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TMT Proteomics Identifies Biomarkers for Drug-Induced Liver Injury

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

Journal: Nature Communications

Published: 2023

Main Technology: TMT-based quantitative proteomic, ELISA, Area Under the Curve (AUC) Receiver Operating Characteristic (ROC) analysis

Background: Drug-induced liver injury (DILI) is a major clinical problem associated with significant morbidity and mortality. Most cases of DILI recover after early detection of causative medication and its discontinuation. Early detection and diagnosis of DILI is a major challenge as current biomarkers do not distinguish DILI from acute liver injury due to other etiologies. DILI and its distinction from other liver diseases are significant challenges in drug development and clinical practice.

Methods: This study utilized mass spectrometry (MS) with higher-order multiplexing via an isobaric labeling strategy to simultaneously identify and quantify serum proteins in multiple cohorts as sensitive and specific biomarkers for early detection and diagnosis of DILI. We combined Tandem Mass Tag (TMT) based reporter methodology with MS instrumentation capable of providing quantitative accuracy using synchronous precursor MS3 analysis that eliminates interference. Then we developed a targeted MS assay to assess the performance characteristics of the selected candidate biomarkers in a second longitudinal confirmatory cohort. Finally, the performance characteristics of top biomarkers were tested in a third, multicenter, prospective cohort.

Results: 1) During the discovery stage, 2323 proteins were identified in a cohort comprising patients with DILI; 2) The comparative analysis of DO vs HV，DO vs DF，NDO vs DO，NDO vs HV, a total of 89 proteins showed significant differential expression in respective pairwise comparisons and 51 proteins showed significant differences between at least two groups; 3) 13 proteins were selected as candidate biomarkers; 4) A positive biomarker panel (FBP1 + GSTA1 + LECT2) had the best performance, and the model had the highest specificity in identifying patients with DILI.

Conclusions: Through a large multicentre case-control study, the researchers used proteomic techniques and subsequent bioinformatics analysis to reveal that the prediction model based on FBP1+GSTA1+LECT2 can identify DILI and non-DILI liver disease, highlighting the important role of proteomic techniques in the clinical study of DILI.

Fig. 1: Schematic overview of the strategy for discovery, confirmation, and replication of DILI candidate biomarkers. (Ravindra, K. C et al. 2023)

Creative Proteomics has been dedicated to the field of life sciences and life technology, pioneering multi-omics integration experiments and analysis based on proteomics and metabolomics in the early stages. After years of development and accumulation, the company has established proteinomics technology platforms, including iTRAQ/TMT,DIA, PRM, modified proteomics, as well as comprehensive metabolomics technology platforms, including full-spectrum metabolomics, targeted metabolomics, and lipidomics. Corresponding data integration and analysis platforms have also been established, along with a scientifically sound service workflow and precise operating standards.

Reference

Ravindra, K. C et al. (2023). Tandem mass tag-based quantitative proteomic profiling identifies candidate serum biomarkers of drug-induced liver injury in humans. Nature communications, 14(1), 1215. https://doi.org/10.1038/s41467-023-36858-6

* For Research Use Only. Not for use in diagnostic procedures.

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