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bioRxiv · 10.1101/2023.09.20.558442

Mapping relationships between glutathione and SLC25 transporters in cancers using hybrid machine learning models

Kennedy, L. S.·Sandhu, J. K.·Harper, M.-E.·Cuperlovic-Culf, M.
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Abstract

BackgroundAlterations of metabolism, including changes in mitochondrial and glutathione (GSH) metabolism, are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences are unable to annotate functions in biological contexts, meaning new approaches must be developed for this task. ResultsWe develop a flexible ML framework for functional annotation from diverse feature data. This hybrid approach leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves an average AUROC across functional annotation tasks of 0.900 and can be effectively applied to annotate a range of biological functions. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to GSH metabolism in cancers. SLC25A24 and the orphan SLC25A43 are also predicted to be associated with mGSH metabolism by this approach and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39. ConclusionThese findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers.

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Kennedy, L. S., Sandhu, J. K., Harper, M.-E., Cuperlovic-Culf, M.. 2023-09-21. Mapping relationships between glutathione and SLC25 transporters in cancers using hybrid machine learning models. https://doi.org/10.1101/2023.09.20.558442

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