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Expression of kidney injury molecule-1 (Kim-1) is rapidly upregulated following tubular injury, constituting a biomarker for acute kidney damage. We examined the renal localization of Kim-1 expression in PKD/Mhm (polycystic kidney disease, Mannheim) (cy/+) rats (cy: mutated allel, +: wild type allel), an established model for autosomal dominant polycystic kidney disease, with chronic, mainly proximal tubulointerstitial alterations. For immunohistochemistry or Western blot analysis, kidneys of male adult heterozygously-affected (cy/+) and unaffected (+/+) littermates were perfusion-fixed or directly removed. Kim-1 expression was determined using peroxidase- or fluorescence-linked immunohistochemistry (alone or in combination with markers for tubule segments or differentiation). Compared to (+/+), only in (cy/+) kidneys, a chronic expression of Kim-1 could be detected by Western blot analysis, which was histologically confined to an apical cellular localization in areas of cystically-transformed proximal tubules with varying size and morphology, but not in distal tubular segments. Kim-1 was expressed by cystic epithelia exhibiting varying extents of dedifferentiation, as shown by double labeling with aquaporin-1, vimentin or osteopontin, yielding partial cellular coexpression. In this model, in contrast to other known molecules indicating renal injury and/or repair mechanisms, the chronic renal expression of Kim-1 is strictly confined to proximal cysts. Its exact role in interfering with tubulo-interstitial alterations in polycystic kidney disease warrants future investigations.
BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.
RESULTS: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).
CONCLUSION: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.