Quantitative targeted bile acid profiling as new markers for DILI in a model of methapyrilene-induced liver injury in rats

Slopianka, Markus; Herrmann, Anne; Pavkovic, Mira; Ellinger‐Ziegelbauer, Heidrun; Ernst, R.; Mally, Angela; Keck, Matthias; Riefke, Bjoern

doi:10.1016/j.tox.2017.05.009

Cited by 26 publications

(20 citation statements)

References 28 publications

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“…After adding 60 μL Milli-Q ® water to the extracts and shaking briefly (600 rpm, 5 min), the 96-well plate containing the samples was analyzed by LC-MS/MS. All target isobaric bile acids were baseline-separated under UHPLC conditions based on a previously described method [ 60 ]. Briefly, UHPLC systems were used at a flow rate of 0.7–1 mL/min.…”

Section: Methodsmentioning

confidence: 99%

Antibiotic-Induced Changes in Microbiome-Related Metabolites and Bile Acids in Rat Plasma

et al. 2020

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Various environmental factors can alter the gut microbiome’s composition and functionality, and modulate host health. In this study, the effects of oral and parenteral administration of two poorly bioavailable antibiotics (i.e., vancomycin and streptomycin) on male Wistar Crl/Wi(Han) rats for 28 days were compared to distinguish between microbiome-derived or -associated and systemic changes in the plasma metabolome. The resulting changes in the plasma metabolome were compared to the effects of a third reference compound, roxithromycin, which is readily bioavailable. A community analysis revealed that the oral administration of vancomycin and roxithromycin in particular leads to an altered microbial population. Antibiotic-induced changes depending on the administration routes were observed in plasma metabolite levels. Indole-3-acetic acid (IAA) and hippuric acid (HA) were identified as key metabolites of microbiome modulation, with HA being the most sensitive. Even though large variations in the plasma bile acid pool between and within rats were observed, the change in microbiome community was observed to alter the composition of the bile acid pool, especially by an accumulation of taurine-conjugated primary bile acids. In-depth investigation of the relationship between microbiome variability and their functionality, with emphasis on the bile acid pool, will be necessary to better assess the potential adverseness of environmentally induced microbiome changes.

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Section: Methodsmentioning

confidence: 99%

Antibiotic-Induced Changes in Microbiome-Related Metabolites and Bile Acids in Rat Plasma

et al. 2020

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“…To further assess the ability of the models to predict liver-injury phenotypes, we constructed five additional independent testing sets from publically accessible rat liver in vivo data on (1) liver necrosis caused by heat stress [(Stallings et al, 2014); GEO accession number GSE56740], (2) three endpoints for bile duct ligation [(Sutherland et al, 2018); GEO accession number GSE87696], (3) liver fibrosis after exposure to nevirapine, galactosamine, and their combination [(Brown et al, 2016); GEO accession number GSE72076], (4) exposure to five chemicals that had no impact on liver histopathology [(Eun et al, 2015); GEO accession number GSE49631], and (5) biliary hyperplasia and liver necrosis after exposure to methapyrilene [(Slopianka et al, 2017); GEO accession number GSE95470].…”

Section: Methodsmentioning

confidence: 99%

Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

et al. 2019

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Improving the accuracy of toxicity prediction models for liver injuries is a key element in evaluating the safety of drugs and chemicals. Mechanism-based information derived from expression (transcriptomic) data, in combination with machine-learning methods, promises to improve the accuracy and robustness of current toxicity prediction models. Deep neural networks (DNNs) have the advantage of automatically assembling the relevant features from a large number of input features. This makes them especially suitable for modeling transcriptomic data, which typically contain thousands of features. Here, we gaged gene- and pathway-level feature selection schemes using single- and multi-task DNN approaches in predicting chemically induced liver injuries (biliary hyperplasia, fibrosis, and necrosis) from whole-genome DNA microarray data. The single-task DNN models showed high predictive accuracy and endpoint specificity, with Matthews correlation coefficients for the three endpoints on 10-fold cross validation ranging from 0.56 to 0.89, with an average of 0.74 in the best feature sets. The DNN models outperformed Random Forest models in cross validation and showed better performance than Support Vector Machine models when tested in the external validation datasets. In the cross validation studies, the effect of the feature selection scheme was negligible among the studied feature sets. Further evaluation of the models on their ability to predict the injury phenotype per se for non-chemically induced injuries revealed the robust performance of the DNN models across these additional external testing datasets. Thus, the DNN models learned features specific to the injury phenotype contained in the gene expression data.

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“…Besides, they are bioactive molecules signaling through receptors such as farnesoid X receptor, pregnane X receptor, vitamin D receptor, sphingosine-1-phosphate receptor 2, and G protein-coupled bile acid receptor 1 in order to modulate lipid and glucose homeostasis, with relevance to cardio-metabolic disease and cancer [ 1 , 2 ]. Several individual bile acids are considered biomarkers of disease or drug-induced liver injury (DILI) [ 3 , 4 , 5 ] and improved quantification methods are therefore needed.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Highly Sensitive LC-MS/MS Method for the Analysis of Bile Acids in Serum, Plasma, and Liver Tissue Samples

et al. 2020

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Bile acids control lipid homeostasis by regulating uptake from food and excretion. Additionally, bile acids are bioactive molecules acting through receptors and modulating various physiological processes. Impaired bile acid homeostasis is associated with several diseases and drug-induced liver injury. Individual bile acids may serve as disease and drug toxicity biomarkers, with a great demand for improved bile acid quantification methods. We developed, optimized, and validated an LC-MS/MS method for quantification of 36 bile acids in serum, plasma, and liver tissue samples. The simultaneous quantification of important free and taurine- and glycine-conjugated bile acids of human and rodent species has been achieved using a simple workflow. The method was applied to a mouse model of statin-induced myotoxicity to assess a possible role of bile acids. Treatment of mice for three weeks with 5, 10, and 25 mg/kg/d simvastatin, causing adverse skeletal muscle effects, did not alter plasma and liver tissue bile acid profiles, indicating that bile acids are not involved in statin-induced myotoxicity. In conclusion, the established LC-MS/MS method enables uncomplicated sample preparation and quantification of key bile acids in serum, plasma, and liver tissue of human and rodent species to facilitate future studies of disease mechanisms and drug-induced liver injury.

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Quantitative targeted bile acid profiling as new markers for DILI in a model of methapyrilene-induced liver injury in rats

Cited by 26 publications

References 28 publications

Antibiotic-Induced Changes in Microbiome-Related Metabolites and Bile Acids in Rat Plasma

Antibiotic-Induced Changes in Microbiome-Related Metabolites and Bile Acids in Rat Plasma

Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

Development and Validation of a Highly Sensitive LC-MS/MS Method for the Analysis of Bile Acids in Serum, Plasma, and Liver Tissue Samples

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