Browsing by Author "Stevens, James L."

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    Advancing Toxicology-Based Cancer Risk Assessment with Informatics
    (2010-05-03T19:38:33Z) Bercu, Joel P.; Mahoui, Malika; Romero, Pedro R.; Stevens, James L.; Jones, Josette F.; Palakal, Mathew J.
    Since exposure to carcinogens can occur in the environment from various point sources, cancer risk assessment attempts to define and limit potential exposure such that the risk of developing cancer is negligible. While cancer risk assessment is widely used with certain methodologies well accepted in the scientific literature and regulatory guidances, there are still gaps which increase uncertainties when assessing risk including: (1) mixtures of genotoxins, (2) genotoxic metabolites, and (3) nongenotoxic carcinogens. An in silico model was developed to predict the cancer risk of a genotoxin which improved methodology for a single compound and mixtures. Monte Carlo simulations performed with a carcinogenicity potency database to estimate the overall carcinogenic risk of a mixture of genotoxic compounds showed that structural similarity would not likely increase the overall cancer risk. A cancer risk model was developed for genotoxic metabolites using excretion material in both animals and humans to determine the probability not exceeding a 1 in 100,000 excess cancer risk. Two model nongenotoxic compounds (fenofibrate and methapyraline) were tested in short-term microarray studies to develop a framework for cancer risk assessment. It was determined that a threshold for potential key events could be derived using benchmark dose analysis in combination with well developed ontologies (Kegg/GO), which were at or below measured tumorigenic and precursor events. In conclusion, informatics was effective in advancing toxicology-based cancer risk assessment using databases and predictive techniques which fill critical gaps in its methodology.
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    CHOP links endoplasmic reticulum stress to NF-κB activation in the pathogenesis of nonalcoholic steatohepatitis
    (2015) Willy, Jeffrey A.; Young, Sara K.; Stevens, James L.; Masuoka, Howard C.; Wek, Ronald C.; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Free fatty acid induction of inflammation and cell death is an important feature of nonalcoholic steatohepatitis (NASH) and has been associated with disruption of the endoplasmic reticulum and activation of the Unfolded Protein Response (UPR). Following chronic UPR activation, the transcription factor CHOP (GADD153/DDIT3) triggers cell death; however, the mechanisms linking the UPR or CHOP to hepatoceullular injury and inflammation in the pathogenesis of NASH are not well understood. Using HepG2 and primary human hepatocytes, we found that CHOP induces cell death and inflammatory responses following saturated free fatty acid exposure by activating NF-κB through a pathway involving IRAK2 expression, resulting in secretion of cytokines IL-8 and TNFα directly from hepatocytes. TNFα facilitates hepatocyte death upon exposure to saturated free fatty acids and secretion of both IL-8 and TNFα contribute to inflammation. Interestingly, CHOP/NF-κB signaling is not conserved in primary rodent hepatocytes. Our studies suggest that CHOP plays a vital role in the pathophysiology of NASH through induction of secreted factors that trigger inflammation and hepatocellular death via a signaling pathway specific to human hepatocytes.
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    Function of inhibitor of Bruton's tyrosine kinase isoform α (IBTKα) in nonalcoholic steatohepatitis links autophagy and the unfolded protein response
    (American Society for Biochemistry and Molecular Biology, 2017-08-25) Willy, Jeffrey A.; Young, Sara K.; Mosley, Amber L.; Gawrieh, Samer; Stevens, James L.; Masuoka, Howard C.; Wek, Ronald C.; Biochemistry and Molecular Biology, School of Medicine
    Nonalcoholic fatty liver disease (steatosis) is the most prevalent liver disease in the Western world. One of the advanced pathologies is nonalcoholic steatohepatitis (NASH), which is associated with induction of the unfolded protein response (UPR) and disruption of autophagic flux. However, the mechanisms by which these processes contribute to the pathogenesis of human diseases are unclear. Herein, we identify the α isoform of the inhibitor of Bruton's tyrosine kinase (IBTKα) as a member of the UPR, whose expression is preferentially translated during endoplasmic reticulum (ER) stress. We found that IBTKα is located in the ER and associates with proteins LC3b, SEC16A, and SEC31A and plays a previously unrecognized role in phagophore initiation from ER exit sites. Depletion of IBTKα helps prevent accumulation of autophagosome intermediates stemming from exposure to saturated free fatty acids and rescues hepatocytes from death. Of note, induction of IBTKα and the UPR, along with inhibition of autophagic flux, was associated with progression from steatosis to NASH in liver biopsies. These results indicate a function for IBTKα in NASH that links autophagy with activation of the UPR.
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    A Novel Open Access Web Portal for Integrating Mechanistic and Toxicogenomic Study Results
    (Oxford University Press, 2019-08-01) Sutherland, Jeffrey J.; Stevens, James L.; Johnson, Kamin; Elango, Navin; Webster, Yue W.; Mills, Bradley J.; Robertson, Daniel H.; Biochemistry and Molecular Biology, School of Medicine
    Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as gene set enrichment analysis, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the 2 approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for "hands-on" computer programming experience, the selection of 1 or more analysis methods (eg pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogeomics, an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements "best-practice" methods in computational biology. New study results are compared with over 4000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.