Quantitative Chemical Proteomic Profiling of the in Vivo Targets of Reactive Drug Metabolites

Abstract

Idiosyncratic liver toxicity represents an important problem in drug research and pharmacotherapy. Reactive drug metabolites that modify proteins are thought to be a principal factor in drug-induced liver injury. Here, we describe a quantitative chemical proteomic method to identify the targets of reactive drug metabolites in vivo. Treating mice with clickable analogues of four representative hepatotoxic drugs, we demonstrate extensive covalent binding that is confined primarily to the liver. Each drug exhibited a distinct target profile that, in certain cases, showed strong enrichment for specific metabolic pathways (e.g., lipid/sterol pathways for troglitazone). Site-specific proteomics revealed that acetaminophen reacts with high stoichiometry with several conserved, functional (seleno)cysteine residues throughout the liver proteome. Our findings thus provide an advanced experimental framework to characterize the proteomic reactivity of drug metabolites in vivo, revealing target profiles that may help to explain mechanisms and identify risk factors for drug-induced liver injury.

Figure 1

Structures and initial profiling of the reactivity of chemical proteomic probes for hepatoxic drugs. (A) Structures of the parent hepatotoxic drugs (1–4) and their corresponding alkyne-modified (clickable) probes (5–8). The alkyne functionality was incorporated at sites intended to minimize interference with the known routes of metabolism of the drugs; see Figure S1 for more details. (B) In vitro reactivity profiles for probes 5–8 in mouse liver lysates with or without NADPH. Freshly prepared mouse liver proteome (2 mg/mL) was treated with DMSO or indicated probes (0.1–50 μM, 2 h, room temperature), and protein reactivity events analyzed by CuAAC with an azide-rhodamine tag followed by SDS-PAGE and in-gel fluorescence scanning (fluorescent gels shown in grayscale). See Figure S2A for a lower intensity image of the gel. (C) Schematic for chemical proteomic workflow used to profile the in vivo reactivity of probes 5–8. Following treatment with vehicle or probes (i.p., 2 h), mice were sacrificed and liver proteomes prepared and analyzed for protein reactivity events as describes in part B. (D) Concentration-dependent reactivity of mouse liver soluble (left) and membrane (right) proteomes following treatment of mice with vehicle or probes 5–8 (i.p., 2 h) at the indicated doses. The dosing range for probe 8 was extended into the acute hepatoxic range for acetaminophen (300 mg/kg). (E) Proteomic reactivity of five tissues from mice treated with vehicle or probes 5–8 (i.p., 90 mg/kg, 2 h). Data shown are for soluble proteomes; see Figure S2B for membrane proteome reactivity data. (F) Proteomic reactivity of liver from mice treated for the indicated number of days with vehicle or probes 5–7 (30 mg/kg, i.p., once daily). Data shown are for membrane proteomes; see Figure S2C for soluble proteome reactivity data.

Figure 2

Quantitative MS-based characterization of the in vivo proteome reactivity of hepatotoxic drugs. (A) Schematic of the workflow for quantitative MS-based proteomic experiments to identify mouse liver proteins modified by probes 5–8 in vivo. Mice were treated with the probes 5–8 or the corresponding parent drugs 1–4 using acute (8 or 4, 200 mg/kg, 2 h) or subchronic (5–7 or 1–3, 30 mg/kg, 7 days, q.d) dosing conditions. Following sacrifice of animals, liver proteomes were prepared and probe-modified proteins conjugated by CuAAC to an azide-biotin tag, enriched using streptavidin, digested on-bead with trypsin, and the resulting tryptic peptides isotopically labeled at N-terminal and lysine ε-amino groups by reductive dimethylation (ReDiMe) with “heavy” (D₂C¹³O, probe-treated sample) or “light” (H₂CO, control sample) formaldehyde. LC-MS/MS analysis of the combined heavy and light samples enabled identification and quantification of proteins based on MS2 and MS1 signals, respectively. (B) Representative ReDiMe ratio plot for proteins identified from mice treated with probe 8 (heavy) versus drug 4 (light, control). Proteins with heavy/light ratios (or R values) ≥ 4 (dashed red line) were considered covalent targets of probe 8. See Figure S3 for the corresponding ReDiMe ratio plots for probes 5–7. (C) Bar graph depicting the number of protein targets for the indicated probes in mouse liver. (D) Left bar, stacked bar graph showing the aggregate number of protein targets of probes 5–8 and the fraction of these targets enriched by one or more probes. Right bar, fraction of protein targets of probes 5–8 representing proteins previously identified as targets of chemically reactive metabolites in mice, as assessed by presence in the Target Protein Database (TPDB; http://targetprotein.res.ku.edu). (E) Stacked bar graph depicting the shared and unique protein targets of probes 5–8 in mouse liver. (F) Bar graph comparing the percentage of proteins containing an annotated functional cysteine residue in the Uniprot Knowledgebase for all mouse proteins (3%) versus proteins identified as targets of probes 5–8 (15%). (G) Stacked bar depicting the percentage of protein targets of probe 5–8 for which orthologous human proteins were identified (combined red and blue sections) and found to be targets of cysteine-directed fragment electrophiles (blue section) in a recent chemical proteomic study. (H) Bar graph depicting the percentage of protein targets of probes 5, 6, and 8 that are involved in the indicated primary metabolic processes, according to the PANTHER classification system (http://pantherdb.org). The protein targets were first classified by biological process (Figure S4A), which revealed a major subset of targets (~60% for all combined probes) classified as metabolic. These metabolic targets were further categorized by metabolic process, and the majority (~70% for all combined probes) were listed as primary metabolic (Figure S4B). The data in panel H represent further classification of these metabolic targets into subcategories of primary metabolic processes.

Figure 3

Characterization of the proteomic reactivity of the troglitazone probe 5 in human hepatocytes. (A) Analysis of the membrane and soluble proteomic reactivity of primary human hepatocytes treated with vehicle (DMSO) or probe 5 (20 μM, 2 h). (B) A ReDiMe ratio plot for proteins enriched and identified from human hepatocytes treated with probe 5 (heavy) versus DMSO control (light) corresponding probe/control (heavy/light isotopic) ratios. Proteins with R values ≥ 4 (dashed red line) were considered covalent targets of probe 5. (C) Venn diagram demonstrating the overlap of orthologous or closely related proteins found to be targets of probe 5 in both mouse liver and human hepatocytes. (D) Bar graph depicting the percentage of protein targets of probes 5, 6, and 8 from human hepatocytes that are involved in the indicated primary metabolic processes, according to the PANTHER classification system. The protein targets were first classified by biological process, which revealed a major subset (~65%) classified as metabolic (Figure S4C). Further categorization indicated that 75% of the metabolic targets were involved in primary metabolic processes (Figure S4D). The data in panel D represents further classification of these metabolic targets into sub-categories of primary metabolic processes.

Figure 4

Characterization of high-occupancy protein reactivity events for acetaminophen (4) in mouse liver. (A) Gel-based profile of competitive blockade of probe 8 reactivity with mouse liver proteins by acetaminophen (4). Mice were treated with vehicle or 4 at the indicated dose (2 h, i.p.), followed by treatment with the probe 8 (100 mg/kg, 1 h, i.p.). Animals were then sacrificed and liver soluble and membrane protein fractions conjugated to rhodamine-azide by CuAAC and analyzed by SDS-PAGE and in-gel fluorescence scanning. Arrows mark proteins for which probe 8 reactivity was competed by pretreatment with 4. (B) A ReDiMe ratio plot for proteins identified from liver tissue of mice treated with 4 (500 mg/kg, 2 h, i.p.) (light) or vehicle (heavy), followed by probe 8 (100 mg/kg, 1 h, i.p.). Proteins with R values ≥ 3 (dashed red line) were considered to be competitively blocked in reactivity with probe 8 and designated as high-occupancy targets of 4 (shown in the magnified region on the right). High-occupancy targets containing catalytic cysteine or selenocysteine residues are shown in red and blue, respectively.

Figure 5

Comparative proteomic reactivity profiling of acetaminophen and its less toxic analogue AMAP. (A) Structures of AMAP (9) and the alkyne-modified probe analogue 10. (B) Comparative gel-based analysis of the mouse liver proteomic reactivity of probes 8 and 10 in vivo. Mice were treated with the indicated probe and dose (10–300 mg/kg, 2 h, i.p.), sacrificed, and liver soluble and membrane protein fractions conjugated to rhodamine-azide by CuAAC and analyzed by SDS-PAGE and in-gel fluorescence scanning. (C) A ReDiMe ratio plot for proteins identified from liver tissue of mice treated wwith 4 or 9 (400 mg/kg, 2 h, i.p.), followed by 8 (100 mg/kg, 1 h, i.p.). Dashed red lines designated proteins with log₂ R values > 1 (preferentially competed by 4) or < 1 (preferentially competed by 9). Six of the fourteen proteins preferentially competed by 4 were selenocysteine-containing proteins (shown in blue).

Figure 6

Schematic depicting pathways containing high-occupancy protein targets of acetaminophen (4), including redox homeostasis (e.g., Txnrd1, Txnrd2, Msrb1) and ER-associated degradation (ERAD) (Park7, Selk, Timp) pathways, the disruption of which stimulates stress signaling by activation of the apoptosis signaling kinase 1 (ASK1). Signaling through ASK1, followed by activation of JNK, is required for acetaminophen-induced cell death.