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Tandem mass tag-based quantitative proteomic profiling identifies candidate serum biomarkers of drug-induced liver injury in humans

Ravindra, Kodihalli C., Vaidya, Vishal S., Wang, Zhenyu, Federspiel, Joel D., Virgen-Slane, Richard, Everley, Robert A., Grove, Jane I., Stephens, Camilla, Ocana, Mireia F., Robles-Díaz, Mercedes, Isabel Lucena, M., Andrade, Raul J., Atallah, Edmond, Gerbes, Alexander L., Weber, Sabine, Cortez-Pinto, Helena, Fowell, Andrew J., Hussaini, Hyder, Bjornsson, Einar S., Patel, Janisha, Stirnimann, Guido, Verma, Sumita, Elsharkawy, Ahmed M., Griffiths, William J. H., Hyde, Craig, Dear, James W., Aithal, Guruprasad P., Ramaiah, Shashi K.·2023·Nature Communications
DOI 10.1038/s41467-023-36858-6PMC PMC9984368
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Abstract

Diagnosis of drug-induced liver injury (DILI) and its distinction from other liver diseases are significant challenges in drug development and clinical practice. Here, we identify, confirm, and replicate the biomarker performance characteristics of candidate proteins in patients with DILI at onset (DO; n = 133) and follow-up ( n = 120), acute non-DILI at onset (NDO; n = 63) and follow-up ( n = 42), and healthy volunteers (HV; n = 104). Area under the receiver operating characteristic curve (AUC) for cytoplasmic aconitate hydratase, argininosuccinate synthase, carbamoylphosphate synthase, fumarylacetoacetase, fructose-1,6-bisphosphatase 1 (FBP1) across cohorts achieved near complete separation (range: 0.94–0.99) of DO and HV. In addition, we show that FBP1, alone or in combination with glutathione S-transferase A1 and leukocyte cell-derived chemotaxin 2, could potentially assist in clinical diagnosis by distinguishing NDO from DO (AUC range: 0.65–0.78), but further technical and clinical validation of these candidate biomarkers is needed.

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Fig. 1 Schematic overview of the strategy for discovery, confirmation, and replication of DILI candidate biomarkers. Biomarkers cytoplasmic aconitate hydratase (ACO1), fructosebisphosphate aldolase B (ALDOB), argininosuccinate synthase (ASS1), liver carboxylesterase 1 (CES1), carbamoylphosphate synthase (CPS1), fructose-1,6-bisphosphatase 1 (FBP1), fumarylacetoacetase (FAH), glutathione S-transferase A1 (GSTA1), 4-hydroxyphenylpyruvate dioxygenase (HPD), leukocyte cell-derived chemotaxin-2 (LECT2), ornithine carbamoyltransferase (OTC) were identified using tandem mass tagging (TMT) mass spectrometry (MS) and subsequent ratio of light peptide/heavy isotopically labeled peptide or enzyme-linked immunosorbent assay (ELISA) in serum from healthy volunteers (HV), patients with nonalcoholic fatty liver disease (NAFLD), DILI or non-DILI, either at onset (DO or NDO, respectively) or at follow-up (DF or NDF).
Fig. 1 Schematic overview of the strategy for discovery, confirmation, and replication of DILI candidate biomarkers. Biomarkers cytoplasmic aconitate hydratase (ACO1), fructosebisphosphate aldolase B (ALDOB), argininosuccinate synthase (ASS1), liver carboxylesterase 1 (CES1), carbamoylphosphate synthase (CPS1), fructose-1,6-bisphosphatase 1 (FBP1), fumarylacetoacetase (FAH), glutathione S-transferase A1 (GSTA1), 4-hydroxyphenylpyruvate dioxygenase (HPD), leukocyte cell-derived chemotaxin-2 (LECT2), ornithine carbamoyltransferase (OTC) were identified using tandem mass tagging (TMT) mass spectrometry (MS) and subsequent ratio of light peptide/heavy isotopically labeled peptide or enzyme-linked immunosorbent assay (ELISA) in serum from healthy volunteers (HV), patients with nonalcoholic fatty liver disease (NAFLD), DILI or non-DILI, either at onset (DO or NDO, respectively) or at follow-up (DF or NDF).CC BY · PubMed Central
Fig. 2 Discovery proteomics to identify candidate biomarkers. a Venn diagram showing statistically significant differentially expressed, liver-enriched proteins (p-value <0.1, two-sided and Benjamini-Hochberg-adjusted) between DILI onset (DO, n = 10) and healthy volunteer (HV, n = 10) samples, DO and DILI follow-up (DF, n = 10) samples, DO and non-DILI onset (NDO, n = 5), NDO and HV. The arrows indicate the liver-enriched candidate biomarkers selected from each group-wise comparison. b Heatmap of the 89-liver enriched, differentially expressed proteins (encoded by 88 genes) between all the 6 groups; + indicates the traditional biomarkers and # indicates selected candidate biomarkers. The variance stabilizing normalization (VSN) normalized expression data were converted to z-scores by row and represented with a color scheme ranging from -1.5 (blue), 0 (white), 1.5 (red), in 101 color step gradients. Row-wise, white represents values at the mean, with red and blue representing values of at least 1.5 standard deviations above or below the mean, respectively. c VSN normalized relative expression of candidate biomarkers identified from the analysis (two-sided and Benjamini-Hochberg-adjusted) shown in a, HV (n = 10), DO (n = 10), DF (n = 10), NDO (n = 5), NDF (n = 5) and NAFLD (n = 10). The center line in the box corresponds to the median, the box represents the first and third quartiles, and the whiskers represent the minimum and maximum observed values. *P < 0.1; **P < 0.01; ***P < 0.001. Source data are provided as a Source Data file.
Fig. 2 Discovery proteomics to identify candidate biomarkers. a Venn diagram showing statistically significant differentially expressed, liver-enriched proteins (p-value <0.1, two-sided and Benjamini-Hochberg-adjusted) between DILI onset (DO, n = 10) and healthy volunteer (HV, n = 10) samples, DO and DILI follow-up (DF, n = 10) samples, DO and non-DILI onset (NDO, n = 5), NDO and HV. The arrows indicate the liver-enriched candidate biomarkers selected from each group-wise comparison. b Heatmap of the 89-liver enriched, differentially expressed proteins (encoded by 88 genes) between all the 6 groups; + indicates the traditional biomarkers and # indicates selected candidate biomarkers. The variance stabilizing normalization (VSN) normalized expression data were converted to z-scores by row and represented with a color scheme ranging from -1.5 (blue), 0 (white), 1.5 (red), in 101 color step gradients. Row-wise, white represents values at the mean, with red and blue representing values of at least 1.5 standard deviations above or below the mean, respectively. c VSN normalized relative expression of candidate biomarkers identified from the analysis (two-sided and Benjamini-Hochberg-adjusted) shown in a, HV (n = 10), DO (n = 10), DF (n = 10), NDO (n = 5), NDF (n = 5) and NAFLD (n = 10). The center line in the box corresponds to the median, the box represents the first and third quartiles, and the whiskers represent the minimum and maximum observed values. *P < 0.1; **P < 0.01; ***P < 0.001. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 3 Performance of candidate biomarkers in confirmatory cohort. Relative intensity levels of selected candidate biomarkers differentially present in DILI onset (DO, n = 82) compared with healthy volunteers (HV, n = 60) and non-DILI onset (NDO, n = 34). The center line in the box corresponds to the median, the box represents the first and third quartiles, and the whiskers represent the minimum and maximum observed values. P (t test, two-sided, no adjustment) and fold change (FC) values are presented in the boxes (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns = not significant). Source data are provided as a Source Data file.
Fig. 3 Performance of candidate biomarkers in confirmatory cohort. Relative intensity levels of selected candidate biomarkers differentially present in DILI onset (DO, n = 82) compared with healthy volunteers (HV, n = 60) and non-DILI onset (NDO, n = 34). The center line in the box corresponds to the median, the box represents the first and third quartiles, and the whiskers represent the minimum and maximum observed values. P (t test, two-sided, no adjustment) and fold change (FC) values are presented in the boxes (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns = not significant). Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 4 AUC-ROC analysis of multivariate biomarker models. AUC-ROC shown for FBP1 + GSTA1 + LECT2 biomarker model in non-DILI patients at onset (NDO) versus DILI patients at onset (DO) in the confirmatory cohort (n = 116) (a) and the replication cohort (n = 65) (b).
Fig. 4 AUC-ROC analysis of multivariate biomarker models. AUC-ROC shown for FBP1 + GSTA1 + LECT2 biomarker model in non-DILI patients at onset (NDO) versus DILI patients at onset (DO) in the confirmatory cohort (n = 116) (a) and the replication cohort (n = 65) (b).CC BY · PubMed Central
Fig. 5 Correlation between biomarkers and ALT. Spearman’s r correlation coefficient (R) (test R = 0 or not, two-sided, no adjustment) was calculated between ALT and either previously identified biomarkers (glutamate dehydrogenase (GLDH) and cytokeratin-18 (CK18)) or candidate biomarkers. Circles indicate HV (n = 60) and DO (n = 82) samples from the confirmatory cohort. Values are log2 transformed and p values are shown. Source data are provided as a Source Data file.
Fig. 5 Correlation between biomarkers and ALT. Spearman’s r correlation coefficient (R) (test R = 0 or not, two-sided, no adjustment) was calculated between ALT and either previously identified biomarkers (glutamate dehydrogenase (GLDH) and cytokeratin-18 (CK18)) or candidate biomarkers. Circles indicate HV (n = 60) and DO (n = 82) samples from the confirmatory cohort. Values are log2 transformed and p values are shown. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 6 Visualization of liver pathway signatures. Visualization of apparent differences in liver zone pathway signatures among NDO, DO, and HV, using single sample gene set enrichment analysis of the discovery proteomics dataset. Pathway enrichment from differentially expressed genes between NDO (n = 5) and HV (n = 10), and NDO and DO (n = 10) shown in Supplementary Fig. 6. Pathway scores are calculated from expression data for each gene set to allow side-by-side visualization. Healthy volunteers (HV) scores were combined with DILI onset and follow-up (DO and DF (n = 10)) or with non-DILI onset and follow-up (NDO and NDF (n = 5)) to simulate transition through healthy, onset, and follow-up in either DILI or non-DILI disease. The structure of individual lobules in the liver is represented having parenchymal cells (hepatocytes) and multiple non-parenchymal cells aligned between a capillary network and a central vein and liver interzonal signature expression plotted below the lobule zones. Smooth lines were generated by loess method, gray indicates standard deviation. Zonal expression levels of differentially expressed proteins which are involved in gluconeogenesis, β-oxidation, glycolysis, glutamate biosynthesis, and xenobiotics metabolism is illustrated below the graphs. Image was made with BioRender (# FR23R67B3Z). Source data are provided as a Source Data file.
Fig. 6 Visualization of liver pathway signatures. Visualization of apparent differences in liver zone pathway signatures among NDO, DO, and HV, using single sample gene set enrichment analysis of the discovery proteomics dataset. Pathway enrichment from differentially expressed genes between NDO (n = 5) and HV (n = 10), and NDO and DO (n = 10) shown in Supplementary Fig. 6. Pathway scores are calculated from expression data for each gene set to allow side-by-side visualization. Healthy volunteers (HV) scores were combined with DILI onset and follow-up (DO and DF (n = 10)) or with non-DILI onset and follow-up (NDO and NDF (n = 5)) to simulate transition through healthy, onset, and follow-up in either DILI or non-DILI disease. The structure of individual lobules in the liver is represented having parenchymal cells (hepatocytes) and multiple non-parenchymal cells aligned between a capillary network and a central vein and liver interzonal signature expression plotted below the lobule zones. Smooth lines were generated by loess method, gray indicates standard deviation. Zonal expression levels of differentially expressed proteins which are involved in gluconeogenesis, β-oxidation, glycolysis, glutamate biosynthesis, and xenobiotics metabolism is illustrated below the graphs. Image was made with BioRender (# FR23R67B3Z). Source data are provided as a Source Data file.CC BY · PubMed Central

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Authors
Ravindra, K. C., Vaidya, V. S., Wang, Z., Federspiel, J. D., Virgen-Slane, R., Everley, R. A., Grove, J. I., Stephens, C., Ocana, M. F., Robles-Díaz, M., Isabel Lucena, M., Andrade, R. J., Atallah, E., Gerbes, A. L., Weber, S., Cortez-Pinto, H., Fowell, A. J., Hussaini, H., Bjornsson, E. S., Patel, J., Stirnimann, G., Verma, S., Elsharkawy, A. M., Griffiths, W. J. H., Hyde, C., Dear, J. W., Aithal, G. P., & Ramaiah, S. K.
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Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-03-03
Volume
14
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-023-36858-6
PMC
PMC9984368
Cited by
44
References
3
Language
en
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