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Published on: January 27, 2023
Artificial intelligence uncovers carcinogenic human metabolites
Aayushi Mittal1, Sanjay Kumar Mohanty1, Vishakha Gautam1
1Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India.
Abstract:
The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.
Insights
Metabokiller accurately predicts carcinogens by analyzing cellular damage indicators. This new tool identifies potential human carcinogens and is validated by lab experiments.
Area of Science:
- Genomics
- Toxicology
- Computational Biology
Background:
- Eukaryotic genomes face constant threats from various compounds, potentially leading to malignant transformation despite DNA repair mechanisms.
- Accurate prediction of carcinogens remains challenging due to limited data on known carcinogens and non-carcinogens.
Purpose of the Study:
- To develop an accurate and interpretable carcinogen prediction tool named Metabokiller.
- To identify potential carcinogenic human metabolites using the developed classifier.
Main Methods:
- Developed Metabokiller, an ensemble classifier assessing electrophilicity, proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic responses.
- Validated predictions using functional assays in Saccharomyces cerevisiae and human cells with flagged metabolites (4-nitrocatechol, 3,4-dihydroxyphenylacetic acid).
Main Results:
- Metabokiller accurately recognizes carcinogens and outperforms existing prediction methods.
- The tool identified potential carcinogenic human metabolites.
- Experimental validations showed high synergy with Metabokiller predictions for flagged metabolites.
Conclusions:
- Metabokiller offers a robust and interpretable approach to carcinogen prediction.
- The study highlights potential risks associated with specific human metabolites.
- The findings pave the way for improved toxicological assessments and cancer prevention strategies.
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