Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feat...Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.展开更多
Objective: To monitor the systemic gene expression profile in a murine model of lipopolysaccharide-induced acute lung injury. Methods: Acute lung injury was induced by intratracheal injection of lipopolysaccharide in ...Objective: To monitor the systemic gene expression profile in a murine model of lipopolysaccharide-induced acute lung injury. Methods: Acute lung injury was induced by intratracheal injection of lipopolysaccharide in 3 mice. Another 3 normal mice receiving same volume of normal saline were taken as the controls. The comprehensive gene expression profile was monitored by the recently modified long serial analysis of gene expression. Results: A total of 24 670 tags representing 12 168 transcripts in the control mice and 26 378 tags representing 13 397 transcripts in the mice with lung injury were identified respectively. There were 11 transcripts increasing and 7 transcripts decreasing more than 10 folds in the lipopolysaccharide-treated mice. The most overexpressed genes in the mice with lung injury included serum amyloid A3, metallothionein 2, lipocalin 2, cyclin-dependent kinase inhibitor 1A, lactate dehydrogenase 1, melatonin receptor, S100 calcium-binding protein A9, natriuretic peptide precursor, etc. Mitogen activated protein kinase 3, serum albumin, complement component 1 inhibitor, and ATP synthase were underexpressed in the lung injury mice. Conclusions: Serial analysis of gene expression provides a molecular characteristic of acute lung injury.展开更多
Chronic kidney disease （CKD） is a major public health problem that affects about 10% of the general population. Current approaches to characterize the category and progression of CKD are normally based on renal hist...Chronic kidney disease （CKD） is a major public health problem that affects about 10% of the general population. Current approaches to characterize the category and progression of CKD are normally based on renal histopathological results and clinical parameters. However, this information is not sufficient to predict CKD progression risk reliably or to guide preventive interventions. Nowadays, the appearance of systems biology has brought forward the concepts of ＂-omics＂ technologies, including genomics, transcriptomics, proteomics, and metabolomics. Systems biology, together with molecular analysis approaches such as microarray analysis, genome-wide association studies （GWAS）, and serial analysis of gene expression （SAGE）, has provided the framework for a comprehensive analysis of renal disease and serves as a starting point for generating novel molecular diagnostic tools for use in nephrology. In particular, analysis of urinary mRNA and protein levels is rapidly evolving as a non-invasive approach for CKD monitoring. All these systems biological molecular approaches are required for application of the concept of ＂personalized medicine＂ to progressive CKD, which will result in tailoring therapy for each patient, in contrast to the ＂one-size-fits-all＂ therapies currently in use.展开更多
基金Supported by the National Natural Science Foundation of China (No. 50877004)
文摘Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.
文摘Objective: To monitor the systemic gene expression profile in a murine model of lipopolysaccharide-induced acute lung injury. Methods: Acute lung injury was induced by intratracheal injection of lipopolysaccharide in 3 mice. Another 3 normal mice receiving same volume of normal saline were taken as the controls. The comprehensive gene expression profile was monitored by the recently modified long serial analysis of gene expression. Results: A total of 24 670 tags representing 12 168 transcripts in the control mice and 26 378 tags representing 13 397 transcripts in the mice with lung injury were identified respectively. There were 11 transcripts increasing and 7 transcripts decreasing more than 10 folds in the lipopolysaccharide-treated mice. The most overexpressed genes in the mice with lung injury included serum amyloid A3, metallothionein 2, lipocalin 2, cyclin-dependent kinase inhibitor 1A, lactate dehydrogenase 1, melatonin receptor, S100 calcium-binding protein A9, natriuretic peptide precursor, etc. Mitogen activated protein kinase 3, serum albumin, complement component 1 inhibitor, and ATP synthase were underexpressed in the lung injury mice. Conclusions: Serial analysis of gene expression provides a molecular characteristic of acute lung injury.
文摘Chronic kidney disease （CKD） is a major public health problem that affects about 10% of the general population. Current approaches to characterize the category and progression of CKD are normally based on renal histopathological results and clinical parameters. However, this information is not sufficient to predict CKD progression risk reliably or to guide preventive interventions. Nowadays, the appearance of systems biology has brought forward the concepts of ＂-omics＂ technologies, including genomics, transcriptomics, proteomics, and metabolomics. Systems biology, together with molecular analysis approaches such as microarray analysis, genome-wide association studies （GWAS）, and serial analysis of gene expression （SAGE）, has provided the framework for a comprehensive analysis of renal disease and serves as a starting point for generating novel molecular diagnostic tools for use in nephrology. In particular, analysis of urinary mRNA and protein levels is rapidly evolving as a non-invasive approach for CKD monitoring. All these systems biological molecular approaches are required for application of the concept of ＂personalized medicine＂ to progressive CKD, which will result in tailoring therapy for each patient, in contrast to the ＂one-size-fits-all＂ therapies currently in use.
