High-throughput techniques based on DNA microarray technology can profile the whole transcriptome of a tissue and therefore provide a practical and economical tool for studying the gene expression of a multitude of genes in parallel (Schena et al. 1995). The underlying principle of DNA microarray technology is based on detection of hybridisation of labeled cDNAs (targets, which are obtained through extraction of mRNAs of a respective sample, followed by reverse transcription and optional amplification steps) to DNA probes of known sequence and position (array) on a chip surface, allowing for determination of the identities and abundances of the complementary target sequences. In the literature there exist at least two confusing nomenclature systems for referring to hybridization partners. According to the nomenclature recommended by B. Phimister of Nature Genetics, a "probe" is the tethered nucleic acid with known sequence, whereas a "target" is the free nucleic acid sample whose identity and abundance is being detected (Phimister 1999).
Performance of transcript profiling analysis, using oligonucleotide DNA microarray technology, allows for detection of genome-wide differences in the expression level of genes, meaning both identification and detection of differences in the abundance of nearly all mRNA transcripts present in the cells of these samples at a given point of time. An oligonucleotide cDNA microarray is an array of oligonucleotide (20~80- mer oligos) probes, chemically synthesized at specific locations (in situ = on-chip) on a coated quartz surface.
An alternative method of fabrication of gene arrays by high density in-situ synthesis of oligonucleotides by photolithography and combinatorial chemistry on wavers provides the basis for commercial available microarray technology and was developed by Steve Fodor and colleges at Affymetrix® Inc., USA (Pease et al. 1994), which sells its products under the GeneChip® trademark. Affymetrix’s GeneChip® methodology is limited to hybridization with single samples, and depends on the inclusion of quality control probe sets to allow intra-array data normalization and inter-array data comparability by complex statistical models (Bottinger et al. 2003). Affymetrix’s GeneChips® use so-called probe sets, containing multiple short
oligonucleotide DNA sequences (probes) of each 25 bases, derived from different regions of a single target transcript. The precise location where each probe is synthesized is called a feature. One feature is composed of a large number of identical oligonucleotide probes. In modern Affymetrix’s GeneChips®, the feature size is 11 µm and up to 1.6 millions of features are contained on one array. A probe set consists of eleven pairs of oligonucleotide probes (=22 different oligonucleotide probes). The individual probes of a probe set are located close to the 3' end of the respective mRNA sequence. Each pair consists of a perfect match (PM) oligonucleotide (complementary to the target sequence of interest), that provides measurable fluorescence when target sequences binds to it and a mismatch (MM) oligonucleotide, identical to its PM counterpart except for one mismatch base inserted at its central position. The paired mismatch probe serves as an internal control for its perfect match partner. False or contaminating fluorescence, for example derived from non-specific cross hybridizations, can efficiently be quantified and subtracted from a gene expression measurement. The difference in detected hybridization signals between the PM and MM partners, as well as their intensity ratios serve as indicators of specific target abundance, allowing for consistent discrimination between signal and background noise and for generation of accurate data sets (Affymetrix Manual). The availability of sequence descriptions and annotations of all probes present on the Affymetrix arrays also allows for approaches of analyses of microarray raw data different from the original “probe-set” approach by Affymetrix© (e.g. ChipInspector 1.2, Genomatix). Sequences of all probes are blasted against the entire mouse genome, thereby using the latest sequences information available to identify the transcripts, represented through the respective probes on the array. Following image acquisition and quality controls of scanned chips, generated microarray raw data are normalized to allow for inter- and intra-array comparability.
For his purpose, Affymetrix’s GeneChips® include a set of maintenance genes (normalization controls) to facilitate the normalization and scaling of array experiments prior to performing data comparisons. Expression levels of individual probe sets detected in the respective samples of an experiment are then compared. “Differentially expressed transcripts or probe sets” are then functionally annotated and further bioinformatical analyses are performed for revelation of their biological function, using software tools for pathway mapping analysis and generation of functional networks. To avoid misleading and confusing nomenclatures, it is important to point out clearly, that the described approaches of analysis of microarray data are designed for detection and identification of “differentially expressed transcripts”, not “genes”. Under optimal conditions, the cDNA samples hybridized to the probes on the surface of the respective arrays, would be regarded to represent the entire population of all mature mRNA-transcripts, present in all cells of the original sample material (e.g. glomerulus isolates) at the respective time point of investigation. Thus, the performance of a microarray experiment is basically capable of providing information concerning both identity and abundance (the frequency of occurrence) of each single transcript, detectable in the respective sample material. The term “differentially expressed or regulated transcript” is used for denomination of a single mRNA species whose abundance was experimentally found to be significantly altered in samples of one investigated group, relative to its abundance detected in samples of another group in the experiment, but does not automatically also refer to any regulatory processes of differential gene expression. The terms “differentially expressed transcript” and “differentially expressed gene” should actually not be used synonymously, since transcription of one single gene might result in the presence of more than a single species of mRNA-transcripts (e. g. by different post-transcriptional modifications of the primary transcript). However, a synonymous denomination of an identified transcript with a detected differential abundance in investigated samples in a given experiment and its corresponding gene is commonly performed.
In nephrology, early studies using DNA array technology were able to describe the basal expression profiles of whole renal tissues in normal human kidney (Yano et al. 2000) and in an animal model of diabetic nephropathy (Wada et al. 2001). However, the number of genes on the membrane-based high-density cDNA arrays was still very limited and critical questions concerning probe design and array quantification remained.
Later experiments benefited from matured technology and comprehensive gene expression analyses could be performed in various acute renal failure animal models, such as ischemia-reperfusion, unilateral ureter obstruction, and adriamycin-induced nephropathy, using whole renal tissue (Higgins et al. 2003, Kieran et al. 2003, Sadlier et al. 2004, Yoshida et al. 2002). Interestingly, a comparison of the gene expression signatures of these murine renal failure models was able to identify a shared transcriptome of 49 differentially expressed genes. Three renal disease-associated genes found in mice were also differentially expressed in human kidney biopsies, and correlated with renal disease stage and/or disease progression. These cross-species expression signatures are consistent with an evolutionarily conserved response of renal tissue, irrespective of the initial renal insult (Yasuda et al. 2005). In 2004, Susztak and colleges (Susztak et al. 2004) performed microarray analysis on samples of whole kidney tissues from different mouse models of diabetic nephropathy with comparable levels of hyperglycemia and albuminuria but different degrees of glomerular mesangial matrix expansion, which is considered to be a valuable indicator for the development of end stage renal disease in humans (Caramori et al. 2000). Comparison of the renal expression profiles of these different models allowed for the identification of a couple of genes whose differential expression was associated with specific steps of diabetic glomerulopathy, regardless of the investigated mouse model, the type of diabetes, its experimental induction as well as the presence or absence of obesity. In human nephrology, the use of oligonucleotide microarray based approaches on total kidney tissue has led to identification of novel diagnostic and prognostic parameters in patients with diverse renal diseases or renal transplants (Akalin et al. 2001, Sarwal et al. 2003, Takahashi et al. 2001). Expression array studies of renal disease can also facilitate the prediction of the disease course over time by definition of disease specific marker profiles that allow for the segregation of patients with a rather progressive or a stable disease course (Henger et al. 2004). The feasibility of such gene expression-based disease categorization in human renal biopsy samples was supported by clinical follow-up investigations, which revealed a stringent correlation between the respective expression fingerprint and the progression of renal disease. Besides molecular diagnostics or identification of candidates of potential therapeutic targets, gene expression profiling can also identify activated molecular pathways in the development of chronic renal diseases.
To gain insight into the molecular programs activated in diabetic nephropathy, for example, genome-wide gene expression profiling was performed in a disease stage- specific manner, using tubulo-interstitial compartments of human renal biopsies. Pathway mapping of the genes activated in DN was consistent with nuclear factor (NF)-κβ pathway activation, and allowed the identification of the promoter models enriched in DN-regulated genes (Schmid et al. 2006). Similar approaches also led to the identification of yet unknown members of the glomerular slit membrane (Cohen et al. 2006). In summary, the present cDNA array technology provides a powerful tool to analyze expression profiles from minimal amounts of renal tissue. This is seen as a prerequisite to identify the molecular processes involved in the pathogenesis of progressive chronic kidney diseases of various origins (Yasuda et al. 2006).
However, in comparison to experiments on the respective animal models, comprehensive gene expression analyses in human native renal biopsy materials are still limited. These experiments principally suffer from the high degree of heterogeneity within the samples in the investigated cohorts of human patients and the restricted availability of sufficient amounts of sample materials for performance of different experiments on the same sample materials.