Omics markers of the red cell storage lesion and metabolic linkage

Abstract

The introduction of omics technologies in the field of Transfusion Medicine has significantly advanced our understanding of the red cell storage lesion. While the clinical relevance of such a lesion is still a matter of debate, quantitative and redox proteomics approaches, as well quantitative metabolic flux analysis and metabolic tracing experiments promise to revolutionise our understanding of the role of blood processing strategies, inform the design and testing of novel additives or technologies (such as pathogen reduction), and evaluate the clinical relevance of donor and recipient biological variability with respect to red cell storability and transfusion outcomes. By reviewing existing literature in this rapidly expanding research endeavour, we highlight for the first time a correlation between metabolic markers of the red cell storage age and protein markers of haemolysis. Finally, we introduce the concept of metabolic linkage, i.e. the appreciation of a network of highly correlated small molecule metabolites which results from biochemical constraints of erythrocyte metabolic enzyme activities. For the foreseeable future, red cell studies will advance Transfusion Medicine and haematology by addressing the alteration of metabolic linkage phenotypes in response to stimuli, including, but not limited to, storage additives, enzymopathies (e.g. glucose 6-phosphate dehydrogenase deficiency), hypoxia, sepsis or haemorrhage.

Figure 1

Advancements in omics technologies for Transfusion Medicine applications. (A) An overview of the QconCAT approach for quantitative proteomics and (B) switch-tag approach for redox proteomics applications,. (C) An overview of a tracing quantitative metabolic experiment, a workflow that can be exploited to inform quantitative metabolic flux analysis elaboration with systems biology tools.

Figure 2

Metabolic markers of the red blood cell (RBC) storage lesion have been identified through statistical analysis, such as Partial least-square discriminant analysis (A) and receiver operating characteristic curves (ROC),. A combination of redox proteomics, quantitative proteomics and metabolic flux analyses has revealed a role for the oxidative stress-dependent regulation of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) activity in mediating the activation of the pentose phosphate pathway (PPP) to generate reducing equivalents in the attempt to counteract oxidative stress over storage,. (B). Correlative analysis of metabolic and protein markers of the storage lesion was here performed and plotted as a heat map (black = R>0.75) (C). Of note, metabolic markers of the RBC storage age correlated with the absolute concentration of supernatant haemoglobin over storage, a marker of RBC vesiculation and haemolysis (D–G). Though only correlative analyses are here provided, it is interesting to note that all the metabolites showing the highest positive correlations with supernatant haemoglobin were part of the purine catabolism/salvage pathway, a pathway that is activated by oxidative lesion to the purine nucleoside pool and is in part counteracted by salvage reactions fueled by aspartate consumption and resulting in fumaratemalate accumulation (H).

Figure 3

Metabolic linkage. Metabolite levels in stored red blood cells (RBCs) are significantly correlated, a phenomenon that is explained by enzymatic biochemical constraints and here defined as metabolic linkage (A). A few examples of metabolites showing significant correlations among each other in 60 packed RBC extracts sampled at random storage time points is shown in (B–J). Figures and panels are the result of the elaborations of metabolomics analyses of samples kindly provided by Dr. Eldad Hod at Columbia University, NY, USA.

Figure 4

Enzymopathies affect the metabolic linkage. Determination of the metabolic linkage in stored red blood cells (RBCs) from glucose 6-phosphate dehydrogenase deficient donors reveals a re-wiring of RBC metabolism (A). As a result, significant correlations observed in healthy volunteers are lost (B), while novel ones appear (C). Figures and panels are the result of the elaborations of freely available data from Tzounakas et al..