OBJECTIVE: To analyze the gene expression profile of human fetal cartilage by expressed sequence tags (ESTs). METHODS: A human fetal cartilage (8-12 weeks) cDNA library was constructed using the lambda ZAP Express vector. ESTs were obtained by partial sequencing of cDNA clones. The basic local alignment search tool algorithm was used to compare all generated ESTs to known sequences. RESULTS: A total of 13,155 ESTs were analyzed, of which 8696 ESTs (66.1%) matched known genes, 53 ESTs (0.4%) were putatively novel (with no match) and the rest matched other ESTs, genomic DNA and repetitive sequences. Importantly, we identified 2448 unique known genes through non-redundancy analysis of the known gene matches, which were then functionally categorized. The tissue specificity of this library was reflected by its EST profile of the extracellular matrix (ECM) proteins. Collagens were the major transcripts, representing 68.5% of the ECM proteins. Proteoglycans were the second most abundant, constituting 9.5%. Collagen type II was the most abundant gene of all. Glypican 3, decorin and aggrecan were the major transcripts of proteoglycans. Many genes involved in cartilage development were identified, such as insulin-like growth factor-II, its receptor and binding proteins, connective tissue growth factor and fibroblast growth factors. Proteases and their regulatory factors were also identified, including matrix metalloprotease 2 and tissue inhibitor of metalloproteinase 1. CONCLUSIONS: The EST approach is an effective way of characterizing the genes expressed in cartilage. These data represent the most extensive molecular information on human fetal cartilage to date. The availability of this information will serve as a basis for further research to identify genes that are essential in cartilage development.
OBJECTIVE: To analyze the gene expression profile of human fetal cartilage by expressed sequence tags (ESTs). METHODS: A human fetal cartilage (8-12 weeks) cDNA library was constructed using the lambda ZAP Express vector. ESTs were obtained by partial sequencing of cDNA clones. The basic local alignment search tool algorithm was used to compare all generated ESTs to known sequences. RESULTS: A total of 13,155 ESTs were analyzed, of which 8696 ESTs (66.1%) matched known genes, 53 ESTs (0.4%) were putatively novel (with no match) and the rest matched other ESTs, genomic DNA and repetitive sequences. Importantly, we identified 2448 unique known genes through non-redundancy analysis of the known gene matches, which were then functionally categorized. The tissue specificity of this library was reflected by its EST profile of the extracellular matrix (ECM) proteins. Collagens were the major transcripts, representing 68.5% of the ECM proteins. Proteoglycans were the second most abundant, constituting 9.5%. Collagen type II was the most abundant gene of all. Glypican 3, decorin and aggrecan were the major transcripts of proteoglycans. Many genes involved in cartilage development were identified, such as insulin-like growth factor-II, its receptor and binding proteins, connective tissue growth factor and fibroblast growth factors. Proteases and their regulatory factors were also identified, including matrix metalloprotease 2 and tissue inhibitor of metalloproteinase 1. CONCLUSIONS: The EST approach is an effective way of characterizing the genes expressed in cartilage. These data represent the most extensive molecular information on human fetal cartilage to date. The availability of this information will serve as a basis for further research to identify genes that are essential in cartilage development.
Authors: Ming-Yiu Yeung; David K Smith; Matthew S Y Chan; Cheuk M Li; Brian C Wong; Kenneth M C Cheung; Keith D K Luk; Kathryn S E Cheah; Pak Sham; Danny Chan; You-Qiang Song Journal: BMC Genet Date: 2007-02-23 Impact factor: 2.797
Authors: Maurijn Y Kessels; Leonie F A Huitema; Sjef Boeren; Sander Kranenbarg; Stefan Schulte-Merker; Johan L van Leeuwen; Sacco C de Vries Journal: PLoS One Date: 2014-03-07 Impact factor: 3.240
Authors: Vincent A Funari; Allen Day; Deborah Krakow; Zachary A Cohn; Zugen Chen; Stanley F Nelson; Daniel H Cohn Journal: BMC Genomics Date: 2007-06-12 Impact factor: 3.969