What mRNA Abundances Can Tell us about Metabolism

Abstract

Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.

Profiling the Metabolism of Human Cells by Deep 13C Labeling

“…In cancer biology, for example, metabolic enzymes associated with transformed cells may present new targets for drugs that aim to reduce cell proliferation (Vander Heiden 2011), and similarly, metabolic traits of specific immune cell types are being explored in the context of autoimmune disorders (Freitag et al 2016). Systematic profiling of metabolic activities in human cells is therefore of fundamental importance for understanding the physiology of normal cells and their metabolic derangements in human disease (Hoppe 2012). …”

Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

“…The relationship between transcription and degradation of mRNA and control of flux is indirect, mediated by protein translation, folding, and degradation, complex formation, posttranslational modification, allosteric regulation, and substrate availability. Indeed, as reviewed by [64], experimentally observed correlations among RNA-seq or microarray data (each itself an imperfect proxy for mRNA abundance or transcription rate), protein abundance, enzyme activity, and fluxes are variable and often weak.…”

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

“…Transcriptome data, in form of microarray data or RNAseq data, is the most widely used omics data for integration with GEMs ( Figure 5C) 56 , most likely due to the relative easiness of generating these data and the fact that they are genome-wide in contrary to most other omics data 57 . The basic idea underlying the use of transcriptome data is that there is a relationship between the expression level of a gene and the flux that the corresponding enzyme catalyzes.…”

“…A main disadvantage of using transcriptome data is that there are many intermediate biological processes between gene expression and metabolic fluxes, sometimes leading to low correlation between the transcriptome and the biological functions57 . To improve this, additional levels of…”