizanur R. Khondoker: Edinburgh Infectious Diseases, University of Edinburgh
Rebecca France: Edinburgh Infectious Diseases, University of Edinburgh
Alasdair Ivens: Fios Genomics Ltd., ETTC, King’s Buildings
David J. Lynn: AGRIC, Teagasc, Grange
Judith Orme: Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh
Allan Jackson: Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh
Paul Lacaze: Edinburgh Infectious Diseases, University of Edinburgh
Katie L. Flanagan: MRC Research Laboratories, Atlantic Boulevard
Benjamin J. Stenson: Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh
Peter Ghazal: Edinburgh Infectious Diseases, University of Edinburgh

Nature Communications, 2014, vol. 5, issue 1, 1-15

Abstract: 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.

Date: 2014
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DOI: 10.1038/ncomms5649

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