Literature DB >> 15135310

Inverse gene expression patterns for macrophage activating hepatotoxicants and peroxisome proliferators in rat liver.

Michael McMillian1, Alex Y Nie, J Brandon Parker, Angelique Leone, Michael Kemmerer, Stewart Bryant, Judy Herlich, Lynn Yieh, Anton Bittner, Xuejun Liu, Jackson Wan, Mark D Johnson.   

Abstract

Macrophage activation contributes to adverse effects produced by a number of hepatotoxic compounds. Transcriptional profiles elicited by two macrophage activators, LPS and zymosan A, were compared to those produced by 100 paradigm compounds (mostly hepatotoxicants) using cDNA microarrays. Several hepatotoxicants previously reported to activate liver macrophages produced transcriptional responses similar to LPS and zymosan, and these were used to construct a gene signature profile for macrophage activators in the liver. Measurement of cytokine mRNAs in the same liver samples by RT-PCR independently confirmed that these compounds are associated with macrophage activation. In addition to expected effects on acute phase proteins and metabolic pathways that are regulated by LPS and inflammation, a strong induction was observed for many endoplasmic reticulum-associated stress/chaperone proteins. Additionally, many genes in our macrophage activator signature profile were well-characterized PPARalpha-induced genes which were repressed by macrophage activators. A shared gene signature profile for peroxisome proliferators was determined using a training set of clofibrate, WY 14643, diethylhexylphthalate, diisononylphthalate, perfluorodecanoic acid, perfluoroheptanoic acid, and perfluorooctanoic acid. The signature profile included macrophage activator-induced genes that were repressed by peroxisome proliferators. NSAIDs comprised an interesting pharmacological class in that some compounds, notably diflunisal, co-clustered with peroxisome proliferators whereas several others co-clustered with macrophage activators, possibly due to endotoxin exposure secondary to their adverse effects on the gastrointestinal system. While much of these data confirmed findings from the literature, the transcriptional patterns detected using this toxicogenomics approach showed relationships between genes and biological pathways requiring complex analysis to be discerned.

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Year:  2004        PMID: 15135310     DOI: 10.1016/j.bcp.2004.01.029

Source DB:  PubMed          Journal:  Biochem Pharmacol        ISSN: 0006-2952            Impact factor:   5.858


  9 in total

1.  Leukotrienes modulate cytokine release from dendritic cells.

Authors:  Szczepan Jozefowski; Rafał Biedroń; Malgorzata Bobek; Janusz Marcinkiewicz
Journal:  Immunology       Date:  2005-12       Impact factor: 7.397

Review 2.  Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity.

Authors:  Alison Hege Harrill; Ivan Rusyn
Journal:  Expert Opin Drug Metab Toxicol       Date:  2008-11       Impact factor: 4.481

Review 3.  Filling and mining the reactive metabolite target protein database.

Authors:  Robert P Hanzlik; Jianwen Fang; Yakov M Koen
Journal:  Chem Biol Interact       Date:  2008-09-06       Impact factor: 5.192

Review 4.  Organotypic liver culture models: meeting current challenges in toxicity testing.

Authors:  Edward L LeCluyse; Rafal P Witek; Melvin E Andersen; Mark J Powers
Journal:  Crit Rev Toxicol       Date:  2012-05-15       Impact factor: 5.635

5.  Toxicogenomic biomarkers for liver toxicity.

Authors:  Naoki Kiyosawa; Yosuke Ando; Sunao Manabe; Takashi Yamoto
Journal:  J Toxicol Pathol       Date:  2009-04-06       Impact factor: 1.628

6.  CEBS: a comprehensive annotated database of toxicological data.

Authors:  Isabel A Lea; Hui Gong; Anand Paleja; Asif Rashid; Jennifer Fostel
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

7.  ABC gene-ranking for prediction of drug-induced cholestasis in rats.

Authors:  Yauheniya Cherkas; Michael K McMillian; Dhammika Amaratunga; Nandini Raghavan; Jennifer C Sasaki
Journal:  Toxicol Rep       Date:  2016-01-18

8.  CEBS--Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data.

Authors:  Michael Waters; Stanley Stasiewicz; B Alex Merrick; Kenneth Tomer; Pierre Bushel; Richard Paules; Nancy Stegman; Gerald Nehls; Kenneth J Yost; C Harris Johnson; Scott F Gustafson; Sandhya Xirasagar; Nianqing Xiao; Cheng-Cheng Huang; Paul Boyer; Denny D Chan; Qinyan Pan; Hui Gong; John Taylor; Danielle Choi; Asif Rashid; Ayazaddin Ahmed; Reese Howle; James Selkirk; Raymond Tennant; Jennifer Fostel
Journal:  Nucleic Acids Res       Date:  2007-10-25       Impact factor: 16.971

9.  A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.

Authors:  Richard Judson; Fathi Elloumi; R Woodrow Setzer; Zhen Li; Imran Shah
Journal:  BMC Bioinformatics       Date:  2008-05-19       Impact factor: 3.169

  9 in total

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