A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

Abstract

Background: Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.

Results: Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer.

Conclusions: Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.

Figure 1

A colour heatmap of the Pearson’s correlation coefficients computed for the 52 metabolites observed in the MDA-MB-231 cells exposed to normoxia (21% oxygen). The metabolites appear in the same order as in Table 1. The colours refer to the pair-wise correlation coefficient ranging from 1 (green) to -1 (red).

Figure 2

An alternative way to view metabolic pathways. Metabolism involves many inter-connections between metabolites; however there are traditional ways to represent pathways. In this schematic 1, 2, 3 and 4 represent 4 individual pathways as they are traditionally considered, however a pathway exists in metabolism that can connect these 4 pathways via the intermediates of each. This pathway (highlighted in black) could biochemically be more important than 1, 2, 3 or 4.

Figure 3

Network of pathways connecting differently correlated metabolites between normoxia and hypoxia in both MDA-MB-231and HCT116 cell lines. Nodes unique to MDA-MB-231 cells are shown in white, nodes unique to HCT116 cells are shown in black, and nodes common between cell lines are shown in grey. The KEGG identification code is given for each metabolite listed along with the enzymes used for each reaction.