The BioInvestigationIndex (BII)

Title:

Gene expression profiling of rat hepatocytes after a 24h treatment with various drugs and other chemicals. [original title: Identification of biomarkers that distinguish chemical contaminants using a gradient feature selection method]

Organism(s):

Rattus norvegicus

Description:

531 arrays; 108 different treatments (including aspirin, ibuprofen, indomethacin, acetaminophen) plus replicates and controls. Many of the chemicals are derivatives of the explosive TNT. [original description: There are many toxic chemicals to contaminate the world and cause harm to human and other organisms. How to quickly discriminate these compounds and characterize their potential molecular mechanism and toxicity is essential. High through put transcriptomics profiles such as microarray have been proven useful to identify biomarkers for different classification and toxicity prediction purposes. Here we aim to investigate how to use microarray to predict chemical contaminants and their possible mechanisms. In this study, we divided 105 compounds plus vehicle control into 14 compound classes. On the basis of gene expression profiles of in vitro primary cultured hepatocytes, we comprehensively compared various normalization, feature selection and classification algorithms for the classification of these 14 class compounds. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine methods LibSVM and SMO had better classification performance. When feature sizes were smaller, LibSVM outperformed other classification methods. Simple logistic algorithm also performed well. At the training stage, usually the feature selection method SVM-RFE performed the best, and PCA was the poorest feature selection algorithm. But overall, SVM-RFE had the highest overfitting rate when an independent dataset used for a prediction in this case. Therefore, we developed a new feature selection algorithm called gradient method which had a pretty high training classification as well as prediction accuracy with the lowest over-fitting rate. Through the analysis of biomarkers that distinguished 14 class compounds, we found a group of genes that mainly involved in cell cycle were significantly downregulated by the metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. For in vitro experiment, primary cultured rat hepatocytes were treated one of 105 compounds with relative controls. At least three biological replicates were used for each unique condition. In total 531 arrays were used.]

Design(s):

transcription profiling by array

Experimental factor(s):

treatment 108 recorded
sodium metaarsenite control condition 17alpha-ethynylestradiol doxorubicin nickel dichloride 1,3,5-trinitrobenzene chlorpromazine Methyl Caproate (PubChem CID 7824) phenobarbital trichloroethene chromate(2-) paraquat 2,3,7,8-TBDD (CHEMBL341968) benzoapyrene dexamethasone 4-amino-2,6-DNT (PubChem CID 29574) tergitol NP-9 spironolactone glucagon Beta-Naphthoflavone (CHEMBL26260) TNX (PubChem CID 69442) unknown compound 2-amino-4,4-DNT Ethoxyquin (CHEMBL172064) troglitazone interleukin-3 pentachlorophenol phenytoin methotrexate tetrachloromethane copper(II) chloride atorvastatin 2,6-dinitrotoluene camptothecin 2-amino-4,6-dinitrotoluene bezafibrate lovastatin potassium chromate MNX (PubChem CID 5326637) 4-Hydroxynonenal (HNE-DMA)* (PubChem CID 14159264) DDT paracetamol pregnenolone 16alpha-carbonitrile 2,3,7,8-tetrachlorodibenzodioxine chlorphenamine 3-methylcholanthrene cortisone disodium selenite Diisopropyl methylphosphonate (DIMP) (CAS # 1445-75-6) cadmium dichloride potassium dichromate amiodarone 1,3,5-trinitro-1,3,5-triazinane SR12813 (PubChem CID 446313) rifampicin N,N-diethyl-m-toluamide 2,4,6-trinitrotoluene metyrapone benzylpenicillin aldosterone Toxaphene (PubChem CID 5284469) methoxychlor lead nitrate clotrimazole fluoranthene fenoprofen Oltipraz (CHEMBL178459) cyclophosphamide etoposide ibuprofen acetylsalicylic acid 2,4-dinitrophenol Musk xylene (CHEMBL228513) unknown compound Spectromycin (sulfate) 3,3'-diindolylmethane pirinixic acid perchlorate rosiglitazone perfluorooctanoic acid vincaleukoblastine sulfate ketoconazole bisphenol A indomethacin tamoxifen lipopolysaccharide piperonyl butoxide sodium tungstate dihydrate DEHP (di-(2-ethylhexyl)phthalate) (CHEMBL402794) hyperforin CL-20 (PubChemCID 9889323) tumor necrosis factor alpha anthracene fipronil genistein cytochalasin D 4-Bromophenyl phenyl ether (CHEMBL1873236) 2,4-diamino-6-nitrotoluene naphthalene paclitaxel permethrin chlorpyrifos 2,4-dinitrotoluene cyclosporin A zinc sulfate heptahydrate octogen 3,3',4,4',5-pentachlorobiphenyl atrazine clofibrate perfluorodecanoic acid
dose 27 recorded
0.1 parts per million 1 parts per million 0.3 parts per million 10 parts per million ~46 parts per million 100 parts per million 0.03 parts per million 0.1 parts per billion 0.3 micromolar 3 micromolar 3 parts per million 10 parts per billion 30 micromolar 30 parts per million 1 millimolar 10 micromolar 100 unit per milliliter 1 micromolar 100 millimolar ~14 parts per million 300 parts per million 100 parts per billion 100 micromolar 300 micromolar 0.01 parts per million 1000 parts per million 3 millimolar

Publication(s):

Deng Y, Johnson DR, Guan X, Ang CY, Ai J, Perkins EJ
In vitro gene regulatory networks predict in vivo function of liver. PubMed:21073692

Sample attribute(s):

sex 1 recorded
male
cell line type 1 recorded
primary culture
Label 1 recorded
CY3
Strain 1 recorded
Sprague Dawley
cell type 1 recorded
hepatocyte
organism 1 recorded
Rattus norvegicus