MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic level
IntroductionDrug-induced liver injury (DILI) has been investigated at the patient level. Analysis of gene perturbation at the cellular level can help better characterize biological mechanisms of hepatotoxicity. Despite accumulating drug-induced transcriptome data such as LINCS, analyzing such transc...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1398370/full |
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| Summary: | IntroductionDrug-induced liver injury (DILI) has been investigated at the patient level. Analysis of gene perturbation at the cellular level can help better characterize biological mechanisms of hepatotoxicity. Despite accumulating drug-induced transcriptome data such as LINCS, analyzing such transcriptome data upon drug treatment is a challenging task because the perturbation of expression is dose and time dependent. In addition, the mechanisms of drug toxicity are known only as literature information, not in a computable form.MethodsTo address these challenges, we propose a Multi-Dimensional Transcriptomic Ruler (MDTR) that quantifies the degree of DILI at the transcriptome level. To translate transcriptome data to toxicity-related mechanisms, MDTR incorporates KEGG pathways as representatives of mechanisms, mapping transcriptome data to biological pathways and subsequently aggregating them for each of the five hepatotoxicity mechanisms. Given that a single mechanism involves multiple pathways, MDTR measures pathway-level perturbation by constructing a radial basis kernel-based toxicity space and measuring the Mahalanobis distance in the transcriptomic kernel space. Representing each mechanism as a dimension, MDTR is visualized in a radar chart, enabling an effective visual presentation of hepatotoxicity at transcriptomic level.Results and DiscussionIn experiments with the LINCS dataset, we show that MDTR outperforms existing methods for measuring the distance of transcriptome data when describing for dose-dependent drug perturbations. In addition, MDTR shows interpretability at the level of DILI mechanisms in terms of the distance, i.e., in a metric space. Furthermore, we provided a user-friendly and freely accessible website (http://biohealth.snu.ac.kr/software/MDTR), enabling users to easily measure DILI in drug-induced transcriptome data. |
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| ISSN: | 1663-9812 |