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.
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Data availability
The raw RNA sequencing files are available at ArrayExpress under accession E-MTAB-11179. The processed datasets detailing about the compound SMILES, compound names, PubChem IDs, InChIs, Bioactivity status and their source information are accessible from GitHub at https://github.com/the-ahuja-lab/Metabokiller/tree/main/datasets as well as Zenodo at https://doi.org/10.5281/zenodo.6683106 repositories. Source data are provided with this paper.
Code availability
A Python package for Metabokiller is provided at https://pypi.org/project/Metabokiller/ or from the project GitHub page at https://github.com/the-ahuja-lab/Metabokiller and Zenodo at https://doi.org/10.5281/zenodo.6683106. Code used for building machine-learning models is provided on the project GitHub page.
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Acknowledgements
The authors would like to thank the IT-HelpDesk team of IIIT-Delhi for providing assistance with the computational resources. We thank all the members of the Ahuja lab for their intellectual contributions at various stages of this project. We also thank K. Datta for providing critical insights into this study and K. Chakraborty for sharing yeast strains. The Ahuja lab is supported by the Ramalingaswami Re-entry Fellowship (BT/HRD/35/02/2006), a re-entry scheme of the Department of Biotechnology, Ministry of Science & Technology, Government of India, Start-Up Research Grant (SRG/2020/000232) from the Science and Engineering Research Board and an intramural Start-up grant from Indraprastha Institute of Information Technology-Delhi. The Sengupta lab is funded by the INSPIRE faculty grant from the Department of Science & Technology, India.
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Contributions
The study was conceived by G.A. Computational analysis workflows were designed by G.A., D.S, and A.Mi. Yeast experimental workflows were designed by G.A., and A.Mi., whereas, human experimental workflows were designed by S.N. Yeast-based assays were performed by A.Mi., S.A., and N.K.D. Human cell culture-based experiments were performed by S.S. Data compilation for the model building was performed by A.Mi., P.G., A.A., P.R., and analysis workflow was made by S.M., V.G., S.A., A.Mi., R.S., R.G. and P.G. V.P.S., A.Me. and J.T. assisted in data interpretation. Metabokiller Python package was created by S.K.M. Illustrations were drafted by A.M. and G.A. G.A. and A.Mi. wrote the paper. All authors have read and approved the manuscript.
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A provisional patent has been filed (reference no. 202111052929, application no. TEMP/E-1/60118/2021-DEL) describing the computational architecture of the Metabokiller. Usage of the Metabokiller Python package is free for the academic institutions, or for any academic-related project, however, for commercial usage, users must contact the authors.
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Extended data
Extended Data Fig. 1 Workflow detailing Metabokiller functionalities.
Schematic representation depicting the step-by-step workflow used to build all the six individual biochemical models and the ensemble model (Metabokiller). Up/downsampling approach was used to counteract the class imbalance. Signaturizer library was used to generate bioactivity features. Hyperparameter tuning was performed to obtain the best-performing model parameters. The ensemble model (Metabokiller) was built using biochemical features of experimentally validated carcinogens/non-carcinogens generated using six models. The majority voting method was used to assign the final carcinogenicity status.
Extended Data Fig. 2 Metabokiller harbors high prediction performance.
(a) Box plot depicting the AUCROC values of the bootstrapping (n = 20 repetitions) of the indicated models. (b-f) Box plots depicting the AUCROC, accuracy, F1 Score, precision, and recall of the indicated models as inferred from the 10-fold cross-validation. (g) Box plot depicting the model performance of the twenty Gradient Boosting Machine (GBM)-based models generated using bootstrapping technique (n = 20 repetitions). (h) Variables factor map (PCA) depicting the direction and contribution of all the six variables (individual models) representing the experimentally validated carcinogens (MKETn) in the Eigenspace. (i) Principal Component Analysis revealing the chemical heterogeneity between the carcinogens and non-carcinogens in the indicated datasets. The heatmap at the bottom depicts the relative enrichment of the indicated functional groups (RNH2: primary amine, R2NH: secondary amine, R3N: tertiary amine, ROPO3: monophosphate, ROH: alcohol, RCHO: aldehyde, RCOR: ketone, RCOOH: carboxylic acid, RCOOR: ester, ROR: ether, RCCH: terminal alkyne, RCN: nitrile) in both classes. (j) Bar graphs depicting the accuracy of Metabokiller on the indicated unseen datasets. In the box plots, center lines represent the medians; box limits indicate the 25th and 75th percentiles as determined by R software (ggplot2); whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; outliers are represented by dots.
Extended Data Fig. 3 Metabokiller unravels potential oncometabolites.
(a) Heatmap depicting the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) predictions on the Independent Dataset (I.D.) for indicated methods/tools. (b) Venn diagram depicting predicted carcinogenic human metabolites, further segregated based on prediction probability cutoffs. (c) Variables factor map depicting the contribution of all the six individual models in predicting carcinogenic metabolites from HMDB (probability cutoff ≥ 0.5). (d) Projection of the predicted carcinogens (indicated as red dots; probability cutoff ≥ 0.7) on the human metabolic space, achieved using iPath Web Server. (e) Schematic representation of the steps involved in processing pan-cancer metabolomics dataset. Of note, Pearson correlation was computed between log2 fold change (tumor vs healthy) and biochemical/carcinogenicity probabilities. (f) Heatmap detailing the correlation values further segregated based on cancer type. (g) Volcano plots depicting the differentially enriched/de-enriched metabolites in the indicated cancer datasets. Gray dots highlight the metabolites that do not qualify for the enrichment cutoff (log2 fold change ≥ 1 or ≤ -1, and p-value (adjusted) < 0.05), and green and red dots represent the metabolites that qualify for the enrichment cutoff and are predicted as non-carcinogenic and carcinogenic by Metabokiller respectively. The p-value was computed using two-sided Mann–Whitney U test and corrected using Benjamini-Hochberg method. (h) Structural information of some of the well-characterized oncometabolites reported in the literature and predicted by Metabokiller.
Extended Data Fig. 4 Experimental validations support Metabokiller predictions.
(a) Schematic representation highlighting the predicted-carcinogenic metabolic intermediates of the tyrosine metabolism pathway and aminobenzoate degradation pathway. (b) Box plots depicting the fluorescence intensity of propidium iodide staining indicating cell viability in the indicated conditions (n = 8 biological replicates) after 9 hours (left) and 12 hours (right) of treatment. Of note, heat-killed (HK) yeast cells were used as a positive control. Two-sided Mann–Whitney U test was used to compute statistical significance between the test conditions and the negative control. For left panel, the p-values are 0.0009 (HK); for 4NC: 0.96 (0.1 µM), 0.87 (1 µM), and 0.02 (10 µM); for DP: 0.59 (0.1 µM), 0.64 (1 µM), and 0.83 (10 µM). For right panel, the p-values are 0.0009 (HK); for 4NC: 0.63 (0.1 µM), 0.75 (1 µM), and 0.2 (10 µM); for DP: 0.42 (0.1 µM), 0.26 (1 µM), and 0.17 (10 µM). (c) Growth curve profiles of the treated and untreated wild-type yeast during transient exposure with the indicated conditions (n = 8 biological replicates with technical duplicates). Data points represent mean ± SD. Two-sided Student’s t-test was used to compute statistical significance between the positive (H2O2 treated yeast cells) and negative control (untreated yeast cells). The p-values are 0.9 (0 hrs), 1.5 × 10−6 (1.5 hrs), 4.85 × 10−6 (3 hrs), 4.45 × 10−16 (4.5 hrs), 1.62 × 10−10 (6 hrs), 2.27 × 10−18 (7.5 hrs), 6.41 × 10−13 (9 hrs), 1.04 × 10−23 (10.5 hrs), 5.82 × 10−34 (12 hrs). (d) Box plot depicting the results of reactive oxygen species (ROS) levels inferred using DCFH-DA dye-based assay in the indicated conditions (n = 8 biological replicates). Of note, ROS levels were measured 12 hours post-incubation. Notably, hydrogen peroxide (H2O2) treated yeast cells were used as a positive control. Two-sided Mann–Whitney U test was used to compute statistical significance between the test conditions and the negative control. The p-values are 0.003 (H2O2); for 4NC: 0.069 (0.1 µM), 0.1 (1 µM), and 0.001 (10 µM); for DP: 0.016 (0.1 µM), 0.087 (1 µM), and 0.07 (10 µM). The p-value cutoff for all the plots is 0.05. *, **, ***, and **** refer to p-values <0.05, <0.01, <0.001, and <0.0001, respectively. In the box plots, center lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; outliers are represented by dots.
Extended Data Fig. 5 RNA-Seq reveals mode-of-action of 4NC and DP.
(a) Bar plots depicting the total read counts (in millions) of the indicated RNA sequencing samples. (b) Box plot representing the distribution of the transformed read count data in the indicated conditions (n = 3 biological replicates). (c) Correlation plot showing the relationship between the individual RNA sequencing samples. Of note, 75% of the normalized and transformed data was used for the correlation analysis. (d-e) Box plots depicting the relative log expression of the 3 biological replicates of the indicated conditions before and after upper quantile normalization. (f) Volcano plot indicating the differentially expressed genes between the treated (metabolite treatment) and untreated conditions. p-value was computed using Wald test and corrected using Benjamini-Hochberg method (g) Metascape-based Functional Gene Ontology analysis identified the involvement of differentially expressed genes in the indicated prominent biological processes. (h) Schematic representation depicting the genomic alterations in the CAN1 gene in the indicated replicates. In the box plots, center lines represent the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; outliers are represented by dots.
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Mittal, A., Mohanty, S.K., Gautam, V. et al. Artificial intelligence uncovers carcinogenic human metabolites. Nat Chem Biol 18, 1204–1213 (2022). https://doi.org/10.1038/s41589-022-01110-7
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DOI: https://doi.org/10.1038/s41589-022-01110-7
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