Link splice-isoform expression to cancer metabolism with GEMsplice

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Metabolic models rely on genes and proteins expression to estimate or predict a metabolic cell phenotype. In the case of cancer, it is now admitted that metabolism dysregulations play a crucial role in cancer onset and proliferation. However, most metabolic models only rely on gene expression, and do not account for splice-isoform expression and/or alteration.

To solve this gap, Claudio Angione developed GEMsplice, a desktop application that allows to link splice-isoform gene expression data to cancer metabolism. Here, he describes the features and benefits of GEMsplice.

Solving the gap in cancer metabolism models

Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict the cellular phenotype. A genome-scale analysis of cancer metabolism captures many effects that cannot be identified using standard transcriptomic analysis.

However, although metabolic models have been successfully integrated with transcriptomic data to provide a mechanistic link between genotype and phenotype in cancer, there is no method for integration of splice isoform expression levels into such models. As a result, transcriptomic data in metabolic models can only be integrated at the gene level. Expression data at the splice-isoform level is currently neglected or simply averaged within the same gene to approximate the expression at the gene level.

This issue has been outlined in a number of recent reviews, and recently acknowledged by the scientific community as one of the main issues of metabolic modelling. In fact, the incorporation of splice isoforms is needed to understand complex diseases like cancer, where alternative splicing plays a crucial role.

GEMsplice features

GEMsplice is the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. It is validated by generating cancer-versus-normal predictions on metabolic pathways and by comparing them with available literature on pathways affected by breast cancer.

GEMsplice uses gene expression and transcript level information to incorporate them into the model (Figure 1). As a result, it exploits the full potential of next-generation sequencing in the context of genome-scale metabolic reconstructions. A set of phenotype-specific RNA-Seq transcript expression levels in a variety of breast cancer types and stages from the Cancer RNA-Seq Nexus dataset (Li et al., 2016), including data from TCGA, GEO and SRA, are then mapped onto the model using constraint-based modeling. Cancer-specific metabolic models are finally generated and investigated using multilevel linear programming, leading to phenotype prediction for different types of breast cancer (Figure 1).

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Figure 1: GEMsplice incorporates RNA-Seq data into genome-scale metabolic models at the splice-isoform level.

GEMsplice is freely available for academic use on Github.

With respect to state-of-the-art methods, GEMsplice will enable for the first time computational analyses of metabolism at transcript level with splice-isoform resolution.

References:

Claudio Angione. (2018). Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism. Bioinformatics.

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