Literature DB >> 26396155

Gene Expression Patterns Associated With Histopathology in Toxic Liver Fibrosis.

Danielle L Ippolito1, Mohamed Diwan M AbdulHameed2, Gregory J Tawa3, Christine E Baer4, Matthew G Permenter4, Bonna C McDyre5, William E Dennis1, Molly H Boyle6, Cheryl A Hobbs6, Michael A Streicker6, Bobbi S Snowden7, John A Lewis1, Anders Wallqvist2, Jonathan D Stallings8.   

Abstract

Toxic industrial chemicals induce liver injury, which is difficult to diagnose without invasive procedures. Identifying indicators of end organ injury can complement exposure-based assays and improve predictive power. A multiplexed approach was used to experimentally evaluate a panel of 67 genes predicted to be associated with the fibrosis pathology by computationally mining DrugMatrix, a publicly available repository of gene microarray data. Five-day oral gavage studies in male Sprague Dawley rats dosed with varying concentrations of 3 fibrogenic compounds (allyl alcohol, carbon tetrachloride, and 4,4'-methylenedianiline) and 2 nonfibrogenic compounds (bromobenzene and dexamethasone) were conducted. Fibrosis was definitively diagnosed by histopathology. The 67-plex gene panel accurately diagnosed fibrosis in both microarray and multiplexed-gene expression assays. Necrosis and inflammatory infiltration were comorbid with fibrosis. ANOVA with contrasts identified that 51 of the 67 predicted genes were significantly associated with the fibrosis phenotype, with 24 of these specific to fibrosis alone. The protein product of the gene most strongly correlated with the fibrosis phenotype PCOLCE (Procollagen C-Endopeptidase Enhancer) was dose-dependently elevated in plasma from animals administered fibrogenic chemicals (P < .05). Semiquantitative global mass spectrometry analysis of the plasma identified an additional 5 protein products of the gene panel which increased after fibrogenic toxicant administration: fibronectin, ceruloplasmin, vitronectin, insulin-like growth factor binding protein, and α2-macroglobulin. These results support the data mining approach for identifying gene and/or protein panels for assessing liver injury and may suggest bridging biomarkers for molecular mediators linked to histopathology. Published by Oxford University Press on behalf of the Society of Toxicology 2015. This work is written by US Government employees and is in the public domain in the US.

Entities:  

Keywords:  bioinformatics; biomarkers; fibrosis; histopathology; toxic liver injury; transcriptomics

Mesh:

Substances:

Year:  2015        PMID: 26396155     DOI: 10.1093/toxsci/kfv214

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  16 in total

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Journal:  Biomedicines       Date:  2022-04-28

4.  Polycomb Repressive Complex 2 Proteins EZH1 and EZH2 Regulate Timing of Postnatal Hepatocyte Maturation and Fibrosis by Repressing Genes With Euchromatic Promoters in Mice.

Authors:  Jessica Mae Grindheim; Dario Nicetto; Greg Donahue; Kenneth S Zaret
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5.  Clinical, pathologic, and toxicologic characterization of Salvia reflexa (lance-leaf sage) poisoning in cattle fed contaminated hay.

Authors:  Kip E Panter; Bryan L Stegelmeier; Dale R Gardner; Clinton A Stonecipher; Stephen T Lee; Don Kitchen; Adeline Brackett; Charlie Davis
Journal:  J Vet Diagn Invest       Date:  2021-03-10       Impact factor: 1.279

6.  Mining kidney toxicogenomic data by using gene co-expression modules.

Authors:  Mohamed Diwan M AbdulHameed; Danielle L Ippolito; Jonathan D Stallings; Anders Wallqvist
Journal:  BMC Genomics       Date:  2016-10-10       Impact factor: 3.969

7.  DAPK1 as an independent prognostic marker in liver cancer.

Authors:  Ling Li; Libin Guo; Qingshui Wang; Xiaolong Liu; Yongyi Zeng; Qing Wen; Shudong Zhang; Hang Fai Kwok; Yao Lin; Jingfeng Liu
Journal:  PeerJ       Date:  2017-07-19       Impact factor: 2.984

8.  Procollagen C-Proteinase Enhancer 1 (PCPE-1) as a Plasma Marker of Muscle and Liver Fibrosis in Mice.

Authors:  Eyal Hassoun; Mary Safrin; Hana Ziv; Sarah Pri-Chen; Efrat Kessler
Journal:  PLoS One       Date:  2016-07-26       Impact factor: 3.240

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Journal:  Front Genet       Date:  2018-03-20       Impact factor: 4.599

10.  Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat.

Authors:  Venkat R Pannala; Martha L Wall; Shanea K Estes; Irina Trenary; Tracy P O'Brien; Richard L Printz; Kalyan C Vinnakota; Jaques Reifman; Masakazu Shiota; Jamey D Young; Anders Wallqvist
Journal:  Sci Rep       Date:  2018-08-03       Impact factor: 4.379

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