Mining kidney toxicogenomic data by using gene co-expression modules

Assessing Kidney Injury Induced by Mercuric Chloride in Guinea Pigs with In Vivo and In Vitro Experiments

Acute kidney injury, which is associated with high levels of morbidity and mortality, affects a significant number of individuals, and can be triggered by multiple factors, such as medications, exposure to toxic chemicals or other substances, disease, and trauma. Because the kidney is a critical organ, understanding and identifying early cellular or gene-level changes can provide a foundation for designing medical interventions. In our earlier work, we identified gene modules anchored to histopathology phenotypes associated with toxicant-induced liver and kidney injuries. Here, using in vivo and in vitro experiments, we assessed and validated these kidney injury-associated modules by analyzing gene expression data from the kidneys of male Hartley guinea pigs exposed to mercuric chloride. Using plasma creatinine levels and cell-viability assays as measures of the extent of renal dysfunction under in vivo and in vitro conditions, we performed an initial range-finding study to identify the appropriate doses and exposure times associated with mild and severe kidney injuries. We then monitored changes in kidney gene expression at the selected doses and time points post-toxicant exposure to characterize the mechanisms of kidney injury. Our injury module-based analysis revealed a dose-dependent activation of several phenotypic cellular processes associated with dilatation, necrosis, and fibrogenesis that were common across the experimental platforms and indicative of processes that initiate kidney damage. Furthermore, a comparison of activated injury modules between guinea pigs and rats indicated a strong correlation between the modules, highlighting their potential for cross-species translational studies.

PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool

Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

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Development of PharmOmics, a platform for drug repositioning and toxicity prediction

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Contains >18000 species/tissue-specific gene signatures for 941 drugs and chemicals

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Benchmarked and validated network-based drug repositioning and toxicity prediction

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PharmOmics is freely accessible via an online web server to facilitate user access

Genomics and metabolomics of early-stage thioacetamide-induced liver injury: An interspecies study between guinea pig and rat

To study the complex processes involved in liver injuries, researchers rely on animal investigations, using chemically or surgically induced liver injuries, to extrapolate findings and infer human health risks. However, this presents obvious challenges in performing a detailed comparison and validation between the highly controlled animal models and development of liver injuries in humans. Furthermore, it is not clear whether there are species-dependent and -independent molecular initiating events or processes that cause liver injury before they eventually lead to end-stage liver disease. Here, we present a side-by-side study of rats and guinea pigs using thioacetamide to examine the similarities between early molecular initiating events during an acute-phase liver injury. We exposed Sprague Dawley rats and Hartley guinea pigs to a single dose of 25 or 100 mg/kg thioacetamide and collected blood plasma for metabolomic analysis and liver tissue for RNA-sequencing. The subsequent toxicogenomic analysis identified consistent liver injury trends in both genomic and metabolomic data within 24 and 33 h after thioacetamide exposure in rats and guinea pigs, respectively. In particular, we found species similarities in the key injury phenotypes of inflammation and fibrogenesis in our gene module analysis for liver injury phenotypes. We identified expression of several common genes (e.g., SPP1, TNSF18, SERPINE1, CLDN4, TIMP1, CD44, and LGALS3), activation of injury-specific KEGG pathways, and alteration of plasma metabolites involved in amino acid and bile acid metabolism as some of the key molecular processes that changed early upon thioacetamide exposure and could play a major role in the initiation of acute liver injury.

Urinary Exosomes Identify Inflammatory Pathways in Vancomycin Associated Acute Kidney Injury

Background: Vancomycin is commonly used as a first line therapy for gram positive organisms such as methicillin resistant Staphylococcusaureus. Vancomycin-induced acute kidney injury (V-AKI) has been reported in up to 43% of patients, especially in those with higher targeted trough concentrations. The precise mechanism of injury in humans remains elusive, with recent evidence directed towards proximal tubule cell apoptosis. In this study, we investigated the protein contents of urinary exosomes in patients with V-AKI to further elucidate biomarkers of mechanisms of injury and potential responses. Methods: Urine samples from patients with V-AKI who were enrolled in the DIRECT study and matched healthy controls from the UAB-UCSD O’Brien Center Biorepository were included in the analysis. Exosomes were extracted using solvent exclusion principle and polyethylene glycol induced precipitation. Protein identity and quantification was determined by label-free liquid chromatography mass spectrometry (LC/MS). The mean peak serum creatinine was 3.7 ± 1.4 mg/dL and time to kidney injury was 4.0 ± 3.0 days. At discharge, 90% of patients demonstrated partial recovery; 33% experienced full recovery by day 28. Proteomic analyses on five V-AKI and 7 control samples revealed 2009 proteins in all samples and 251 proteins significantly associated with V-AKI (Pi-score > 1). The top discriminatory proteins were complement C3, complement C4, galectin-3-binding protein, fibrinogen, alpha-2 macroglobulin, immunoglobulin heavy constant mu and serotransferrin. Conclusion: Urinary exosomes reveal up-regulation of inflammatory proteins after nephrotoxic injury in V-AKI. Further studies are necessary in a large patient sample to confirm these findings for elucidation of pathophysiologic mechanisms and validation of potential injury biomarkers.

TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries

Gene-set analysis is commonly used to identify trends in gene expression when cells, tissues, organs, or organisms are subjected to conditions that differ from those within the normal physiological range. However, tools for gene-set analysis to assess liver and kidney injury responses are less common. Furthermore, most websites for gene-set analysis lack the option for users to customize their gene-set database. Here, we present the ToxPanel website, which allows users to perform gene-set analysis to assess liver and kidney injuries using activation scores based on gene-expression fold-change values. The results are graphically presented to assess constituent injury phenotypes (histopathology), with interactive result tables that identify the main contributing genes to a given signal. In addition, ToxPanel offers the flexibility to analyze any set of custom genes based on gene fold-change values. ToxPanel is publically available online at https://toxpanel.bhsai.org. ToxPanel allows users to access our previously developed liver and kidney injury gene sets, which we have shown in previous work to yield robust results that correlate with the degree of injury. Users can also test and validate their customized gene sets using the ToxPanel website.

A toxicogenomic approach to assess kidney injury induced by mercuric chloride in rats

Kidney injury caused by disease, trauma, environmental exposures, or drugs may result in decreased renal function, chronic kidney disease, or acute kidney failure. Diagnosis of kidney injury using serum creatinine levels, a common clinical test, only identifies renal dysfunction after the kidneys have undergone severe damage. Other indicators sensitive to kidney injury, such as the level of urine kidney injury molecule-1 (KIM-1), lack the ability to differentiate between injury phenotypes. To address early detection as well as detailed categorization of kidney-injury phenotypes in preclinical animal or cellular studies, we previously identified eight sets (modules) of co-expressed genes uniquely associated with kidney histopathology. Here, we used mercuric chloride (HgCl₂)-a model nephrotoxicant-to chemically induce kidney injuries as monitored by KIM-1 levels in Sprague Dawley rats at two doses (0.25 or 0.50 mg/kg) and two exposure lengths (10 or 34 h). We collected whole transcriptome RNA-seq data derived from five animals at each dose and time point to perform a toxicogenomics analysis. Consistent with documented injury phenotypes for HgCl₂ toxicity, our kidney-injury-module approach identified the onset of necrosis and dilation as early as 10 h after a dose of 0.50 mg/kg that produced only mild injury as judged by urinary KIM-1 excretion. The results of these animal studies highlight the potential of the kidney-injury-module approach to provide a sensitive and histopathology-specific readout of renal toxicity.

EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species

Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow (Pimephales promelas) to (1) re-cast predefined molecular pathways into a toxicological framework and (2) provide a data-driven, unsupervised grouping of genes impacted by exposure to environmental contaminants. The functional EcoToxModules were identified by re-organizing KEGG pathways into biological processes that are more relevant to ecotoxicology based on the input from expert scientists and regulators. The statistical EcoToxModules were identified using co-expression analysis of publicly available microarray data (n = 303 profiles) measured in livers of fathead minnows after exposure to 38 different conditions. Potential applications of the EcoToxModules were demonstrated with two case studies that represent exposure to a pure chemical and to environmental wastewater samples. In comparisons to differential expression and gene set analysis, we found that EcoToxModule responses were consistent with these traditional results. Additionally, they were easier to visualize and quantitatively compare across different conditions, which facilitated drawing conclusions about the relative toxicity of the exposures within each case study.

Assessing Chemical-Induced Liver Injury In Vivo From In Vitro Gene Expression Data in the Rat: The Case of Thioacetamide Toxicity

Consumers are exposed to thousands of chemicals with potentially adverse health effects. However, these chemicals will never be tested for toxicity because of the immense resources needed for animal-based (in vivo) toxicological studies. Today, there are no viable in vitro alternatives to these types of animal studies. To develop an in vitro approach, we investigated whether we could predict in vivo organ injuries in rats with the use of RNA-seq data acquired from tissues early in the development of toxicant-induced injury, by comparing gene expression data from RNA isolated from these rat tissues with those obtained from in vitro exposure of primary liver and kidney cells. We collected RNA-seq data from the liver and kidney tissues of Sprague-Dawley rats 8 or 24 h after exposing them to vehicle (control), low (25 mg/kg), or high (100 mg/kg) doses of thioacetamide, a known liver toxicant that promotes fibrosis; we used these doses and exposure times to cause only mild toxicant-induced injury. For the in vitro study, we treated two cell types from Sprague-Dawley rats, primary hepatocytes (vehicle; low, 0.025 mM; or high, 0.125 mM dose), and renal tube epithelial cells (vehicle; low, 0.125 mM; or high, 0.500 mM) dose) with the thioacetamide metabolite, thioacetamide-S-oxide, selecting in vitro doses and exposure times to recreate the early-stage toxicant-induced injury model that we achieved in vivo. RNA-seq data were collected 9 or 24 h after application of vehicle or thioacetamide-S-oxide. We found that our modular approach for the analysis of gene expression data derived from in vivo RNA-seq strongly correlated (R² > 0.6) with the in vitro results at two different dose levels of thioacetamide/thioacetamide-S-oxide after 24 h of exposure. The top-ranked liver injury modules in vitro correctly identified the ensuing development of liver fibrosis.

Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways

Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.

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

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.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

Evaluation of the potential nephrotoxicity and mechanism in rats after long-term exposure to the traditional Tibetan medicine tsothel

CONTEXT

Tsothel, a traditional Tibetan medicine, is regarded as 'the king of essences'. Nevertheless, tsothel has aroused serious concern regarding its biosafety because its main component is HgS. Unfortunately, toxicological studies on tsothel are scarce.

OBJECTIVE

As inorganic mercury has high affinity for the kidney, the present investigation was designed to determine the potential nephrotoxicity and mechanism of tsothel.

MATERIALS AND METHODS

Sprague-Dawley rats were orally administered different doses of tsothel (0, 66.70, 33.35 and 16.68 mg/kg) daily for 180 days, followed by the withdrawal of tsothel for 120 days. Then, the related nephrotoxicity was examined by the ICP-MS, ELISA, colorimetric, RT-PCR, HE staining, immunohistochemical staining and flow cytometry methods.

RESULTS

Although tsothel administration led to a large accumulation of Hg (794.25 ± 464.30 ng/g in the 66.70 mg/kg group, 775.75 ± 307.89 ng/g in the 33.35 mg/kg group and 532.60 ± 356.77 ng/g in the 16.68 mg/kg group) in the kidney after 120 days of tsothel withdrawal, the blood CREA and BUN, urinary Kim-1, NAG, RBP and β2-MG, renal SOD, MDA, pathology, proliferation, apoptosis and cell cycle had no significant changes compared with the control group. Additionally, the high GSH content (318.87 ± 44.19 nmol/mL in the 33.35 mg/kg group) and the relative expression levels of Kim-1 (1.08 ± 0.11 in the 33.35 mg/kg group), MT-1 (1.46 ± 0.10 in the 66.70 mg/kg group, 1.61 ± 0.19 in the 33.35 mg/kg group and 1.57 ± 0.14 in the 16.68 mg/kg group) and GST-Pi (1.76 ± 0.89 in the 33.35 mg/kg group) mRNA recovered to normal after tsothel withdrawal. Interestingly, the change trend of GST-Pi gene expression was consistent with the change trend of GSH activity.

CONCLUSIONS

Overall, our study shows that tsothel administration did not induce overt nephrotoxicity but did have reversible stress-related effects. These results suggest that tsothel affects stress response mechanisms with the involvement of detoxifying enzyme systems. The formulation method and chemotype could play a role in the reduced toxicity potential of tsothel compared to common mercurials.

Identification of the Toxicity Pathways Associated With Thioacetamide-Induced Injuries in Rat Liver and Kidney

Ingestion or exposure to chemicals poses a serious health risk. Early detection of cellular changes induced by such events is vital to identify appropriate countermeasures to prevent organ damage. We hypothesize that chemically induced organ injuries are uniquely associated with a set (module) of genes exhibiting significant changes in expression. We have previously identified gene modules specifically associated with organ injuries by analyzing gene expression levels in liver and kidney tissue from rats exposed to diverse chemical insults. Here, we assess and validate our injury-associated gene modules by analyzing gene expression data in liver, kidney, and heart tissues obtained from Sprague-Dawley rats exposed to thioacetamide, a known liver toxicant that promotes fibrosis. The rats were injected intraperitoneally with a low (25 mg/kg) or high (100 mg/kg) dose of thioacetamide for 8 or 24 h, and definite organ injury was diagnosed by histopathology. Injury-associated gene modules indicated organ injury specificity, with the liver being most affected by thioacetamide. The most activated liver gene modules were those associated with inflammatory cell infiltration and fibrosis. Previous studies on thioacetamide toxicity and our histological analyses supported these results, signifying the potential of gene expression data to identify organ injuries.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.

The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury

Drug-induced liver injury (DILI) is one of the major causes of drug attrition and failure. Currently, there is increasing evidence that direct inhibition of the human bile salt export pump (BSEP/ABCB11) by drugs and/or metabolites is one of the most important mechanisms of cholestatic DILI. In the present study, we employ two in silico methods, random forest (RF) and the pharmacophore method, to recognize potential BSEP inhibitors that could cause cholestatic DILI, with the aim of mitigating the risk of cholestatic DILI to some extent. The RF model achieved the best prediction performance, producing AUC (area under receiver operating characteristic curve) values of 0.901, 0.929 and 0.996 for leave-one-out cross-validation, the test set and the external test set, respectively, indicating that the built RF model has a satisfactory identification ability. As a complement to the RF model, the pharmacophore model was also built and was proved to be reliable with good predictive performance based on the internal and external validation results. Further analysis indicates that hydrophobicity, molecular size and polarity are important factors that influence the inhibitory activity of BSEP. Furthermore, the two models are applied to screen FDA-approved small molecule drugs, among which the drugs with the potential risk of cholestatic DILI are reported. In conclusion, the RF and pharmacophore models that we present can be considered as integrated screening tools to indicate the potential risk of cholestatic DILI by inhibition of BSEP.