Blood toxicogenomics reveals potential biomarkers for management of idiosyncratic drug-induced liver injury

Abstract

Introduction: Accurate diagnosis, assessment, and prognosis of idiosyncratic drug-induced liver injury (IDILI) is problematic, in part due to the shortcomings of traditional blood biomarkers. Studies in rodents and healthy volunteers have supported that RNA transcript changes in whole blood may address some of these shortcomings. Methods: In this study, we conducted RNA-Seq analysis on peripheral blood samples collected from 55 patients with acute IDILI and 17 patients with liver injuries not attributed to IDILI. Results and discussion: Three differentially expressed genes (DEGs; CFD, SQLE, and INKA1) were identified as significantly associated with IDILI vs. other liver injuries. No DEGs were identified comparing IDILI patients to the 5 patients with autoimmune hepatitis, suggesting possible common underlying mechanisms. Two genes (VMO1 and EFNA1) were significantly associated with hepatocellular injury compared to mixed/cholestatic injury. When patients with severe vs. milder IDILI were compared, we identified over 500 DEGs. The top pathways identified from these DEGs had in common down regulation of multiple T-cell specific genes. Further analyses confirmed that these changes could largely be accounted for by a fall in the concentration of circulating T-cells during severe DILI, perhaps due to exhaustion or sequestration of these cells in the liver. Exploration of DEGs specific for the individual causal agents was largely unsuccessful, but isoniazid-induced IDILI demonstrated 25 DEGs compared to other non-isoniazid IDILI cases. Finally, among the 14 IDILI patients that had hepatocellular jaundice (i.e., Hy's Law cases), we identified 39 DEGs between the 4 patients with fatal or liver transplantation outcomes compared to those that recovered. These findings suggest the potential for blood-based transcriptomic biomarkers to aid in the diagnosis and prognostic stratification of IDILI.

Keywords: RNA-seq; differentially expressed genes (DEGs); drug-induced liver injury; hepatotoxicity; immune-regulated pathways; peripheral blood transcriptomics.

FIGURE 1

Samples used for DILIN RNA-Seq Analyses. Whole blood from N = 78 DILIN patients were RNA-Sequenced. Following sequencing and prior to any data analyses, N = 6 samples were removed (N = 1 sample had low read counts, N = 3 samples had unique population and ethnicity combinations, N = 1 processed in separate batch, and N = 1 was an outlier by PCA analysis). A causality analysis was performed using data from the remaining N = 72 patients. Most subsequent analyses were performed using only data from patients with a “high causality” score (≥50% likely that the liver injury was caused by use of indicated drug) removing data from the N = 17 patients with a “low” causality score (<50% chance liver injury was caused by a drug). Expert review of the remaining cases (by author PBW) identified N = 3 patients where severity score could not be determined due confounding medical conditions; therefore these patients were removed from analyses examining gene differences related to IDILI severity.

FIGURE 2

Gene expression profiles across DILIN severity. Heatmap of significant gene expression profiles for N = 52 patients, showing two major global signatures. To increase the power of differential analysis, patients with mild, moderate, and moderate-hospitalized severity scores are classified into a “low severity” group, while patients with severe or fatal scores are combined into a “high severity” group.

FIGURE 3

Total T Cell Changes Related to IDILI Severity CIBERSORTx analysis was utilized to determine total T cell fraction (A) changes related to injury severity in n = 52 high causality IDILI patients. Ordinal rankings of “mild” (mild severity score, n = 10), “moderate” (moderate and moderate-hospitalized severity scores n = 29), and “severe” (severe and fatal severity scores, n = 13) were assigned. A built-in immune cells (LM22) data set was utilized as a training expression data set. Total T cell concentration (B) was estimated in the n = 51 of these patients who had a blood sample for total WBC measurement collected within 4 days of RNA-seq blood sample collection (n = 1 moderate severity IDILI patient removed). The fraction of T cells determined using CIBERSORTx was multiplied by the WBC concentration for each patient to estimate the concentration of total cells. Significance was calculated using a one-way ANOVA test and was p < 0.001 and p = 0.053 for A and B, respectively.