{"title":"9 Genome-wide Views of Aging Gene Networks","authors":"Stuart K. Kim","doi":"10.1101/087969824.51.215","DOIUrl":null,"url":null,"abstract":"Aging is a complex process involving the additive effects of many genetic pathways (Kirkwood and Austad 2000). To embrace the complexity of aging, an attractive approach is to use DNA microarrays to scan the entire genome for genes that change expression as a function of age or under conditions when longevity is extended. The list of age-regulated genes provides clues about genetic pathways and mechanisms that underlie the aging process. In addition to single-gene analysis, the combined transcriptional profile of aging can act as a molecular phenotype of old age. During the last 20 years, there has been a great deal of effort to search for biomarkers of aging, and recent studies have shown that expression profiles of aging derived from DNA microarray experiments may provide this long-desired goal. A gene expression signature for aging is a quantitative phenotype that gives a high-resolution view of the aging process, much like using transcriptional profiles of cancer to inform about their severity or malignancy. Previously, one could recognize old versus young individuals in a photograph, or old versus young tissue on a microscope slide. Now it is possible to recognize old versus young genetic networks by analyzing expression levels of the entire set of age-regulated genes (Fig. 1). Unlike photographs or micrographs, expression data from DNA microarrays are quantitative, and thus it is possible to compare age-related transcriptional profiles between different tissues, between different conditions that affect longevity, and even between diverse species. Such comparisons are not possible by browsing images of...","PeriodicalId":10493,"journal":{"name":"Cold Spring Harbor Monograph Archive","volume":"94 1","pages":"215-235"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cold Spring Harbor Monograph Archive","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/087969824.51.215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Aging is a complex process involving the additive effects of many genetic pathways (Kirkwood and Austad 2000). To embrace the complexity of aging, an attractive approach is to use DNA microarrays to scan the entire genome for genes that change expression as a function of age or under conditions when longevity is extended. The list of age-regulated genes provides clues about genetic pathways and mechanisms that underlie the aging process. In addition to single-gene analysis, the combined transcriptional profile of aging can act as a molecular phenotype of old age. During the last 20 years, there has been a great deal of effort to search for biomarkers of aging, and recent studies have shown that expression profiles of aging derived from DNA microarray experiments may provide this long-desired goal. A gene expression signature for aging is a quantitative phenotype that gives a high-resolution view of the aging process, much like using transcriptional profiles of cancer to inform about their severity or malignancy. Previously, one could recognize old versus young individuals in a photograph, or old versus young tissue on a microscope slide. Now it is possible to recognize old versus young genetic networks by analyzing expression levels of the entire set of age-regulated genes (Fig. 1). Unlike photographs or micrographs, expression data from DNA microarrays are quantitative, and thus it is possible to compare age-related transcriptional profiles between different tissues, between different conditions that affect longevity, and even between diverse species. Such comparisons are not possible by browsing images of...
衰老是一个复杂的过程,涉及许多遗传途径的加性效应(Kirkwood and Austad 2000)。为了接受衰老的复杂性,一个有吸引力的方法是使用DNA微阵列扫描整个基因组,寻找随着年龄或寿命延长而改变表达的基因。年龄调节基因的列表为衰老过程背后的遗传途径和机制提供了线索。除了单基因分析外,衰老的组合转录谱可以作为老年的分子表型。在过去的20年里,人们一直在努力寻找衰老的生物标志物,最近的研究表明,从DNA微阵列实验中获得的衰老表达谱可能提供了这一长期期望的目标。衰老的基因表达特征是一种定量表型,它提供了衰老过程的高分辨率视图,就像使用癌症的转录谱来了解其严重程度或恶性程度一样。以前,人们可以在照片中识别出老年人和年轻人,或者在显微镜载玻片上识别出老年人和年轻人。现在,通过分析整个年龄调节基因的表达水平,可以识别年老与年轻的遗传网络(图1)。与照片或显微照片不同,DNA微阵列的表达数据是定量的,因此可以比较不同组织之间、影响寿命的不同条件之间,甚至不同物种之间与年龄相关的转录谱。通过浏览……的图片是不可能进行这样的比较的。