Browsing by Person "Barr, Janice B."

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Differential protein expression profiling in BSE disease
(Taylor and Francis, 2010-08-10) Barr, Janice B.; Waddington, Dave; Barron, Rona
Bovine spongiform encephalopathy (BSE) is a fatal neurodegenerative disease affecting cattle. Current tests for the detection of BSE are based solely on the only definitive marker of the disease, an abnormal conformer (PrPd), of the host encoded prion protein (PrPc). Recent evidence that other transmissible spongiform encephalopathy diseases can be present in the absence of PrPd, coupled with the need to establish pre-mortem diagnostic assays have led to a search for alternative diagnostic approaches. In this study we apply differential protein expression profiling for the prediction of BSE disease in post-mortem bovine brain tissue. The protein profiles of groups of 27 BSE diseased cattle were compared with 28 control animals. Analysis using statistical learning (and linear discriminant analysis) techniques established protein markers of disease with good predictive power (sensitivity 85% and specificity 71%). Further work will be required to test the predictive markers in a wider range of diseases, particularly other neurological conditions.
Differential protein profiling as a potential multi-marker approach for TSE diagnosis
(BMC, 2009-11-27) Barr, Janice B.; Watson, Michael; Head, Mark W.; Ironside, James W.; Harris, Nathan; Hogarth, Caroline; Fraser, Janet R.; Barron, Rona
This "proof of concept" study, examines the use of differential protein expression profiling using surface enhanced laser desorption and ionisationtime of flight mass spectrometry (SELDI-TOF) for the diagnosis of TSE disease. Spectral output from all proteins selectively captured from individual murine brain homogenate samples, are compared as "profiles" in groups of infected and non-infected animals. Differential protein expression between groups is thus highlighted and statistically significant protein "peaks" used to construct a panel of disease specific markers. Studies at both terminal stages of disease and throughout the time course of disease have shown a disease specific protein profile or "disease fingerprint" which could be used to distinguish between groups of TSE infected and uninfected animals at an early time point of disease. Results Our results show many differentially expressed proteins in diseased and control animals, some at early stages of disease. Three proteins identified by SELDI-TOF analysis were verified by immunohistochemistry in brain tissue sections. We demonstrate that by combining the most statistically significant changes in expression, a panel of markers can be constructed that can distinguish between TSE diseased and normal animals. Conclusion Differential protein expression profiling has the potential to be used for the detection of disease in TSE infected animals. Having established that a "training set" of potential markers can be constructed, more work would be required to further test the specificity and sensitivity of the assay in a "testing set". Based on these promising results, further studies are being performed using blood samples from infected sheep to assess the potential use of SELDI-TOF as a pre-mortem blood based diagnostic.
The Human Urinary Proteome Fingerprint Database UPdb
(Hindawi, 2013-10-09) Husi, Holger; Barr, Janice B.; Skipworth, Richard J. E.; Stephens, Nathan A.; Greig, Carolyn A.; Wackerhage, Henning; Barron, Rona; Fearon, Kenneth C. H.; Ross, James A.
The use of human urine as a diagnostic tool has many advantages, such as ease of sample acquisition and noninvasiveness. However, the discovery of novel biomarkers, as well as biomarker patterns, in urine is hindered mainly by a lack of comparable datasets. To fill this gap, we assembled a new urinary fingerprint database. Here, we report the establishment of a human urinary proteomic fingerprint database using urine from 200 individuals analysed by SELDI-TOF (surface enhanced laser desorption ionisation-time of flight) mass spectrometry (MS) on several chip surfaces (SEND, HP50, NP20, Q10, CM10, and IMAC30). The database currently lists 2490 unique peaks/ion species from 1172 nonredundant SELDI analyses in the mass range of 1500 to 150000. All unprocessed mass spectrometric scans are available as “.xml” data files. Additionally, 1384 peaks were included from external studies using CE (capillary electrophoresis)-MS, MALDI (matrix assisted laser desorption/ionisation), and CE-MALDI hybrids. We propose to use this platform as a global resource to share and exchange primary data derived from MS analyses in urinary research.