Analysis of mucosal gene expression in inflammatory bowel disease by parallel oligonucleotide arrays.
Analysis of mucosal gene expression in inflammatory bowel disease by parallel oligonucleotide arrays.
复制标题
通过平行寡核苷酸阵列分析炎症性肠病的粘膜基因表达。
DOI：
10.1152/physiolgenomics.2000.4.1.1
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发表时间：
2000
影响因子：
4.6
通讯作者：
Harrington,CA
中科院分区：
文献类型：
--
作者：
Dieckgraefe,BK;Stenson,WF;Korzenik,JR;Swanson,PE;Harrington,CA
能够同时测量来自受影响和正常个体的临床标本中数千个基因表达的DNA阵列有可能提供以前不可能的关于疾病发病机制的信息。很少有研究将mRNA分析应用于涉及肠粘膜等复杂组织的疾病，这反映了这种类型的分析所固有的独特挑战。我们报告溃疡性结肠炎（UC）患者和炎症和非炎症对照标本的粘膜基因表达的分析。基因可用作免疫调节分子的细胞募集、活化和粘膜合成的标记物。自组织图谱被应用于聚类和分析基因表达模式，并与组织病理学评分配对，以识别与疾病活动增加相关的基因。聚类是基于个体标本间表达水平的差异实现的。几种炎症介质被确定为活动性UC的特征性组织学特征的可能决定因素。这些结果为功能基因组学应用于更大的炎症性肠病人群的基因发现提供了原理证明，以促进基于基因表达特征的疾病亚组的鉴定，并用于预测疾病行为或最佳治疗干预。
DNA arrays capable of simultaneously measuring expression of thousands of genes in clinical specimens from affected and normal individuals have the potential to provide information about disease pathogenesis not previously possible. Few studies have applied mRNA profiling to diseases involving complex tissues like the intestinal mucosa, reflecting the unique challenges inherent to this type of analysis. We report the analysis of mucosal gene expression in ulcerative colitis (UC) patients and inflamed and noninflamed control specimens. Genes can be used as markers for cell recruitment, activation, and mucosal synthesis of immunoregulatory molecules. Self-organizing maps were applied to cluster and analyze gene expression patterns and were paired with histopathological scores to identify genes associated with increased disease activity. Clustering was achieved on the basis of differences in expression levels across individual specimens. Several inflammatory mediators were identified as likely determinants of characteristic histological features of active UC. These results provide proof of principle for application of functional genomics to larger inflammatory bowel disease populations for gene discovery, to facilitate identification of disease subgroups on the basis of gene expression signatures, and for prediction of disease behavior or optimal therapeutic intervention.