Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes

A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients

Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.

Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling

Background

Type 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence worldwide. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake and plays a critical role in T2DM. Here, we sought to provide a better understanding of the abnormalities in this tissue.

Methods

The muscle gene expression patterns were explored in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification approaches. Moreover, the potential of subtyping T2DM patients was evaluated based on the gene expression patterns.

Results

A machine-learning technique was applied to identify a set of genes whose expression patterns could discriminate diabetic subjects from healthy ones. A gene set comprising of 26 genes was found that was able to distinguish healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified.

Conclusions

This study indicates that T2DM is triggered by different cellular/molecular mechanisms, and it can be categorized into different subtypes. Subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of the abnormalities in each group and more effective therapeutic approaches in the future.

Cytoprotective effects of berry anthocyanins against induced oxidative stress and inflammation in primary human diabetic aortic endothelial cells

Type 2 diabetes is associated with oxidative stress and low-grade inflammation resulting in endothelial dysfunction (ED). This study determined to explore the protective effects of berry-derived anthocyanins (AC) with potent antioxidant and anti-inflammatory activities in human diabetic endothelial cells upon oxidative and inflammatory stressors. Cultured healthy human aortic endothelial cells (HAEC) and diabetic human aortic endothelial cells (D-HAEC) exposed to oxidative stress by hydrogen peroxide (H₂O₂, 75 μM) and lipopolysaccharide (LPS, 1 μg/mL) as an inflammatory inducer before treatment with AC (50 μl/ml). The results from cytotoxicity assays showed that AC had no significant effects in cell viability (P-value < 0.0001), and exposure to H₂O₂ 75 μM had a less toxic effect (P-value < 0.05). Although, AC significantly decreased H₂O₂-induced cytotoxicity and oxidative stress in both HAEC and D-HAEC cell lines (P-value < 0.0001), no positive impact of AC was found on the GSSG/GSH ratios (P-value < 0.05). Exposure to the LPS increased the production of IL-6 in both HAEC and D-HAEC cell lines (P-value < 0.0001), whereas AC treatment reduced LPS-induced IL-6 production in both cell lines with a more robust impact on D-HAEC (P-value < 0.0001). While LPS increased inflammasome assembling and caspase-1 activation, AC treatment inhibited caspase-1 activation in D-HAEC (P ≤ 0.05). This study indicated that berry anthocyanins reduced oxidative stress and inflammation via the inhibition of the NF-ƙB signaling pathway, which contributes to mitigating the diabetes-induced up-regulation of NF-ƙB.

Exosomes derived from monocytes and from endothelial cells mediate monocyte and endothelial cell activation under high d-glucose conditions

Diabetes mellitus type 2 (DMT2) is characterized by hyperglycemia and associated with low grade inflammation affecting both endothelial cells and monocytes. Exosomes are nanovesicles, allow communication between endothelial cells and monocytes and have been associated with diabetic complications. In this study we evaluated whether high glucose can activate monocytes and endothelial cells and whether exosomes play a role in this activation. Moreover, we studied whether endothelial cells and monocytes communicate with each other via exosomes under high and basal glncubation. In the first experiment, monomac 6 cells (MM6) were exposed to high glucose (HG; 25 mmol/L) or to exosomes from MM6 exposed to HG (exoMM6-HG) or basal glucose (5.5 mmol/L) (exoMM6-BG). In the second experiment, MM6 were exposed to exosomes from human umbilical vein endothelial cells (HUVECs) and HUVECs to exosomes from MM6. In the third experiment, MM6 and HUVECs were exposed to a mixture of exosomes from MM6 and HUVECs (exoMix). Cell activation was evaluated by measuring the protein surface expression of intracellular adhesion molecule-1 (ICAM-1) by flow cytometry. HG increased ICAM-1 expression in MM6 and monocytic exosomes from HG or BG shown similar effect in HG and BG MM6 cells. Exosomes from HUVECs increased ICAM-1 expression in MM6 cells, incubated under HG or BG, while also exosomes from MM6 increased ICAM-1 expression in HUVECs incubated under HG or BG. The combination of exosomes from both cell types (exoMixHG or exoMixBG) also increased ICAM-1 expression in both type cells in most conditions. However, the exoMixBG reversed the effect of HG in both MM6 and HUVECs. Our results show that HG activated monocytes and endothelial cells and that exosomes play a role in this HG-induced cell ICAM-1 expression. We hypothesize that during DMT2, exosomes may act as a communication mechanism between monocytes and endothelial cells, inducing and maintaining activating of both cell types in the presence of high glucose.