Literature DB >> 18318280

Quantitative assessment of human whole blood RNA as a potential biomarker for infectious disease.

Claire L Smith1, Paul Dickinson, Thorsten Forster, Mizanur Khondoker, Marie Craigon, Alan Ross, Petter Storm, Stewart Burgess, Paul Lacaze, Benjamin J Stenson, Peter Ghazal.   

Abstract

Infection remains a significant cause of morbidity and mortality especially in newborn infants. Analytical methods for diagnosing infection are severely limited in terms of sensitivity and specificity and require relatively large samples. It is proposed that stringent regulation of the human transcriptome affords a new molecular diagnostic approach based on measuring a highly specific systemic inflammatory response to infection, detectable at the RNA level. This proposition raises a number of as yet poorly characterised technical and biological variation issues that urgently need to be addressed. Here we report a quantitative assessment of methodological approaches for processing and extraction of RNA from small samples of infant whole blood and applying analysis of variation from biochip measurements. On the basis of testing and selection from a battery of assays we show that sufficient high quality RNA for analysis using multiplex array technology can be obtained from small neonatal samples. These findings formed the basis of implementing a set of robust clinical and experimental standard operating procedures for whole blood RNA samples from 58 infants. Modelling and analysis of variation between samples revealed significant sources of variation from the point of sample collection to processing and signal generation. These experiments further permitted power calculations to be run indicating the tractability and requirements of using changes in RNA expression profiles to detect different states between patient groups. Overall the results of our investigation provide an essential first step toward facilitating an alternative way for diagnosing infection from very small neonatal blood samples, providing methods and requirements for future chip-based studies.

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Year:  2007        PMID: 18318280     DOI: 10.1039/b707122c

Source DB:  PubMed          Journal:  Analyst        ISSN: 0003-2654            Impact factor:   4.616


  4 in total

1.  Whole blood gene expression profiling of neonates with confirmed bacterial sepsis.

Authors:  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
Journal:  Genom Data       Date:  2014-11-15

2.  mSep: investigating physiological and immune-metabolic biomarkers in septic and healthy pregnant women to predict feto-maternal immune health - a prospective observational cohort study protocol.

Authors:  Simran Sharma; Summia Zaher; Patrícia R S Rodrigues; Luke C Davies; Sarah Edkins; Angela Strang; Mallinath Chakraborty; W John Watkins; Robert Andrews; Edward Parkinson; Nicos Angelopoulos; Linda Moet; Freya Shepherd; Kate Megan Megan Davies; Daniel White; Shaun Oram; Kate Siddall; Vikki Keeping; Kathryn Simpson; Federica Faggian; Maryanne Bray; Claire Bertorelli; Sarah Bell; Rachel E Collis; James E McLaren; Mario Labeta; Valerie B O'Donnell; Peter Ghazal
Journal:  BMJ Open       Date:  2022-09-17       Impact factor: 3.006

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

Authors:  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; Peter Ghazal
Journal:  Nat Commun       Date:  2014-08-14       Impact factor: 14.919

4.  A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

Authors:  Mizanur Khondoker; Richard Dobson; Caroline Skirrow; Andrew Simmons; Daniel Stahl
Journal:  Stat Methods Med Res       Date:  2013-09-18       Impact factor: 3.021

  4 in total

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