Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism

TL;DR

GEMsplice is a novel computational method that integrates splice-isoform expression data into genome-scale metabolic models, enabling detailed analysis of breast cancer metabolism at the transcript level.

Contribution

This work introduces GEMsplice, the first method to incorporate splice-isoform data into metabolic models, enhancing the understanding of gene expression's impact on metabolism.

Findings

Predicts metabolic effects of splice isoform changes in breast cancer

Validates predictions with pathway analysis and literature comparison

Enables transcript-level metabolic modeling for the first time

Abstract

Motivation: Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict cellular phenotype. During the process of gene expression, 95% of human genes can code for multiple proteins due to alternative splicing. While most splice variants of a gene carry the same function, variants within some key genes can have remarkably different roles. To bridge the gap between genotype and phenotype, condition- and tissue-specific models of metabolism have been constructed. However, current metabolic models only include information at the gene level. Consequently, as recently acknowledged by the scientific community, common situations where changes in splice-isoform expression levels alter the metabolic outcome cannot be modeled. Results: We here propose GEMsplice, the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. Using GEMsplice, we make full use of RNA-Seq quantitative expression profiles to predict, for the first time, the effects of splice isoform-level changes in the metabolism of 1455 patients with 31 different breast cancer types. We validate GEMsplice by generating cancer-versus-normal predictions on metabolic pathways, and by comparing with gene-level approaches and available literature on pathways affected by breast cancer. GEMsplice is freely available for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to state-of-the-art methods, we anticipate that GEMsplice will enable for the first time computational analyses at transcript level with splice-isoform resolution.