BACKGROUND: Large-scale partial sequencing of cDNA libraries to generate expressed sequence tags (ESTs) is an effective means of discovering novel genes and characterizing transcription patterns in different tissues. To catalogue the identities and expression levels of genes in the cardiovascular system, we initiated large-scale sequencing and analysis of human cardiac cDNA libraries. METHODS AND RESULTS: Using automated DNA sequencing, we generated 43,285 ESTs from human heart cDNA libraries. An additional 41,619 ESTs were retrieved from public databases, for a total of 84,904 ESTs representing more than 26 million nucleotides of raw cDNA sequence data from 13 independent cardiovascular system-based cDNA libraries. Of these, 55% matched to known genes in the Genbank/EMBL/DDBJ databases, 33% matched only to other ESTs, and 12% did not match to any known sequences (designated cardiovascular system-based ESTs, or CVbESTs). ESTs that matched to known genes were classified according to function, allowing for detection of differences in general transcription patterns between various tissues and developmental stages of the cardiovascular system. In silico Northern analysis of known gene matches identified widely expressed cardiovascular genes as well as genes putatively exhibiting greater tissue specificity or developmental stage specificity. More detailed analysis identified 48 genes potentially overexpressed in cardiac hypertrophy, at least 10 of which were previously documented as differentially expressed. Computer-based chromosomal localizations of 1048 cardiac ESTs were performed to further assist in the search for disease-related genes. CONCLUSIONS: These data represent the most extensive compilation of cardiovascular gene expression information to date. They further demonstrate the untapped potential of genome research for investigating questions related to cardiovascular biology and represent a first-generation genome-based resource for molecular cardiovascular medicine.
BACKGROUND: Large-scale partial sequencing of cDNA libraries to generate expressed sequence tags (ESTs) is an effective means of discovering novel genes and characterizing transcription patterns in different tissues. To catalogue the identities and expression levels of genes in the cardiovascular system, we initiated large-scale sequencing and analysis of human cardiac cDNA libraries. METHODS AND RESULTS: Using automated DNA sequencing, we generated 43,285 ESTs from human heart cDNA libraries. An additional 41,619 ESTs were retrieved from public databases, for a total of 84,904 ESTs representing more than 26 million nucleotides of raw cDNA sequence data from 13 independent cardiovascular system-based cDNA libraries. Of these, 55% matched to known genes in the Genbank/EMBL/DDBJ databases, 33% matched only to other ESTs, and 12% did not match to any known sequences (designated cardiovascular system-based ESTs, or CVbESTs). ESTs that matched to known genes were classified according to function, allowing for detection of differences in general transcription patterns between various tissues and developmental stages of the cardiovascular system. In silico Northern analysis of known gene matches identified widely expressed cardiovascular genes as well as genes putatively exhibiting greater tissue specificity or developmental stage specificity. More detailed analysis identified 48 genes potentially overexpressed in cardiac hypertrophy, at least 10 of which were previously documented as differentially expressed. Computer-based chromosomal localizations of 1048 cardiac ESTs were performed to further assist in the search for disease-related genes. CONCLUSIONS: These data represent the most extensive compilation of cardiovascular gene expression information to date. They further demonstrate the untapped potential of genome research for investigating questions related to cardiovascular biology and represent a first-generation genome-based resource for molecular cardiovascular medicine.
Authors: Anna Raskin; Stephan Lange; Katherine Banares; Robert C Lyon; Anke Zieseniss; Leonard K Lee; Katrina G Yamazaki; Henk L Granzier; Carol C Gregorio; Andrew D McCulloch; Jeffrey H Omens; Farah Sheikh Journal: J Biol Chem Date: 2012-07-09 Impact factor: 5.157
Authors: M Mao; G Fu; J S Wu; Q H Zhang; J Zhou; L X Kan; Q H Huang; K L He; B W Gu; Z G Han; Y Shen; J Gu; Y P Yu; S H Xu; Y X Wang; S J Chen; Z Chen Journal: Proc Natl Acad Sci U S A Date: 1998-07-07 Impact factor: 11.205
Authors: S Bortoluzzi; L Rampoldi; B Simionati; R Zimbello; A Barbon; F d'Alessi; N Tiso; A Pallavicini; S Toppo; N Cannata; G Valle; G Lanfranchi; G A Danieli Journal: Genome Res Date: 1998-08 Impact factor: 9.043