Whole blood gene expression profiling of neonates with confirmed bacterial sepsis
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...
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doaj-d54f889f318f4447bd703971eee188f32020-11-25T02:08:01ZengElsevierGenomics Data2213-59602015-03-013C414810.1016/j.gdata.2014.11.003Whole blood gene expression profiling of neonates with confirmed bacterial sepsisPaul Dickinson0Claire L. Smith1Thorsten Forster2Marie Craigon3Alan J. Ross4Mizan R. Khondoker5Alasdair Ivens6David J. Lynn7Judith Orme8Allan Jackson9Paul Lacaze10Katie L. Flanagan11Benjamin J. Stenson12Peter Ghazal13Division of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKNeonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UKDivision of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKDivision of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKDivision of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKDivision of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKFios Genomics Ltd., Edinburgh BioQuater, Edinburgh EH16 4SB, UKAnimal & Bioscience Research Department, AGRIC, Teagasc, Grange, Dunsany, Co. Meath, IrelandNeonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UKNeonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UKDivision of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKMRC Research Laboratories, Atlantic Boulevard, PO Box 273, Fajara, GambiaNeonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UKDivision of Infection and Pathway Medicine, Edinburgh Infectious Diseases, University of Edinburgh, Edinburgh EH16 4SB, UKNeonatal 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.http://www.sciencedirect.com/science/article/pii/S2213596014001160Neonatal sepsisWhole bloodGene expression profilingMicroarray |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paul Dickinson Claire L. Smith Thorsten Forster Marie Craigon Alan J. Ross Mizan R. Khondoker Alasdair Ivens David J. Lynn Judith Orme Allan Jackson Paul Lacaze Katie L. Flanagan Benjamin J. Stenson Peter Ghazal |
spellingShingle |
Paul Dickinson Claire L. Smith Thorsten Forster Marie Craigon Alan J. Ross Mizan R. Khondoker Alasdair Ivens David J. Lynn Judith Orme Allan Jackson Paul Lacaze Katie L. Flanagan Benjamin J. Stenson Peter Ghazal Whole blood gene expression profiling of neonates with confirmed bacterial sepsis Genomics Data Neonatal sepsis Whole blood Gene expression profiling Microarray |
author_facet |
Paul Dickinson Claire L. Smith Thorsten Forster Marie Craigon Alan J. Ross Mizan R. Khondoker Alasdair Ivens David J. Lynn Judith Orme Allan Jackson Paul Lacaze Katie L. Flanagan Benjamin J. Stenson Peter Ghazal |
author_sort |
Paul Dickinson |
title |
Whole blood gene expression profiling of neonates with confirmed bacterial sepsis |
title_short |
Whole blood gene expression profiling of neonates with confirmed bacterial sepsis |
title_full |
Whole blood gene expression profiling of neonates with confirmed bacterial sepsis |
title_fullStr |
Whole blood gene expression profiling of neonates with confirmed bacterial sepsis |
title_full_unstemmed |
Whole blood gene expression profiling of neonates with confirmed bacterial sepsis |
title_sort |
whole blood gene expression profiling of neonates with confirmed bacterial sepsis |
publisher |
Elsevier |
series |
Genomics Data |
issn |
2213-5960 |
publishDate |
2015-03-01 |
description |
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. |
topic |
Neonatal sepsis Whole blood Gene expression profiling Microarray |
url |
http://www.sciencedirect.com/science/article/pii/S2213596014001160 |
work_keys_str_mv |
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