CD4+ T cell surface alpha enolase is lower in older adults

To identify novel cell ageing markers in order to gain insight into ageing mechanisms, we adopted membrane enrichment and comparison of the CD4+ T cell membrane proteome (purified by cell surface labelling using Sulfo-NHS-SS-Biotin reagent) between healthy young (n=9, 20-25y) and older (n=10; 50-70y) male adults. Following two-dimensional gel electrophoresis (2DE) to separate pooled membrane proteins in triplicates, the identity of protein spots with age-dependent differences (p<0.05 and >1.4 fold difference) was determined using liquid chromatography-mass spectrometry (LC-MS/MS). Seventeen protein spot density differences (ten increased and seven decreased in the older adult group) were observed between young and older adults. From spot intensity analysis, CD4+ T cell surface α-enolase was decreased in expression by 1.5 fold in the older age group; this was verified by flow cytometry (n=22) and qPCR with significantly lower expression of cellular α-enolase mRNA and protein compared to young adult CD4+ T cells (p<0.05). In an independent age-matched case-control study, lower CD4+ T cell surface α-enolase expression was observed in age-matched patients with cardiovascular disease (p<0.05). An immune-modulatory role has been proposed for surface α-enolase and our findings of decreased expression suggest that deficits in surface α-enolase merit investigation in the context of immune dysfunction during ageing and vascular disease.

The sensitivity of mapping methods to reference data quality:training supervised image classifications with imperfect reference data

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.