Dickinson, Paul, Smith, Claire L., Forster, Thorsten, Craigon, Marie, Ross, Alan J., Khondoker, Mizanur R. ORCID: https://orcid.org/0000-0002-1801-1635, Ivens, Alasdair, Lynn, David J., Orme, Judith, Jackson, Allan, Lacaze, Paul, Flanagan, Katie L., Stenson, Benjamin J. and Ghazal, Peter (2015) Whole blood gene expression profiling of neonates with confirmed bacterial sepsis. Genomics Data, 3. pp. 41-48. ISSN 2213-5960
Preview |
PDF (Published manuscript)
- Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.
Item Type: | Article |
---|---|
Additional Information: | © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). |
Uncontrolled Keywords: | neonatal sepsis,whole blood,gene expression profiling,microarray,sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being |
Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health Faculty of Medicine and Health Sciences > Research Groups > Public Health and Health Services Research (former - to 2023) Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Population Health |
Depositing User: | Pure Connector |
Date Deposited: | 24 Sep 2016 00:39 |
Last Modified: | 19 Oct 2023 01:46 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/60160 |
DOI: | 10.1016/j.gdata.2014.11.003 |
Downloads
Downloads per month over past year
Actions (login required)
View Item |