An expression profile of human pancreatic islet mRNAs by Serial Analysis of Gene Expression (SAGE)
An expression profile of human pancreatic islet mRNAs by Serial Analysis of Gene Expression (SAGE)
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DOI：
10.1007/s00125-003-1300-8
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发表时间：
2004-02-01
期刊：
影响因子：
8.2
通讯作者：
Permutt, MA
中科院分区：
文献类型：
--
作者：
Cras-Méneur, C;Inoue, H;Permutt, MA
目的/假说。人类基因组计划旨在鉴定所有基因，最终目标是评估生理和疾病状态下的相对表达水平。本研究的目的是利用基因表达序列分析方法鉴定人类胰岛中最丰富的转录本及其相对表达水平。通过将cDNA切割成小而均匀的片段（标签）并将其拼接成更大的克隆，可以估计cDNA文库中基因的身份和相对丰度。从三个人类文库中获得了大约49,000个SAGE标签：(i)胰腺梯度纯化胰岛（ii）通过手工采摘进一步单独分离的胰岛，以及（iii）胰腺外分泌组织。所鉴定的每个基因的相对丰度由标签的频率近似。基因本体论功能显示，这三个文库都包含了主要编码分泌因子的转录本。两个胰岛文库的比较显示不同程度的污染来自周围的外分泌组织（11% vs 25%）。去除外分泌转录本后，测定了2180个胰岛转录本的相对丰度。除了最常见的基因（如胰岛素、促甲状腺素、胰高血糖素）外，还发现了一些功能不明确的基因，如proSAAS或促分泌素。该信息可作为基因发现、组织间转录物丰度比较和糖尿病β细胞功能障碍研究中基因表达监测的资源。由于已鉴定基因的染色体位置是已知的，因此SAGE表达数据可用于确定糖尿病家族连锁峰候选基因的优先级。
Aims/hypothesis. The Human Genome Project seeks to identify all genes with the ultimate goal of evaluation of relative expression levels in physiology and in disease states. The purpose of the current study was the identification of the most abundant transcripts in human pancreatic islets and their relative expression levels using Serial Analysis of Gene Expression.Methods. By cutting cDNAs into small uniform fragments (tags) and concatemerizing them into larger clones, the identity and relative abundance of genes can be estimated for a cDNA library. Approximately 49 000 SAGE tags were obtained from three human libraries: (i) ficoll gradient-purified islets (ii) islets further individually isolated by hand-picking, and (iii) pancreatic exocrine tissue.Results. The relative abundance of each of the genes identified was approximated by the frequency of the tags. Gene ontology functions showed that all three libraries contained transcripts mostly encoding secreted factors. Comparison of the two islet libraries showed various degrees of contamination from the surrounding exocrine tissue (11 vs 25%). After removal of exocrine transcripts, the relative abundance of 2180 islet transcripts was determined. In addition to the most common genes (e.g. insulin, transthyretin, glucagon), a number of other abundant genes with ill-defined functions such as proSAAS or secretagogin, were also observed.Conclusion/interpretation. This information could serve as a resource for gene discovery, for comparison of transcript abundance between tissues, and for monitoring gene expression in the study of beta-cell dysfunction of diabetes. Since the chromosomal location of the identified genes is known, this SAGE expression data can be used in setting priorities for candidate genes that map to linkage peaks in families affected with diabetes.