Division of Matrix Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.
Kidney Int. 2013 Sep;84(3):591-9. doi: 10.1038/ki.2013.169. Epub 2013 Jun 19.
Glomerular diseases represent major diagnostic and therapeutic challenges with classification of these diseases largely relying on clinical and histological findings. Elucidation of molecular mechanisms of progressive glomerular disease could facilitate quicker development. High-throughput expression profiling reveals all genes and proteins expressed in tissue and cell samples. These methods are very appropriate for glomerular disease as pure glomeruli can be obtained from kidney biopsies. To date, proteome profiling data are only available for normal glomeruli, but more robust transcriptome methods have been applied to many mouse model and a few human glomerular diseases. Here, we have carried out a meta-analysis of currently available glomerular expression data in normal and diseased glomeruli from mice, rats, and humans using a standardized protocol. The results suggest a potential for glomerular transcriptomics in identifying pathogenic pathways, disease monitoring, and the feasibility to use animal models to study human glomerular disease. We also found that currently there are no specific consensus biomarkers or pathways among different disease data sets, indicating there are likely disease-specific mechanisms and expression profiles. Thus, further transcriptomics and proteomics analysis, especially that of dynamic changes in the diseases, may lead to novel diagnostics tools and specific pharmacologic therapies.
肾小球疾病是主要的诊断和治疗挑战，这些疾病的分类主要依赖于临床和组织学发现。阐明进行性肾小球疾病的分子机制可以促进更快的发展。高通量表达谱分析揭示了组织和细胞样本中表达的所有基因和蛋白质。这些方法非常适用于肾小球疾病，因为可以从肾活检中获得纯肾小球。迄今为止，蛋白质组谱数据仅可用于正常肾小球，但更强大的转录组方法已应用于许多小鼠模型和少数人类肾小球疾病。在这里，我们使用标准化方案对来自小鼠、大鼠和人类的正常和病变肾小球中目前可用的肾小球表达数据进行了荟萃分析。结果表明，肾小球转录组学在确定致病途径、疾病监测以及使用动物模型研究人类肾小球疾病方面具有一定的可行性。我们还发现，目前不同疾病数据集之间没有特定的共识生物标志物或途径，这表明可能存在疾病特异性的机制和表达谱。因此，进一步的转录组学和蛋白质组学分析，特别是对疾病动态变化的分析，可能会导致新的诊断工具和特定的药物治疗方法。
