1. Multi-omics analysis of immune-metabolic blood responses of very preterm infants for improving diagnosis of late-onset sepsis
- Author
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Currie, Andrew, Strunk, Tobias, Maker, Garth, Reinke, Stacey, Decuypere, S., Ng, Sherrianne Qin Yin, Currie, Andrew, Strunk, Tobias, Maker, Garth, Reinke, Stacey, Decuypere, S., and Ng, Sherrianne Qin Yin
- Abstract
Very preterm infants are at highest risk of developing late-onset sepsis (LOS) through nosocomial infection, but our understanding of the associated transcriptional and metabolic responses remains very limited. Interrogating the neonatal immunometabolome could provide novel insights into the complex and dynamic pathophysiology and has potential to identify new signatures/biomarkers and improve LOS diagnosis. We hypothesised that neonatal sepsis will trigger characteristic changes in transcriptional regulation of critical innate immune response genes that will relate to their blood metabolome. Additionally, that the pathophysiological changes can be used to identify infected infants using a minimum metabolic and transcriptional signature specific for LOS. The blood transcriptome and plasma metabolome of very preterm infants with suspected LOS were profiled using RNA sequencing (RNA-seq) (n=18) and non-targeted liquid chromatography-mass spectrometry (LC-MS; n=20). Transcriptional profiling included bioinformatic and statistical analyses consisting of differential gene expression analysis, over-representation analysis, and protein-protein interaction (PPI) network and pathway enrichment analysis. Metabolomic analyses consisted of univariate and multivariate statistical methods including principal component analysis (PCA), partial least squaresdiscriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). The metabolite features potentially important for discriminating infants with confirmed LOS, possible LOS and without LOS were identified using stringent criteria of: p<0.05, fold-change (FC)>3 and OPLS-DA variable importance in projection (VIP)>1. Separately, we used a multi-dimensional approach by integrating the transcriptomic, metabolomic, demographic and laboratory data of matching very preterm infants with suspected LOS using multiple factor analysis (MFA; n=17) to assess patient classification. Our transcriptomic findings s
- Published
- 2019