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

Claire Lindsay Smith, Paul Dickinson, Thorsten Forster, Marie Craigon, Alan Ross, Mizanur Khondoker, Rebecca France, Alasdair Ivens, David Lynn, Judith Orme, Allan Jackson, Paul Lacaze, Katie Flanagan, Ben Stenson, Peter Ghazal

    Research output: Contribution to journalArticlepeer-review

    72 Citations (Scopus)


    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.

    Original languageEnglish
    Article number4649
    Pages (from-to)4649
    Number of pages15
    JournalNature Communications
    Publication statusPublished - 14 Aug 2014


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