基因表达的系列分析方法研究进展

基因表达的系列分析（SAGE）是探讨组织或器官在不同条件下基因表达丰度以及差异表达的一种有效方法。本文介绍了SAGE方法的详细机理并且对SAGE方法学的改进进行了综述。     

SIRT3 gene expression: a link between inherited mitochondrial DNA variants and oxidative stress

Signaling pathways between mitochondrial and nuclear genomes are activated to preserve cellular homeostasis, especially in the event of stress. Using cybrid cell lines, we investigated whether inherited mitochondrial DNA (mtDNA) variants modulate the expression profiles of mammalian sirtuins (SIRT1-7) under oxidative stress conditions. We found that the expression of the SIRT3 gene was down-regulated in cybrids harboring mtDNA of the J haplogroup, which correlated with mitochondrial function, resulting in a decline of NAD(+)/NADH and ATP levels. Overall, the data reported here highlight a link between SIRT3, mitochondrial DNA variability and mitochondrial functionality, three fundamental components of the cellular stress response.     

Comparative Analysis of Proteome and Transcriptome in Human Hepatocellular Carcinoma using 2D-DIGE and SAGE

Proteome analysis of human hepatocellular carcinoma was conducted using two-dimensional difference gel electrophoresis, and the protein expression profiles were compared to the mRNA expression profiles made from serial analysis of gene expression (SAGE) in identical samples from a single patient. Image-to-image analysis of protein abundances together with protein identification by peptide mass fingerprinting yielded the protein expression profiles. A total of 188 proteins were identified, and the expression profiles of 164 proteins which had the corresponding SAGE data were compared to the mRNA expression profiles. Among them, 40 proteins showed significant differences in the mRNA expression levels between non HCC and HCC. We compared expression changes of proteins with those of mRNAs. We found that the expression tendency of 24 proteins were similar to that of mRNA, whereas 16 proteins showed different or opposite tendency to the mRNA expression.     

Bayesian shrinkage estimation of the relative abundance of mRNA transcripts using SAGE

Serial analysis of gene expression (SAGE) is a technology for quantifying gene expression in biological tissue that yields count data that can be modeled by a multinomial distribution with two characteristics: skewness in the relative frequencies and small sample size relative to the dimension. As a result of these characteristics, a given SAGE sample may fail to capture a large number of expressed mRNA species present in the tissue. Empirical estimators of mRNA species' relative abundance effectively ignore these missing species, and as a result tend to overestimate the abundance of the scarce observed species comprising a vast majority of the total. We have developed a new Bayesian estimation procedure that quantifies our prior information about these characteristics, yielding a nonlinear shrinkage estimator with efficiency advantages over the MLE. Our prior is mixture of Dirichlets, whereby species are stochastically partitioned into abundant and scarce classes, each with its own multivariate prior. Simulation studies reveal our estimator has lower integrated mean squared error (IMSE) than the MLE for the SAGE scenarios simulated, and yields relative abundance profiles closer in Euclidean distance to the truth for all samples simulated. We apply our method to a SAGE library of normal colon tissue, and discuss its implications for assessing differential expression.     

Gene expression profiling in a renal cell carcinoma cell line: dissecting VHL and hypoxia-dependent pathways

The von Hippel-Lindau tumor suppressor, pVHL, is a key player in one of the best characterized hypoxia signaling pathways, the VHL-hypoxia-inducible factor (VHL-HIF) pathway. To better understand the role of VHL in the hypoxia signaling pathways of tumor cells, we used serial analysis of gene expression (SAGE) to investigate hypoxia-regulated gene expression in renal carcinoma cells (786-0), with and without VHL. The gene expression profiles of the cancer cells were compared to SAGE profiles from normal renal proximal tubule cells grown under both normoxia and hypoxia. The data suggest that the role of VHL as a tumor suppressor may be more complex than previously thought. Further, the data reveal that renal carcinoma cells have evolved an alternative hypoxia signaling pathway(s) compared with normal renal cells. These alternative hypoxia pathways demonstrate VHL-dependent and VHL-independent regulation. The genes involved in such pathways include those with potential importance in the physiological and pathological regulation of tumor growth and angiogenesis. Some of the genes identified as showing overexpression in the cancer cells, particularly those encoding secreted or membrane-bound proteins, could be potential biomarkers for tumors or targets for rational therapeutics that are dependent on VHL status.     

A comparative analysis of the information content in long and short SAGE libraries

Background  

Serial Analysis of Gene Expression (SAGE) is a powerful tool to determine gene expression profiles. Two types of SAGE libraries, ShortSAGE and LongSAGE, are classified based on the length of the SAGE tag (10 vs. 17 basepairs). LongSAGE libraries are thought to be more useful than ShortSAGE libraries, but their information content has not been widely compared. To dissect the differences between these two types of libraries, we utilized four libraries (two LongSAGE and two ShortSAGE libraries) generated from the hippocampus of Alzheimer and control samples. In addition, we generated two additional short SAGE libraries, the truncated long SAGE libraries (tSAGE), from LongSAGE libraries by deleting seven 5' basepairs from each LongSAGE tag.     

Correct identification of genes from serial analysis of gene expression tag sequences

SAGE (serial analysis of gene expression) is a remarkable technique for genome-wide analysis of gene expression. It is crucial to understand the extent to which SAGE can accurately indicate a gene or expressed sequence tag (EST) with a single tag. We analyzed the effect of the size of SAGE tag on gene identification. Our observation indicates that SAGE tags are in general not long enough to achieve the degree of uniqueness of identification originally envisaged. Our observations also indicate that the limitation of using SAGE tag to identify a gene can be overcome by converting SAGE tags into longer 3' EST sequences with the generation of longer cDNA fragments from SAGE tages for gene identification (GLGI) method.