Regulation of Glycan Structures in Animal Tissues
2008; Elsevier BV; Volume: 283; Issue: 25 Linguagem: Inglês
10.1074/jbc.m801964200
ISSN1083-351X
AutoresAlison V. Nairn, William S. York, Kyle Harris, Erica M. Hall, J. Michael Pierce, Kelley W. Moremen,
Tópico(s)RNA modifications and cancer
ResumoGlycan structures covalently attached to proteins and lipids play numerous roles in mammalian cells, including protein folding, targeting, recognition, and adhesion at the molecular or cellular level. Regulating the abundance of glycan structures on cellular glycoproteins and glycolipids is a complex process that depends on numerous factors. Most models for glycan regulation hypothesize that transcriptional control of the enzymes involved in glycan synthesis, modification, and catabolism determines glycan abundance and diversity. However, few broad-based studies have examined correlations between glycan structures and transcripts encoding the relevant biosynthetic and catabolic enzymes. Low transcript abundance for many glycan-related genes has hampered broad-based transcript profiling for comparison with glycan structural data. In an effort to facilitate comparison with glycan structural data and to identify the molecular basis of alterations in glycan structures, we have developed a medium-throughput quantitative real time reverse transcriptase-PCR platform for the analysis of transcripts encoding glycan-related enzymes and proteins in mouse tissues and cells. The method employs a comprehensive list of >700 genes, including enzymes involved in sugar-nucleotide biosynthesis, transporters, glycan extension, modification, recognition, catabolism, and numerous glycosylated core proteins. Comparison with parallel microarray analyses indicates a significantly greater sensitivity and dynamic range for our quantitative real time reverse transcriptase-PCR approach, particularly for the numerous low abundance glycan-related enzymes. Mapping of the genes and transcript levels to their respective biosynthetic pathway steps allowed a comparison with glycan structural data and provides support for a model where many, but not all, changes in glycan abundance result from alterations in transcript expression of corresponding biosynthetic enzymes. Glycan structures covalently attached to proteins and lipids play numerous roles in mammalian cells, including protein folding, targeting, recognition, and adhesion at the molecular or cellular level. Regulating the abundance of glycan structures on cellular glycoproteins and glycolipids is a complex process that depends on numerous factors. Most models for glycan regulation hypothesize that transcriptional control of the enzymes involved in glycan synthesis, modification, and catabolism determines glycan abundance and diversity. However, few broad-based studies have examined correlations between glycan structures and transcripts encoding the relevant biosynthetic and catabolic enzymes. Low transcript abundance for many glycan-related genes has hampered broad-based transcript profiling for comparison with glycan structural data. In an effort to facilitate comparison with glycan structural data and to identify the molecular basis of alterations in glycan structures, we have developed a medium-throughput quantitative real time reverse transcriptase-PCR platform for the analysis of transcripts encoding glycan-related enzymes and proteins in mouse tissues and cells. The method employs a comprehensive list of >700 genes, including enzymes involved in sugar-nucleotide biosynthesis, transporters, glycan extension, modification, recognition, catabolism, and numerous glycosylated core proteins. Comparison with parallel microarray analyses indicates a significantly greater sensitivity and dynamic range for our quantitative real time reverse transcriptase-PCR approach, particularly for the numerous low abundance glycan-related enzymes. Mapping of the genes and transcript levels to their respective biosynthetic pathway steps allowed a comparison with glycan structural data and provides support for a model where many, but not all, changes in glycan abundance result from alterations in transcript expression of corresponding biosynthetic enzymes. Carbohydrate structures attached to glycoproteins, glycolipids, and proteoglycans have been shown to play key roles in a variety of biological recognition events (1Varki A. Glycobiology. 1993; 3: 97-130Crossref PubMed Scopus (5004) Google Scholar). Although there are many examples of the contribution of N-glycans to the bioactivity, folding, localization, and immunogenicity of the attached polypeptide, the functional roles of individual oligosaccharide structures on a given glycoprotein are difficult to predict (1Varki A. Glycobiology. 1993; 3: 97-130Crossref PubMed Scopus (5004) Google Scholar, 2Lowe J.B. Cell. 2001; 104: 809-812Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar, 3Lowe J.B. Marth J.D. Annu. Rev. Biochem. 2003; 72: 643-691Crossref PubMed Scopus (529) Google Scholar, 4Ohtsubo K. Marth J.D. Cell. 2006; 126: 855-867Abstract Full Text Full Text PDF PubMed Scopus (2124) Google Scholar, 5Schachter H. Glycoconj. J. 2000; 17: 465-483Crossref PubMed Scopus (132) Google Scholar). At the cellular level, N-linked, O-linked, and glycolipid glycan structures have been shown to contribute to several essential aspects of biological recognition, including cell adhesion during development, immune surveillance, inflammatory reactions, hormone action, viral infection, arthritis, and metastasis of oncogenically transformed cells (6Brockhausen I. Schutzbach J. Kuhns W. Acta Anat. 1998; 161: 36-78Crossref PubMed Scopus (153) Google Scholar, 7Dennis J.W. Granovsky M. Warren C.E. BioEssays. 1999; 21: 412-421Crossref PubMed Scopus (358) Google Scholar, 8Haltiwanger R.S. Lowe J.B. Annu. Rev. Biochem. 2004; 73: 491-537Crossref PubMed Scopus (649) Google Scholar, 9Taniguchi N. Miyoshi E. Gu J. Honke K. Matsumoto A. Curr. Opin. Struct. Biol. 2006; 16: 561-566Crossref PubMed Scopus (99) Google Scholar). Most of our understanding of the roles of cellular glycosylation in physiology and pathology comes from a combination of glycan structural analysis on specific glycoproteins, cell surfaces, or total tissue extracts in combination with years of study on the biochemistry and enzymology of glycan biosynthetic and degradative enzymes (10Kornfeld R. Kornfeld S. Annu. Rev. Biochem. 1985; 54: 631-664Crossref PubMed Scopus (3779) Google Scholar, 11Schachter H. Clin. Biochem. 1984; 17: 3-14Crossref PubMed Scopus (70) Google Scholar, 12Schachter H. Biol. Cell. 1984; 51: 133-145Crossref PubMed Scopus (37) Google Scholar, 13Schachter H. Glycobiology. 1991; 1: 453-461Crossref PubMed Scopus (169) Google Scholar). Despite this array of biochemical and genetic information, very little is known about the global regulation of glycan synthesis and degradation. A major goal in the field of "glycobiology" is an understanding of how glycan structures are regulated in abundance and the impact that these changes have on the physiology and pathology of an organism. Several difficulties arise when attempting to examine the regulation of glycan structures in complex biological systems. Because glycan biosynthesis is a post-translational modification, it is not directly template-driven like the synthesis of polypeptide structures from genome-derived transcripts. Thus, numerous factors can impact the efficiency and penetrance of individual glycosylation steps on protein and lipid acceptors, including enzyme accessibility to glycan modification sites, the abundance of the respective protein or lipid acceptors, availability of sugar-nucleotide precursors, and relative enzyme levels or relative localization of biosynthetic enzymes that can compete for the same glycan substrates. Despite these complexities in glycan biosynthesis, several lines of evidence indicate that one of the major modes of regulating cellular glycosylation is transcriptional regulation of the enzymes involved in glycan synthesis and catabolism (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar). One method for testing whether the elaboration of glycan structures is controlled at the transcriptional level is by the comparison of glycan structural data with transcript abundance measurements in multiple biological samples, where differences in glycan structures are known to occur. The last decade has seen significant advancements in methods for glycan structural analysis providing increased breadth, depth, and sensitivity to the glycan structures detected and quantitated within a single experiment (15Aoki K. Perlman M. Lim J.M. Cantu R. Wells L. Tiemeyer M. J. Biol. Chem. 2007; 282: 9127-9142Abstract Full Text Full Text PDF PubMed Scopus (223) Google Scholar, 16Kui Wong N. Easton R.L. Panico M. Sutton-Smith M. Morrison J.C. Lattanzio F.A. Morris H.R. Clark G.F. Dell A. Patankar M.S. J. Biol. Chem. 2003; 278: 28619-28634Abstract Full Text Full Text PDF PubMed Scopus (210) Google Scholar, 17Sutton-Smith M. Morris H.R. Grewal P.K. Hewitt J.E. Bittner R.E. Goldin E. Schiffmann R. Dell A. Biochem. Soc. Symp. 2002; 69: 105-115Crossref Scopus (27) Google Scholar). Although these analyses have revealed critical changes in glycan structures during development or between biological samples, rarely have they been paired with broad-based transcript analysis to determine whether transcriptional regulation is the major mechanism driving the structural alterations (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar, 18Bax M. Garcia-Vallejo J.J. Jang-Lee J. North S.J. Gilmartin T.J. Hernandez G. Crocker P.R. Leffler H. Head S.R. Haslam S.M. Dell A. van Kooyk Y. J. Immunol. 2007; 179: 8216-8224Crossref PubMed Scopus (105) Google Scholar, 19Hemmoranta H. Satomaa T. Blomqvist M. Heiskanen A. Aitio O. Saarinen J. Natunen J. Partanen J. Laine J. Jaatinen T. Exp. Hematol. 2007; 35: 1279-1292Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar, 20Naito Y. Takematsu H. Koyama S. Miyake S. Yamamoto H. Fujinawa R. Sugai M. Okuno Y. Tsujimoto G. Yamaji T. Hashimoto Y. Itohara S. Kawasaki T. Suzuki A. Kozutsumi Y. Mol. Cell. Biol. 2007; 27: 3008-3022Crossref PubMed Scopus (141) Google Scholar, 21Smith F.I. Qu Q. Hong S.J. Kim K.S. Gilmartin T.J. Head S.R. Gene Expr. Patterns. 2005; 5: 740-749Crossref PubMed Scopus (20) Google Scholar, 22Yamamoto H. Takematsu H. Fujinawa R. Naito Y. Okuno Y. Tsujimoto G. Suzuki A. Kozutsumi Y. Plos ONE. 2007; 2: e1232Crossref PubMed Scopus (13) Google Scholar, 23Young Jr., W.W. Holcomb D.R. Ten Hagen K.G. Tabak L.A. Glycobiology. 2003; 13: 549-557Crossref PubMed Scopus (57) Google Scholar, 24Ishii A. Ikeda T. Hitoshi S. Fujimoto I. Torii T. Sakuma K. Nakakita S. Hase S. Ikenaka K. Glycobiology. 2007; 17: 261-276Crossref PubMed Scopus (49) Google Scholar). Transcript profiling of glycan-related genes has its own set of complexities. The enzymes involved in glycan synthesis and modification have been collated into multigene families based on sequence and structural similarities. In mammalian cells, glycosyltransferases number ∼200 members and are subdivided into 40 families (CAZy database (25Coutinho P.M. Deleury E. Davies G.J. Henrissat B. J. Mol. Biol. 2003; 328: 307-317Crossref PubMed Scopus (931) Google Scholar, 26Coutinho P.M. Henrissat B. Gilbert H.J. Davies G. Henrissat B. Svensson B Recent Advances in Carbohydrate Bioengineering. Royal Society of Chemistry, Cambridge, UK1999: 3-12Google Scholar)), but in many cases the acceptor specificity of individual family members is not known or potential enzymatic redundancy may exist between multiple members of the same enzyme family. Thus, one-to-one mapping of individual gene products to steps in glycan biosynthetic pathways is difficult to achieve or may have ambiguity among multiple family members. Existing web-based resources (CAZy (25Coutinho P.M. Deleury E. Davies G.J. Henrissat B. J. Mol. Biol. 2003; 328: 307-317Crossref PubMed Scopus (931) Google Scholar, 26Coutinho P.M. Henrissat B. Gilbert H.J. Davies G. Henrissat B. Svensson B Recent Advances in Carbohydrate Bioengineering. Royal Society of Chemistry, Cambridge, UK1999: 3-12Google Scholar), KEGG (27Kanehisa M. Araki M. Goto S. Hattori M. Hirakawa M. Itoh M. Katayama T. Kawashima S. Okuda S. Tokimatsu T. Yamanishi Y. Nucleic Acids Res. 2008; 36: D480-D484Crossref PubMed Scopus (4731) Google Scholar, 28Kanehisa M. Goto S. Nucleic Acids Res. 2000; 28: 27-30Crossref PubMed Scopus (18876) Google Scholar, 29Kanehisa M. Goto S. Hattori M. Aoki-Kinoshita K.F. Itoh M. Kawashima S. Katayama T. Araki M. Hirakawa M. Nucleic Acids Res. 2006; 34: D354-D357Crossref PubMed Scopus (2414) Google Scholar), Consortium for Functional Glycomics (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar, 18Bax M. Garcia-Vallejo J.J. Jang-Lee J. North S.J. Gilmartin T.J. Hernandez G. Crocker P.R. Leffler H. Head S.R. Haslam S.M. Dell A. van Kooyk Y. J. Immunol. 2007; 179: 8216-8224Crossref PubMed Scopus (105) Google Scholar, 21Smith F.I. Qu Q. Hong S.J. Kim K.S. Gilmartin T.J. Head S.R. Gene Expr. Patterns. 2005; 5: 740-749Crossref PubMed Scopus (20) Google Scholar), and SOURCE (30Diehn M. Sherlock G. Binkley G. Jin H. Matese J.C. Hernandez-Boussard T. Rees C.A. Cherry J.M. Botstein D. Brown P.O. Alizadeh A.A. Nucleic Acids Res. 2003; 31: 219-223Crossref PubMed Scopus (354) Google Scholar)) have collated and annotated many of the genes related to glycan biosynthesis, but comprehensive resources for mapping enzymes to complex glycan biosynthetic pathways for glycoprotein, glycolipid, and proteoglycan biosynthesis and catabolism are still in their early stages. An additional complexity for the study of glycan-related gene expression is the relatively low abundance of transcripts encoding many of the critical enzymes involved in glycan modifications. These low transcript levels make it difficult to employ broad-based survey methods, such as microarray approaches (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar, 18Bax M. Garcia-Vallejo J.J. Jang-Lee J. North S.J. Gilmartin T.J. Hernandez G. Crocker P.R. Leffler H. Head S.R. Haslam S.M. Dell A. van Kooyk Y. J. Immunol. 2007; 179: 8216-8224Crossref PubMed Scopus (105) Google Scholar, 19Hemmoranta H. Satomaa T. Blomqvist M. Heiskanen A. Aitio O. Saarinen J. Natunen J. Partanen J. Laine J. Jaatinen T. Exp. Hematol. 2007; 35: 1279-1292Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar, 20Naito Y. Takematsu H. Koyama S. Miyake S. Yamamoto H. Fujinawa R. Sugai M. Okuno Y. Tsujimoto G. Yamaji T. Hashimoto Y. Itohara S. Kawasaki T. Suzuki A. Kozutsumi Y. Mol. Cell. Biol. 2007; 27: 3008-3022Crossref PubMed Scopus (141) Google Scholar, 21Smith F.I. Qu Q. Hong S.J. Kim K.S. Gilmartin T.J. Head S.R. Gene Expr. Patterns. 2005; 5: 740-749Crossref PubMed Scopus (20) Google Scholar, 22Yamamoto H. Takematsu H. Fujinawa R. Naito Y. Okuno Y. Tsujimoto G. Suzuki A. Kozutsumi Y. Plos ONE. 2007; 2: e1232Crossref PubMed Scopus (13) Google Scholar, 24Ishii A. Ikeda T. Hitoshi S. Fujimoto I. Torii T. Sakuma K. Nakakita S. Hase S. Ikenaka K. Glycobiology. 2007; 17: 261-276Crossref PubMed Scopus (49) Google Scholar), for global transcriptome analyses. More focused approaches employing quantitative real time PCR (qRT-PCR) 2The abbreviations used are: qRT-PCR, quantitative real time polymerase chain reaction; MALDI-MS, matrix-assisted laser desorption ionizationmass spectrometry; RMA, robust multichip average; gDNA, genomic DNA; Rpl4, ribosomal protein L4; EST, expressed sequence tag; IGnT, I-GlcNAc transferase; GPI, glycosylphosphatidylinositol; NCAM, neural cell adhesion molecule. have been employed effectively (23Young Jr., W.W. Holcomb D.R. Ten Hagen K.G. Tabak L.A. Glycobiology. 2003; 13: 549-557Crossref PubMed Scopus (57) Google Scholar, 31Garcia-Vallejo J.J. Gringhuis S.I. van Dijk W. van Die I. Methods Mol. Biol. 2006; 347: 187-209PubMed Google Scholar, 32Garcia-Vallejo J.J. Van Dijk W. Van Het Hof B. Van Die I. Engelse M.A. Van Hinsbergh V.W. Gringhuis S.I. J. Cell. Physiol. 2006; 206: 203-210Crossref PubMed Scopus (63) Google Scholar), but this strategy has generally been restricted to the analysis of a relatively small number of target genes. We have chosen to develop a broad-based analytical platform for transcript analysis of glycan-related genes that has three key components. First, we have drawn on numerous publicly available resources and the primary literature to generate a comprehensive gene list encoding enzymes and proteins involved in glycobiology, including sugar-nucleotide biosynthesis, transporters, glycan extension, modification, recognition, catabolism, and numerous glycosylated core proteins (>700 genes in the mouse). Second, we have developed a robust, sensitive, and flexible qRT-PCR platform for transcript analysis using experimentally validated primer sets for all of the members of the mouse gene list. This strategy has allowed us to examine global changes in glycan-related transcripts for both highly expressed genes as well as low abundance transcripts that may play key roles in generating important glycan epitopes. Third, we have developed a set of detailed pathway diagrams for glycan biosynthesis and modification and initiated the mapping of all members of the gene list to their respective biochemical pathway steps. Initial use of these pathway diagrams has allowed the visual depiction of transcript abundance within a framework of glycan biosynthetic pathways as a means of correlating glycan structural data with transcript abundance. As an experimental framework for examining the regulation of glycan structures in mammalian systems, we have analyzed RNA samples derived from several adult mouse tissues and compared them with microarray data from parallel tissue samples and glycan structural data previously obtained by MALDI-MS approaches (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar, 16Kui Wong N. Easton R.L. Panico M. Sutton-Smith M. Morrison J.C. Lattanzio F.A. Morris H.R. Clark G.F. Dell A. Patankar M.S. J. Biol. Chem. 2003; 278: 28619-28634Abstract Full Text Full Text PDF PubMed Scopus (210) Google Scholar, 17Sutton-Smith M. Morris H.R. Grewal P.K. Hewitt J.E. Bittner R.E. Goldin E. Schiffmann R. Dell A. Biochem. Soc. Symp. 2002; 69: 105-115Crossref Scopus (27) Google Scholar). Greater sensitivity and dynamic range was found for our qRT-PCR approach compared with focused microarrays, particularly for the numerous low abundance glycan-related enzymes. Comparison with glycan structural data demonstrated numerous correlations between glycan structures and transcript abundance for their respective biosynthetic enzymes. Several cases were also noted where differences in glycan structures did not correlate with transcript abundance suggesting that regulation may occur at a post-transcriptional level. The analysis of glycan-related transcripts within the context of biosynthetic pathways also predicted differences in low abundance glycans consistent with previous observations of these structures in the literature. Compilation of the Glycan-related Gene List—Our murine glycan-related gene list was compiled from several sources, including the following: the database of Carbohydrate Active Enzymes (26Coutinho P.M. Henrissat B. Gilbert H.J. Davies G. Henrissat B. Svensson B Recent Advances in Carbohydrate Bioengineering. Royal Society of Chemistry, Cambridge, UK1999: 3-12Google Scholar), a web-based genomic resource for animal lectins (33Taylor M.E. Drickamer K. Introduction to Glycobiology, 2nd Ed. Oxford University Press, Oxford2006: 122-175Google Scholar) organized by Dr. Kurt Drikamer, the Kyoto Encyclopedia of Genes and Genomes (27Kanehisa M. Araki M. Goto S. Hattori M. Hirakawa M. Itoh M. Katayama T. Kawashima S. Okuda S. Tokimatsu T. Yamanishi Y. Nucleic Acids Res. 2008; 36: D480-D484Crossref PubMed Scopus (4731) Google Scholar, 28Kanehisa M. Goto S. Nucleic Acids Res. 2000; 28: 27-30Crossref PubMed Scopus (18876) Google Scholar, 29Kanehisa M. Goto S. Hattori M. Aoki-Kinoshita K.F. Itoh M. Kawashima S. Katayama T. Araki M. Hirakawa M. Nucleic Acids Res. 2006; 34: D354-D357Crossref PubMed Scopus (2414) Google Scholar); the gene list for the GLYCOv2 Gene Chip from the Consortium for Functional Genomics (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar, 18Bax M. Garcia-Vallejo J.J. Jang-Lee J. North S.J. Gilmartin T.J. Hernandez G. Crocker P.R. Leffler H. Head S.R. Haslam S.M. Dell A. van Kooyk Y. J. Immunol. 2007; 179: 8216-8224Crossref PubMed Scopus (105) Google Scholar, 21Smith F.I. Qu Q. Hong S.J. Kim K.S. Gilmartin T.J. Head S.R. Gene Expr. Patterns. 2005; 5: 740-749Crossref PubMed Scopus (20) Google Scholar); the microarray gene list from the Glyco-Chain Expression Laboratory (20Naito Y. Takematsu H. Koyama S. Miyake S. Yamamoto H. Fujinawa R. Sugai M. Okuno Y. Tsujimoto G. Yamaji T. Hashimoto Y. Itohara S. Kawasaki T. Suzuki A. Kozutsumi Y. Mol. Cell. Biol. 2007; 27: 3008-3022Crossref PubMed Scopus (141) Google Scholar, 22Yamamoto H. Takematsu H. Fujinawa R. Naito Y. Okuno Y. Tsujimoto G. Suzuki A. Kozutsumi Y. Plos ONE. 2007; 2: e1232Crossref PubMed Scopus (13) Google Scholar); the Transport Classification database (34Saier Jr., M.H. Tran C.V. Barabote R.D. Nucleic Acids Res. 2006; 34: D181-D186Crossref PubMed Scopus (637) Google Scholar); NCBI (www.ncbi.nlm.nih.gov (35Wheeler D.L. Barrett T. Benson D.A. Bryant S.H. Canese K. Chetvernin V. Church D.M. DiCuccio M. Edgar R. Federhen S. Geer L.Y. Kapustin Y. Khovayko O. Landsman D. Lipman D.J. Madden T.L. Maglott D.R. Ostell J. Miller V. Pruitt K.D. Schuler G.D. Sequeira E. Sherry S.T. Sirotkin K. Souvorov A. Starchenko G. Tatusov R.L. Tatusova T.A. Wagner L. Yaschenko E. Nucleic Acids Res. 2007; 35: D5-D12Crossref PubMed Scopus (726) Google Scholar)); SOURCE (30Diehn M. Sherlock G. Binkley G. Jin H. Matese J.C. Hernandez-Boussard T. Rees C.A. Cherry J.M. Botstein D. Brown P.O. Alizadeh A.A. Nucleic Acids Res. 2003; 31: 219-223Crossref PubMed Scopus (354) Google Scholar); contributions from collaborating investigators, and extensive searches of the primary literature (see Table 1 for gene list categories, member totals, and sources as well as supplemental Table 1 for the detailed gene list, including additional gene annotation information). Unique NCBI gene identifiers (GeneIDs) (36Maglott D. Ostell J. Pruitt K.D. Tatusova T. Nucleic Acids Res. 2007; 35: D26-D31Crossref PubMed Scopus (465) Google Scholar) for each member of the gene list were used to check for isoforms of a single gene to prevent duplications of gene entries in the list. Members of gene families that were >95% identical at the DNA sequence level were treated as the same gene, and one primer pair was designed to amplify a region that was 100% identical in sequence. Genes that encode proteins with multiple functions (i.e. glycosyltransferase activity and carbohydrate binding domains) were grouped by their catalytic activity to prevent redundant entries for primer design. Genes that encode proteins with two separate catalytic activities were listed under both categories in the gene list, but only one primer pair was designed for the transcript.TABLE 1Organization of the mouse glycan-related gene list A list of all glycan-related genes was compiled from several sources, as indicated in the Source column, and collated into groups and families based on enzyme or protein function and sequence. The full gene list, organized by the gene list prefix and containing additional annotation information, can be found in supplemental Table 1. Totals for each category of glycan-related genes are indicated by No. of members.GroupFamily or subgroupGene list prefixNo. of membersSourceaSources are identified by letters A–H. A was compiled from the database of Carbohydrate Active Enzymes (25, 26). B was compiled from gene list of the GLYCOv2 gene chip from the Consortium for Functional Glycomics (14, 18, 21) and the microarray gene list from the Glyco-Chain Expression Laboratory (20, 22). C was compiled from the web-based genomic resource for animal lectins (33). D was from K. Drickamer, personal communication. E was compiled from the Kyoto Encyclopedia of Genes and Genomes (27–29). F was compiled from the Transport Classification Database (34). G was from gene lists provided from collaborative investigators. H was compiled and edited based on database information at NCBI (www.ncbi.nlm.nih.gov (35)) SOURCE (30), and the primary literatureGlycosyltransferasesbCAZy entries >90% identical at the nucleotide level were combined into a single entry in the gene listCAZy families: GT 1–4, 6–8, 10–14, 17, 18, 21–25, 27, 29, 31–33, 35, 39, 41, 43, 47, 49–51, 54, 57–59, 61, 64–66, 68, 76GT190A, HGlycosylhydrolasesbCAZy entries >90% identical at the nucleotide level were combined into a single entry in the gene listCAZy families: GH 1, 2, 13, 18, 20, 22, 23, 27, 29–31, 33, 35, 37–39, 47, 56, 59, 63, 65, 79, 84, 85, 89, 99GH84A, HCarbohydrate-binding modulescBifunctional carbohydrate-binding module genes with transferase or hydrolase activities are listed as GTs or GHs to avoid redundancyCAZy families: CBM 13, 20, 21CBM10A, HCarbohydrate esterasesCAZy families: CE 1, 9, 10, 13CE21A, HLectinsC-typeLCxdWithin this family numerous subgroups are distinguished within the full gene list and distinctive prefixes have been appended for each subgroup as indicated by the "x." See supplemental Table 1 for details92C, HI-typeLIxdWithin this family numerous subgroups are distinguished within the full gene list and distinctive prefixes have been appended for each subgroup as indicated by the "x." See supplemental Table 1 for details16C, HCalnexin-likeLCNX/LCRT2C, HS-typeLSG11C, HP-typeLPR2C, HL-typeLL4C, HM-typeGH47eData were also classified as GH family members, not included in total gene number7C, HF-boxLFBX1C, HMiscellaneous glycan-bindingGH18,eData were also classified as GH family members, not included in total gene number LAx,dWithin this family numerous subgroups are distinguished within the full gene list and distinctive prefixes have been appended for each subgroup as indicated by the "x." See supplemental Table 1 for details LMHC, LGB21DLectin-associated proteinsLAP3DCarbohydrate synthesis and metabolismNucleotide synthesisNSN42E, HTransportersTR28F, HMetabolic enzymesCM24E, HSulfate-relatedArylsulfatasesSAS4B, E, HSulfatasesST4B, E, HSulfohydrolasesSH1B, E, HSulfotransferasesSTR36B, E, HGlycolipid-relatedGL34G, HGlycosaminoglycan-relatedCore proteinsGR37G, HFibroblast growth factorsFGF20B, HFibroblast growth factor receptorsFGFR4B, HLipid-linked oligosaccharide pathway-relatedLLO12B, HGPI anchor biosynthesis-relatedGPI19B, HMiscellaneousMISC8B, HHousekeeping genes23B, HTotal745a Sources are identified by letters A–H. A was compiled from the database of Carbohydrate Active Enzymes (25Coutinho P.M. Deleury E. Davies G.J. Henrissat B. J. Mol. Biol. 2003; 328: 307-317Crossref PubMed Scopus (931) Google Scholar, 26Coutinho P.M. Henrissat B. Gilbert H.J. Davies G. Henrissat B. Svensson B Recent Advances in Carbohydrate Bioengineering. Royal Society of Chemistry, Cambridge, UK1999: 3-12Google Scholar). B was compiled from gene list of the GLYCOv2 gene chip from the Consortium for Functional Glycomics (14Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.-K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (140) Google Scholar, 18Bax M. Garcia-Vallejo J.J. Jang-Lee J. North S.J. Gilmartin T.J. Hernandez G. Crocker P.R. Leffler H. Head S.R. Haslam S.M. Dell A. van Kooyk Y. J. Immunol. 2007; 179: 8216-8224Crossref PubMed Scopus (105) Google Scholar, 21Smith F.I. Qu Q. Hong S.J. Kim K.S. Gilmartin T.J. Head S.R. Gene Expr. Patterns. 2005; 5: 740-749Crossref PubMed Scopus (20) Google Scholar) and the microarray gene list from the Glyco-Chain Expression Laboratory (20Naito Y. Takematsu H. Koyama S. Miyake S. Yamamoto H. Fujinawa R. Sugai M. Okuno Y. Tsujimoto G. Yamaji T. Hashimoto Y. Itohara S. Kawasaki T. Suzuki A. 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Nucleic Acids Res. 2006; 34:
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