Here, we attempted to unravel the metabolism reprogramming in skeletal muscle of newly diagnosed diabetic patients. Since diabetes is a metabolic disease, our goal was to identify the metabolic disorders that occur at the onset of the disease. We used the gene expression data from the most comprehensive human skeletal muscle transcriptome study to date. The diabetic sample consists of participants who have newly been diagnosed with diabetes (with an average blood glucose concentration of ~ 7.2 mmol/L) and had not taken any medications. For understanding metabolism alterations in T2DM, we first reconstructed our functional muscle-specific metabolic model because we could not utilize Bordbar [3] or Nogiec [4] muscle metabolic models due to the being small-scale. Varemo model [5] has been the most complete muscle metabolic model to date, but because of the wrong gene-protein-reaction (GPR) association and lack of biomass production reaction, it is not suitable for simulation. Thus, we reconstructed the most updated skeletal muscle metabolic model. The model quality was successfully validated for several pathways, and metabolites productions as well as biomass production. This model is freely available and can be used for other muscle metabolic analysis.
We employed the reconstructed model for investigation of dysmetabolism in T2DM by using topology-based and constraint-based analyses. By topology-based analysis, we determined those metabolites that are involved in reactions in which the associated genes are significantly dysregulated. We also applied constraint-based analysis to quantitatively compare metabolic capabilities between healthy and newly diagnosed diabetic patients. In these analyses ubiquinone and ubiquinol the oxidized and reduced forms of coenzyme Q10, respectively, were identified as reporter metabolites. These metabolites also participate in some perturbed reactions. Coenzyme Q10, a critical component of oxidative phosphorylation, is produced or consumed in the electron transport of the mitochondrial respiratory chain and possesses antioxidant and anti-inflammatory properties. Pieces of evidence have revealed that supplementations of coenzyme Q10 in T2DM patients, preserve mitochondrial function, reduce oxidative stress, and improve glucose tolerance [14, 15]. In addition, the administration of coenzyme Q10 in patients with prediabetes has alleviated the progression from prediabetes to diabetes [16]. Therefore, this metabolite should receive more attention in the treatment of diabetes.
We also found that metabolic alterations occurred in carbohydrates, fatty acids, lipids, and amino acids metabolisms. Dysregulation of Branched-chain amino acids (BCAAs) metabolism results in the serine phosphorylation of insulin receptor substrates and subsequent uncoupling of insulin signaling [17]. Moreover, perturbations in metabolism of inositol phosphate, keratin, chondroitin, and heparan sulfate were observed. Inositol mediates insulin signal transduction, associated with glucose uptake and plays an important role in oxidative stress and inflammation. Administration of inositol supplements improves glucose metabolism and insulin resistance [18]. Chondroitin sulfate, keratin sulfate, heparan sulfate, and hyaluronic acid are glycosaminoglycans (GAGs). GAGs play a vital role in cell physiology including cell signaling, proliferation, and cell adhesion. In T2DM, alteration in GAGs structures and functions can occur [19]. The insulin-sensitizing and anti-diabetic impacts of some GAGs have been reported [20, 21]. Perturbations in GAGs related pathways and sphingolipid metabolism imply the role of the ECM in insulin resistance, which involved in the regulation of insulin action. Our analyses suggested that it seems muscle dysmetabolism disrupts the abundance of metabolites involved in the process of sensing insulin and transmitting insulin signal into the cell. Decreased insulin sensitivity results in lower expression of insulin-responsive genes, reduced glucose uptake, and consequently changes in energy, glucose, lipids, and amino acids metabolisms (Fig. 4).
As the final analysis, potential metabolite markers were predicted. For this purpose, we first identified exchange metabolites that their flux interval was shifted in comparison with healthy ones. Then, a wrapper feature selection method applied to find potential metabolite markers. This approach led to the identification of 13 exchange reactions that could discriminate healthy individuals from T2DM patients with 81% accuracy. We validated these markers using a separate gene expression data from another study that has investigated the gene expression pattern in the muscle of obese and non-obese of healthy and diabetic individuals. Their result has shown that transcriptional reprogramming in obesity is similar to that occur in T2DM [7]. Here, we found that using all data from this study, including normoglycemic obese individuals for validation, the accuracy of our proposed marker was notably low (~ 50%). We thought that this low accuracy may be due to the metabolic similarities between obese and diabetic individuals. In fact, as noted in the original data article, obese and diabetic individuals have shown similar gene expression patterns [7] that can lead to similar metabolisms. Moreover, obese individuals had high levels of fasting insulin levels, which demonstrate the insulin resistance in this group. To test this issue, we removed obese healthy individuals from the validation set and examined the classification result, which leads to the improvement in accuracy to 78.50%. Therefore, this analysis confirmed both the original data article claim about the similarity of the gene expression pattern between obese and diabetic individuals and the appropriate efficiency of our proposed markers in identifying insulin-resistant individuals. These markers represent some of the insulin-resistance associated abnormalities represented in exchanging metabolites. Important metabolites such as methylglyoxal, hyaluronan, retinoic acid (vitamin A), sodium, alanine, and aspartate are present in the predicted markers. Notably, this method also successfully identified glucose as one of the markers. Methylglyoxal, a glycolytic by-product, is a toxic and highly reactive compound involved in cellular dysregulations. This compound modifies nucleotides, proteins, and lipids producing advanced glycation end products (AGEs) which also contribute to the diabetes complications. Methylglyoxal is associated with oxidative stress, cellular inflammation, and age-related disease such as diabetes [22]. Several studies have revealed the impact of methylglyoxal on insulin signaling pathways and insulin resistance [23, 24] and recently this metabolite has introduced as an emerging marker for T2DM diagnosis [22]. Hyaluronan is an anionic GAG metabolite implicated in several functions like as cell signaling, proliferation and migration, and angiogenesis. This metabolite also contributes to the inflammation and pathogenesis of T2DM [25, 26]. Studies have demonstrated that hyaluronan increases in the serum and skeletal muscle of T2DM subjects [27, 28]. Several analyses have shown the possible roles of vitamin A in glucose metabolism, and the progression of insulin resistance [29–31]. Association of purine metabolites such as xanthine and hypoxanthine with the risk of T2DM incidence and complications has been reported [32, 33]. Also, change in serum concentration of amino acids [34], and sodium [35] is associated with obesity and T2DM. In addition, the implication of gangliosides in insulin resistance has been shown [36–38]. Here, GQ1b ganglioside was predicted in the top-ranked metabolites list that can be considered for future analysis. We also checked metabolomics-based studies of T2DM and the Human Metabolome Database for these metabolites [39]. We found that glucose, hypoxanthine, alanine, aspartate, galactose, hyaluronate, and methylglyoxal levels have been reported to be associated with T2DM [32, 39, 40]. These metabolite markers can be used for further empirical investigation to verify their prognostic and diagnostic values in insulin resistance.