DetoxiProt: an integrated database for detoxification proteins
- Zhen Yang†1,
- Ying Yu†2,
- Lei Yao1,
- Guangui Li2,
- Lin Wang2,
- Yiyao Hu1,
- Haibin Wei3,
- Li Wang4,
- Riadh Hammami5,
- Roxanne Razavi6,
- Yang Zhong7Email author and
- Xufang Liang2Email author
© Yang et al; licensee BioMed Central Ltd. 2011
Published: 30 November 2011
Detoxification proteins are a class of proteins for degradation and/or elimination of endogenous and exogenous toxins or medicines, as well as reactive oxygen species (ROS) produced by these materials. Most of these proteins are generated as a response to the stimulation of toxins or medicines. They are essential for the clearance of harmful substances and for maintenance of physiological balance in organisms. Thus, it is important to collect and integrate information on detoxification proteins.
To store, retrieve and analyze the information related to their features and functions, we developed the DetoxiProt, a comprehensive database for annotation of these proteins. This database provides detailed introductions about different classes of the detoxification proteins. Extensive annotations of these proteins, including sequences, structures, features, inducers, inhibitors, substrates, chromosomal location, functional domains as well as physiological-biochemical properties were generated. Furthermore, pre-computed BLAST results, multiple sequence alignments and evolutionary trees for detoxification proteins are also provided for evolutionary study of conserved function and pathways. The current version of DetoxiProt contains 5956 protein entries distributed in 628 organisms. An easy to use web interface was designed, so that annotations about each detoxification protein can be retrieved by browsing with a specific method or by searching with different criteria.
DetoxiProt provides an effective and efficient way of accessing the detoxification protein sequences and other high-quality information. This database would be a valuable source for toxicologists, pharmacologists and medicinal chemists. DetoxiProt database is freely available at http://lifecenter.sgst.cn/detoxiprot/.
Living organisms are exposed to the external environment and are continuously confronted with a variety of chemical compounds and xenobiotics. Many of these naturally occurring substances or man-made chemicals can be deleterious when inhaled or absorbed, or even hazardous to organisms, (e.g. plant toxins to prevent herbivory). Artificial chemicals include anti-cancer drugs, antibiotics produced by fungi, insecticide, herbicide and other environmental pollutants . Some kinds of toxins can lead the cell to produce large amounts of reactive oxygen species (ROS), though in some cases, ROS can be natural by-products of metabolic processes. ROS can react with many cellular molecules and cause damage or contribute to the development of various pathologies . Thus, self-protective mechanisms including catalytic biotransformation have evolved as an adaptation against various toxic chemical species. Cells possess various kinds of detoxification proteins that are capable of metabolizing a wide variety of toxins, mainly classified into Phase I and Phase II xenobiotic-metabolizing enzymes (XEMs) as well as antioxidant enzymes according to their functional mechanisms [3–5]. These proteins react directly with toxins to reduce their toxicity and enhance their solubility so that they can be easily eliminated from the cell.
Phase I reactions include oxidation, reduction and hydrolysis. A major function of the Phase I reaction is the transformation of substrates so that they can be easily eliminated or modified by reactive groups for subsequent Phase II reaction. The most important Phase I system is the cytochrome P450 enzyme (CYP). It is an important class of heme-containing monooxigenase that mainly catalyzes the oxidative reactions of the adjunction of an atom from molecular oxygen into a substrate. This superfamily can be functionally classified into three groups, with more than 50 protein members have been identified in humans . The genetic association between CYP superfamily and various diseases has been identified, such as cancer, fatty liver disease and Parkinson's disease [7–9]. Phase II reactions include glutathione (GSH) conjugation, glucuronidation, sulfation, acetylation, and methylation. Small polar molecules, such as glutathione, UDP-glucuronic acid (UDPGA) and acetyl coenzyme A (AcCoA), can be conjugated with the toxic compounds [10–12]. Glutathione conjugation is the primary Phase II reaction. The glutathione S-transferase (GST) catalyzes the conjugation of the thiol group of GSH, the tripeptide gamma-Glu-Cys-Gly, with a wide variety of electrophiles. It has been widely demonstrated that the polymorphism of GSTs is related to cancer susceptibility and patient survival . ROS produced by exogenous toxins or generated during the endogenous metabolic process can react with the oxidation enzymes and was eventually reduced by non-enzymatic antioxidant defenses, such as ascorbic acid (vitamin C), alpha-tocopherol (vitamin E) and GSH, or enzymatic antioxidant defenses, such as catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) [14, 15]. Relatively high levels of oxygen in the brain make it one of the vulnerable organs to ROS damage. Many neurodegenerative diseases are associated with oxidative damage such as Parkinson's disease . Thus, these proteins may constitute the major protective mechanism of the brain from damage due to oxidative stress .
Detoxification proteins have been found from invertebrates to vertebrates. More than one hundred genes encoding these proteins have been identified in humans. Large efforts have been made to understand the structural, functional and evolutionary basis of detoxification proteins, as well as their roles in disease development [18–20]. Many detoxification protein-related databases have been established to provide this kind of information, such as PeroxisomeDB  and PeroxiBase . These databases mainly focus on peroxidase related proteins, which constitute only part of the steps in cellular detoxification. There is currently no system for the classification and annotation of all these proteins. Thus, the DetoxiProt database was created to facilitate the efficient annotation and acquisition of information about the detoxification proteins. The aim of the DetoxiProt is to provide a useful platform for detoxification protein researchers in the fields of physiology, pharmacology, toxicology, food security, farm chemical development and environmental pollution related research. It provides relevant information for the function and evolutionary analysis of these proteins. In addition to the chemical features of the genes, the DetoxiProt features 3D structures, sub-cellular location, tissue expression and especially, the inducer, inhibitor and substrates of these enzymes. Presently, the database contains 5956 entries encompassing 20 different detoxification protein families.
Construction and content
Extra information from other databases and literature, as well as the predicted protein features were incorporated into each detoxification record. Protein cofactors, tissue distribution and cellular location were retrieved from the references and publicly available databases such as NCBI and UniProtKB, while experimentally determined protein structures were retrieved from protein databank (PDB) . DetoxiProt also provides the predicted protein physiological-biochemical properties including the molecular weight, theoretical pI, aliphatic index and Grand average of hydropathicity (GRAVY), which computed by the ProtParam tool from the Expasy server (http://www.expasy.org/tools/protpar-ref.html). To provide fast protein classification, protein domains were predicted by various web servers including the Pfam , Prosite  and SMART . External database links such as NCBI Protein, UNIPROT, KEGG etc, were also included.
DetoxiProt was implemented as a relational database using MySQL. Five major tables were used to store data. Web interface was implemented using PHP language. The database is running on a Red Hat Enterprise Linux 3 with Apache as HTTP server (please see additional file 1).
DetoxiProt provides a user-friendly interface allowing easy access to data. The “Home” page contains general information about the database and detoxification proteins. The “detoxification protein classification” page summarizes detailed information about the biological activity of each kind of protein. Other useful tools are also provided:
Basic and advanced searches
The “Search DB” page permits users to perform a quick search and advanced search in the database. Gene symbol name, NCBI gene ID, UNIPROT ID and PDB ID are supported for the quick search. Advanced search tool allow users to combine multiple criteria including protein name, organism, tissue distribution, sub-cellular location, etc. This method helps users to locate specific proteins of interest rapidly and accurately. For example, a medicinal chemist or a pharmacologist may be particularly interested in the specific human cell membrane proteins that are inhibited by some kind of toxin, so the options of protein name, organism, cellular location and inhibitor in each of the pull-down lists can be selected separately and simultaneously, and records limited by specific keywords will be displayed.
Peptide mapping and protein domain search
A tool of peptide mapping is designed for mapping the functional peptides to the entire sequence of detoxification protein. User defined peptide sequences can be exactly searched from protein sequences and the position information provided. This is helpful for understanding the distribution of the active peptides across entire proteins. Another useful tool to infer the functional diversity of detoxification protein is the protein domain search. The concept that conserved sequences among sequences may be a symbol of functionally similar protein domains underlies many classification methods of protein families. Thus we predict the domains within each protein by Pfam, Prosite and SMART web servers. A simple keyword search tool was provided to search database entries by domain or family name of interest to the user.
Structural similarity search
DetoxiProt provides a structural similarity search tool, the 3D-BLAST [33, 34], to search detoxification proteins that have similar 3D-structures. This method allows for a quick and accurate search based on protein structure. It identifies 23 states of structural alphabet, which are used to represent pattern profiles of the protein backbone fragment. Protein structures are represented as structural alphabet sequence databases (SADB). Users may upload their own structure files in PDB format, after which the 3D-Blast program uses BLAST to search for the longest common substructures, known as structural alphabet high-scoring segment pairs (SAHSP), between the structural alphabet sequence translated from query structure and that of other structures in the database. A statistical significance score (E-value) is calculated to indicate the reliability of the prediction of the alignment. Depiction of the results for different search methods is provided (please see additional file 2).
Comparative genomics tools
A customized BLAST tool is embedded for sequence similarity search . To identify potential paralogs or orthologs of each detoxification protein, pre-computed top-matched hits by BLAST search against other proteins in DetoxiProt were listed in the “detailed information” page. This would facilitate phylogenetic relationships studies of the genes across different organisms throughout evolution. Putative ortholog genes may be identified from the best reciprocal hits. For each of the nine model organisms, we generated multiple sequence alignments for each protein family by MUSCLE . Corresponding phylogenetic trees were built by Maximum Likelihood (ML) method embedded in RAxML . The nonparametric bootstrap test was performed for 100 replicates. A Java application of Archaeopteryx tree viewer is embedded for browsing and manipulation of the phylogenetic trees . An example illustrating the result of cytosolic sulfotransferases of Mus musculus is available in the additional file 3. Both the multiple sequence alignment and phylogenetic tree pages can be found in the “tools” page.
Although information concerning specific types of detoxification proteins, for example, peroxidase or peroxisome related proteins, proteins involved in ethanol or drug metabolism, can be found in some databases including the PeroxisomeDB, PeroxiBase, ERGR and SuperTarget , a complete collection of all these categories of proteins is still deficient. Here, the key categories of proteins involved in detoxification are identified and classified. In addition, the targets identified for the detoxification proteins may provide clues to determine the possible effective enzymes in specific drug metabolism pathway. In total, 20 different protein families were identified to be involved in the functionality of detoxification after a literature review. For complete analysis of the database, we inspected the phylogenetic distribution of protein families across model organisms (please see additional file 4). All of the three major detoxification protein classes were found in the nine model organisms. For the protein families, relatively complete datasets were found in mammals. However, the aldehyde oxidases and xanthine oxidoreductases are missing in chimpanzees, the catalases are missing in chickens and seven protein families are missing in amphibia. In contrast, a relatively small number of proteins were found in invertebrates including the Drosophila melanogaster and Caenorhabditis elegans. The amount of detoxification genes took up about 1.5% of genomic genes in most model organisms. At the gene-family level, the cytochrome P450 family and the glutathione S-transferase family are the most abundant groups, with 68 and 48 members identified in humans, respectively. A relatively small number of proteins were found in non-model organisms, probably due to the incomplete annotation of the genes in these species.
In summary, DetoxiProt is a powerful and reliable database that provides comprehensive information about detoxification proteins. Various browsing manners and powerful search engines were implemented providing users with useful ways to explore information in the database. We expect that DetoxiProt will serve as a useful tool for researchers in the detoxification protein field. It could also lead to better understanding of the function and evolution of detoxification proteins. The database will be continually updated, and as the increasing numbers of novel sequences become available, newly identified detoxification proteins will be added to the database. We also hope to adopt text-mining tools to help us to pre-screen protein-toxin relationships. These methods will continue to enhance the completeness of the database.
Availability and requirements
The DetoxiProt may be accessed through http://lifecenter.sgst.cn/detoxiprot/. All the information is freely available to users. To browse the protein 3D structures and phylogenetic trees, the Java Runtime Environment (JRE) plug-in is required.
This work was supported by the National ‘863’ Program of China [2007AA09Z437]; the National Natural Science Foundation of China [30670367 and 31000583]; the National Basic Research Program of China [Project No. 2009CB118702]; and the China Postdoctoral Science Foundation (20100470746). We would like to thank Larissa Wenren (Harvard University) and Joan Kirtland (University of Virginia) for their comments and suggestions.
This article has been published as part of BMC Genomics Volume 12 Supplement 3, 2011: Tenth International Conference on Bioinformatics – First ISCB Asia Joint Conference 2011 (InCoB/ISCB-Asia 2011): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/12?issue=S3.
- Gonzalez FJ: Cytochrome P450 humanised mice. Hum Genomics. 2004, 1 (4): 300-306.PubMed CentralView ArticlePubMedGoogle Scholar
- Kovacic P, Somanathan R: Mechanism of teratogenesis: electron transfer, reactive oxygen species, and antioxidants. Birth Defects Res C Embryo Today. 2006, 78 (4): 308-325. 10.1002/bdrc.20081.View ArticlePubMedGoogle Scholar
- Hines RN, McCarver DG: The ontogeny of human drug-metabolizing enzymes: phase I oxidative enzymes. J Pharmacol Exp Ther. 2002, 300 (2): 355-360. 10.1124/jpet.300.2.355.View ArticlePubMedGoogle Scholar
- Limòn-Pacheco J, Gonsebatt ME: The role of antioxidants and antioxidant-related enzymes in protective responses to environmentally induced oxidative stress. Mutat Res. 2009, 674 (1-2): 137-147.View ArticlePubMedGoogle Scholar
- McCarver DG, Hines RN: The ontogeny of human drug-metabolizing enzymes: phase II conjugation enzymes and regulatory mechanisms. J Pharmacol Exp Ther. 2002, 300 (2): 361-366. 10.1124/jpet.300.2.361.View ArticlePubMedGoogle Scholar
- Ingelman-Sundberg M: Human drug metabolising cytochrome P450 enzymes: properties and polymorphisms. Naunyn Schmiedebergs Arch Pharmacol. 2004, 369 (1): 89-104. 10.1007/s00210-003-0819-z.View ArticlePubMedGoogle Scholar
- Elbaz A, Dufouil C, Alpérovitch A: Interaction between genes and environment in neurodegenerative diseases. C R Biol. 2007, 330 (4): 318-328. 10.1016/j.crvi.2007.02.018.View ArticlePubMedGoogle Scholar
- Kohjima M, Enjoji M, Higuchi N, Kato M, Kotoh K, Yoshimoto T, Fujino T, Yada M, Yada R, Harada N, et al: Re-evaluation of fatty acid metabolism-related gene expression in nonalcoholic fatty liver disease. Int J Mol Med. 2007, 20 (3): 351-358.PubMedGoogle Scholar
- Kumarakulasingham M, Rooney PH, Dundas SR, Telfer C, Melvin WT, Curran S, Murray GI: Cytochrome p450 profile of colorectal cancer: identification of markers of prognosis. Clin Cancer Res. 2005, 11 (10): 3758-3765. 10.1158/1078-0432.CCR-04-1848.View ArticlePubMedGoogle Scholar
- Abel EL, Opp SM, Verlinde CL, Bammler TK, Eaton DL: Characterization of atrazine biotransformation by human and murine glutathione S-transferases. Toxicol Sci. 2004, 80 (2): 230-238. 10.1093/toxsci/kfh152.View ArticlePubMedGoogle Scholar
- Lin SY, Yang JH, Hsia TC, Lee JH, Chiu TH, Wei YH, Chung JG: Effect of inhibition of aloe-emodin on N-acetyltransferase activity and gene expression in human malignant melanoma cells (A375.S2). Melanoma Res. 2005, 15 (6): 489-494. 10.1097/00008390-200512000-00002.View ArticlePubMedGoogle Scholar
- Mackenzie PI, Gregory PA, Gardner-Stephen DA, Lewinsky RH, Jorgensen BR, Nishiyama T, Xie W, Radominska-Pandya A: Regulation of UDP glucuronosyltransferase genes. Curr Drug Metab. 2003, 4 (3): 249-257. 10.2174/1389200033489442.View ArticlePubMedGoogle Scholar
- Di Pietro G, Magno LA, Rios-Santos F: Glutathione S-transferases: an overview in cancer research. Expert Opin Drug Metab Toxicol. 2010, 6 (2): 153-170. 10.1517/17425250903427980.View ArticlePubMedGoogle Scholar
- Wu C, Wang L, Liu C, Gao F, Su M, Wu X, Hong F: Mechanism of Cd2+ on DNA cleavage and Ca2+ on DNA repair in liver of silver crucian carp. Fish Physiol Biochem. 2008, 34 (1): 43-51. 10.1007/s10695-007-9144-7.View ArticlePubMedGoogle Scholar
- Zhao H, Xu X, Na J, Hao L, Huang L, Li G, Xu Q: Protective effects of salicylic acid and vitamin C on sulfur dioxide-induced lipid peroxidation in mice. Inhal Toxicol. 2008, 20 (9): 865-871. 10.1080/08958370701861512.View ArticlePubMedGoogle Scholar
- Seet RC, Lee CY, Lim EC, Tan JJ, Quek AM, Chong WL, Looi WF, Huang SH, Wang H, Chan YH, et al: Oxidative damage in Parkinson disease: Measurement using accurate biomarkers. Free Radic Biol Med. 2010, 48 (4): 560-566. 10.1016/j.freeradbiomed.2009.11.026.View ArticlePubMedGoogle Scholar
- Power JH, Blumbergs PC: Cellular glutathione peroxidase in human brain: cellular distribution, and its potential role in the degradation of Lewy bodies in Parkinson's disease and dementia with Lewy bodies. Acta Neuropathol. 2009, 117 (1): 63-73. 10.1007/s00401-008-0438-3.View ArticlePubMedGoogle Scholar
- Danielson PB: The cytochrome P450 superfamily: biochemistry, evolution and drug metabolism in humans. Curr Drug Metab. 2002, 3 (6): 561-597. 10.2174/1389200023337054.View ArticlePubMedGoogle Scholar
- Sheehan D, Meade G, Foley VM, Dowd CA: Structure, function and evolution of glutathione transferases: implications for classification of non-mammalian members of an ancient enzyme superfamily. Biochem J. 2001, 360 (Pt 1): 1-16.PubMed CentralView ArticlePubMedGoogle Scholar
- Zamocky M, Furtmuller PG, Obinger C: Evolution of catalases from bacteria to humans. Antioxid Redox Signal. 2008, 10 (9): 1527-1548. 10.1089/ars.2008.2046.PubMed CentralView ArticlePubMedGoogle Scholar
- Schluter A, Fourcade S, Domenech-Estevez E, Gabaldon T, Huerta-Cepas J, Berthommier G, Ripp R, Wanders RJ, Poch O, Pujol A: PeroxisomeDB: a database for the peroxisomal proteome, functional genomics and disease. Nucleic Acids Res. 2007, 35 (Database issue): D815-822.PubMed CentralView ArticlePubMedGoogle Scholar
- Koua D, Cerutti L, Falquet L, Sigrist CJ, Theiler G, Hulo N, Dunand C: PeroxiBase: a database with new tools for peroxidase family classification. Nucleic Acids Res. 2009, 37 (Database issue): D261-266.PubMed CentralView ArticlePubMedGoogle Scholar
- Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, et al: InterPro: the integrative protein signature database. Nucleic Acids Res. 2009, 37 (Database issue): D211-215.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, et al: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36 (Database issue): D480-484.PubMed CentralPubMedGoogle Scholar
- The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res. 2009, 37 (Database issue): D169-174.Google Scholar
- Guo AY, Webb BT, Miles MF, Zimmerman MP, Kendler KS, Zhao Z: ERGR: An ethanol-related gene resource. Nucleic Acids Res. 2009, 37 (Database issue): D840-845.PubMed CentralView ArticlePubMedGoogle Scholar
- Hyndman D, Bauman DR, Heredia VV, Penning TM: The aldo-keto reductase superfamily homepage. Chem Biol Interact. 2003, 143-144: 621-631.View ArticlePubMedGoogle Scholar
- Sayers EW, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Edgar R, Federhen S, et al: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2009, 37 (Database issue): D5-15.PubMed CentralView ArticlePubMedGoogle Scholar
- Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res. 2000, 28 (1): 235-242. 10.1093/nar/28.1.235.PubMed CentralView ArticlePubMedGoogle Scholar
- Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K, et al: The Pfam protein families database. Nucleic Acids Res. 2010, 38 (Database issue): D211-222.PubMed CentralView ArticlePubMedGoogle Scholar
- Hulo N, Bairoch A, Bulliard V, Cerutti L, Cuche BA, de Castro E, Lachaize C, Langendijk-Genevaux PS, Sigrist CJ: The 20 years of PROSITE. Nucleic Acids Res. 2008, 36 (Database issue): D245-249.PubMed CentralPubMedGoogle Scholar
- Letunic I, Doerks T, Bork P: SMART 6: recent updates and new developments. Nucleic Acids Res. 2009, 37 (Database issue): D229-232.PubMed CentralView ArticlePubMedGoogle Scholar
- Tung CH, Huang JW, Yang JM: Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database. Genome Biol. 2007, 8 (3): R31-10.1186/gb-2007-8-3-r31.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang JM, Tung CH: Protein structure database search and evolutionary classification. Nucleic Acids Res. 2006, 34 (13): 3646-3659. 10.1093/nar/gkl395.PubMed CentralView ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25 (17): 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralView ArticlePubMedGoogle Scholar
- Edgar RC: MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004, 5: 113-10.1186/1471-2105-5-113.PubMed CentralView ArticlePubMedGoogle Scholar
- Stamatakis A: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006, 22 (21): 2688-2690. 10.1093/bioinformatics/btl446.View ArticlePubMedGoogle Scholar
- Zmasek CM, Eddy SR: ATV: display and manipulation of annotated phylogenetic trees. Bioinformatics. 2001, 17 (4): 383-384. 10.1093/bioinformatics/17.4.383.View ArticlePubMedGoogle Scholar
- Gunther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, et al: SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2008, 36 (Database issue): D919-922.PubMed CentralPubMedGoogle Scholar
- Zheng Q, Wang XJ: GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis. Nucleic Acids Res. 2008, 36 (Web Server issue): W358-363.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou SF, Liu JP, Chowbay B: Polymorphism of human cytochrome P450 enzymes and its clinical impact. Drug Metab Rev. 2009, 41 (2): 89-295. 10.1080/03602530902843483.View ArticlePubMedGoogle Scholar
- Yao L, Qiu LX, Yu L, Yang Z, Yu XJ, Zhong Y, Yu L: The association between TA-repeat polymorphism in the promoter region of UGT1A1 and breast cancer risk: a meta-analysis. Breast Cancer Res Treat. 2010, 122 (3): 879-882. 10.1007/s10549-010-0742-1.View ArticlePubMedGoogle Scholar
- Lin YC, Chang PF, Hu FC, Chang MH, Ni YH: Variants in the UGT1A1 gene and the risk of pediatric nonalcoholic fatty liver disease. Pediatrics. 2009, 124 (6): e1221-1227. 10.1542/peds.2008-3087.View ArticlePubMedGoogle Scholar
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