Lande Jeffrey D, Patil Jagadish, Li Na, Berryman Todd R, King Richard A, Hertz Marshall I
Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota 55405, USA.
Proc Am Thorac Soc. 2007 Jan;4(1):44-51. doi: 10.1513/pats.200605-110JG.
Gene expression microarrays can estimate the prevalence of mRNA for thousands of genes in a small sample of cells or tissue. Organ transplant researchers are increasingly using microarrays to identify specific patterns of gene expression that predict and characterize acute and chronic rejection, and to improve our understanding of the mechanisms underlying organ allograft dysfunction. We used microarrays to assess gene expression in bronchoalveolar lavage cell samples from lung transplant recipients with and without acute rejection on simultaneous lung biopsies. These studies showed increased expression during acute rejection of genes involved in inflammation, apoptosis, and T-cell activation and proliferation. We also studied gene expression during the evolution of airway obliteration in a murine heterotopic tracheal transplant model of chronic rejection. These studies demonstrated specific patterns of gene expression at defined time points after transplantation in allografts, whereas gene expression in isografts reverted back to that of native tracheas within 2 wk after transplantation. These studies demonstrate the potential power of microarrays to identify biomarkers of acute and chronic lung rejection. The application of new genetic, genomic, and proteomic technologies is in its infancy, and the microarray-based studies described here are clearly only the beginning of their application to lung transplantation. The massive amount of data generated per tissue or cell sample has spawned an outpouring of invention in the bioinformatics field, which is developing methodologies to turn data into meaningful and reproducible clinical and mechanistic inferences.
基因表达微阵列能够在少量细胞或组织样本中估算数千种基因的mRNA丰度。器官移植研究人员越来越多地使用微阵列来识别预测和表征急性与慢性排斥反应的特定基因表达模式,并增进我们对器官同种异体移植功能障碍潜在机制的理解。我们使用微阵列来评估肺移植受者支气管肺泡灌洗细胞样本中的基因表达,这些受者在同期肺活检时有的发生了急性排斥反应,有的则没有。这些研究表明,在急性排斥反应期间,参与炎症、细胞凋亡以及T细胞活化与增殖的基因表达增加。我们还在慢性排斥反应的小鼠异位气管移植模型中研究了气道闭塞演变过程中的基因表达。这些研究证明了同种异体移植在移植后特定时间点的特定基因表达模式,而异种移植的基因表达在移植后2周内恢复到天然气管的基因表达水平。这些研究证明了微阵列在识别急性和慢性肺排斥反应生物标志物方面的潜在能力。新的遗传、基因组和蛋白质组技术的应用尚处于起步阶段,这里描述的基于微阵列的研究显然只是它们应用于肺移植的开端。每个组织或细胞样本产生的大量数据催生了生物信息学领域的大量发明,该领域正在开发将数据转化为有意义且可重复的临床和机制推断的方法。