The Combination of Whole Cell Lipidomics Analysis and Single Cell Confocal Imaging of Fluidity and Micropolarity Provides Insight into Stress-Induced Lipid Turnover in Subcellular Organelles of Pancreatic Beta Cells

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

Erythrocyte membrane fluidity: A novel biomarker of residual cardiovascular risk in type 2 diabetes

AIMS

Improving the composition of circulating fatty acids (FA) leads to a reduction in cardiovascular diseases (CVD) in high-risk individuals. The membrane fluidity of red blood cells (RBC), which reflects circulating FA status, may be a valid biomarker of cardiovascular (CV) risk in type 2 diabetes (T2D).

METHODS

Red blood cell membrane fluidity, quantified as general polarization (GP), was assessed in 234 subjects with T2D, 86 with prior major CVD. Based on GP distribution, a cut-off of .445 was used to divide the study cohort into two groups: the first with higher GP, called GEL, and the second, defined as lower GP (LGP). Lipidomic analysis was performed to evaluate FA composition of RBC membranes.

RESULTS

Although with comparable CV risk factors, the LGP group had a greater percentage of patients with major CVD than the GEL group (40% vs 24%, respectively, p < .05). Moreover, in a logistic regression analysis, a lower GP value was independently associated with the presence of macrovascular complications. Lipidomic analysis showed a clear shift of LGP membranes towards a pro-inflammatory condition due to higher content of arachidonic acid and increased omega 6/omega 3 index.

CONCLUSIONS

Increased membrane fluidity is associated with a higher CV risk in subjects with T2D. If confirmed in prospective studies, membrane fluidity could be a new biomarker for residual CV risk assessment in T2D.

Advancing Biomarker Discovery and Therapeutic Targets in Duchenne Muscular Dystrophy: A Comprehensive Review

Mounting evidence underscores the intricate interplay between the immune system and skeletal muscles in Duchenne muscular dystrophy (DMD), as well as during regular muscle regeneration. While immune cell infiltration into skeletal muscles stands out as a prominent feature in the disease pathophysiology, a myriad of secondary defects involving metabolic and inflammatory pathways persist, with the key players yet to be fully elucidated. Steroids, currently the sole effective therapy for delaying onset and symptom control, come with adverse side effects, limiting their widespread use. Preliminary evidence spotlighting the distinctive features of T cell profiling in DMD prompts the immuno-characterization of circulating cells. A molecular analysis of their transcriptome and secretome holds the promise of identifying a subpopulation of cells suitable as disease biomarkers. Furthermore, it provides a gateway to unraveling new pathological pathways and pinpointing potential therapeutic targets. Simultaneously, the last decade has witnessed the emergence of novel approaches. The development and equilibrium of both innate and adaptive immune systems are intricately linked to the gut microbiota. Modulating microbiota-derived metabolites could potentially exacerbate muscle damage through immune system activation. Concurrently, genome sequencing has conferred clinical utility for rare disease diagnosis since innovative methodologies have been deployed to interpret the functional consequences of genomic variations. Despite numerous genes falling short as clinical targets for MD, the exploration of Tdark genes holds promise for unearthing novel and uncharted therapeutic insights. In the quest to expedite the translation of fundamental knowledge into clinical applications, the identification of novel biomarkers and disease targets is paramount. This initiative not only advances our understanding but also paves the way for the design of innovative therapeutic strategies, contributing to enhanced care for individuals grappling with these incapacitating diseases.

Oleic Acid Exhibits Anti-Proliferative and Anti-Invasive Activities via the PTEN/AKT/mTOR Pathway in Endometrial Cancer

Reprogramming of fatty acid metabolism promotes cell growth and metastasis through a variety of processes that stimulate signaling molecules, energy storage, and membrane biosynthesis in endometrial cancer. Oleic acid is one of the most important monounsaturated fatty acids in the human body, which appears to have both pro- and anti-tumorigenic activities in various pre-clinical models. In this study, we evaluated the potential anti-tumor effects of oleic acid in endometrial cancer cells and the LKB1fl/flp53fl/fl mouse model of endometrial cancer. Oleic acid increased lipogenesis, inhibited cell proliferation, caused cell cycle G1 arrest, induced cellular stress and apoptosis, and suppressed invasion in endometrial cancer cells. Targeting of diacylglycerol acyltransferases 1 and 2 effectively increased the cytotoxicity of oleic acid. Moreover, oleic acid significantly increased the expression of wild-type PTEN, and knockdown of PTEN by shRNA partially reversed the anti-proliferative and anti-invasive effects of oleic acid. Inhibition of the AKT/mTOR pathway by ipatasertib effectively increased the anti-tumor activity of oleic acid in endometrial cancer cells. Oleic acid treatment (10 mg/kg, daily, oral) for four weeks significantly inhibited tumor growth by 52.1% in the LKB1fl/flp53fl/fl mice. Our findings demonstrated that oleic acid exhibited anti-tumorigenic activities, dependent on the PTEN/AKT/mTOR signaling pathway, in endometrial cancer.

Stearoyl-CoA desaturase 1 deficiency exacerbates palmitate-induced lipotoxicity by the formation of small lipid droplets in pancreatic β-cells

The accelerating accumulation of surplus lipids in the pancreas triggers structural and functional changes in type 2 diabetes-affected islets. Pancreatic β-cells exhibit a restricted capacity to store fat reservoirs in lipid droplets (LDs), which act as transient buffers to prevent lipotoxic stress. With the increasing incidence of obesity, growing interest has been seen in the intracellular regulation of LD metabolism for β-cell function. Stearoyl-CoA desaturase 1 (SCD1) is critical for producing unsaturated fatty acyl moieties for fluent storage into and out of LDs, likely affecting the overall rate of β-cell survival. We explored LD-associated composition and remodeling in SCD1-deprived INS-1E cells and in pancreatic islets in wildtype and SCD1^-/- mice in the lipotoxic milieu. Deficiency in the enzymatic activity of SCD1 led to decrease in the size and number of LDs and the lower accumulation of neutral lipids. This occurred in parallel with a higher compactness and lipid order inside LDs, followed by changes in the saturation status and composition of fatty acids within core lipids and the phospholipid coat. The lipidome of LDs was enriched in 18:2n-6 and 20:4n-6 in β-cells and pancreatic islets. These rearrangements markedly contributed to differences in protein association with the LD surface. Our findings highlight an unexpected molecular mechanism by which SCD1 activity affects the morphology, composition and metabolism of LDs. We demonstrate that SCD1-dependent disturbances in LD enrichment can impact proper pancreatic β-cells and islet functioning, which may have considerable therapeutic value for the management of type 2 diabetes.

Microbiota dysbiosis influences immune system and muscle pathophysiology of dystrophin-deficient mice

Duchenne muscular dystrophy (DMD) is a progressive severe muscle-wasting disease caused by mutations in DMD, encoding dystrophin, that leads to loss of muscle function with cardiac/respiratory failure and premature death. Since dystrophic muscles are sensed by infiltrating inflammatory cells and gut microbial communities can cause immune dysregulation and metabolic syndrome, we sought to investigate whether intestinal bacteria support the muscle immune response in mdx dystrophic murine model. We highlighted a strong correlation between DMD disease features and the relative abundance of Prevotella. Furthermore, the absence of gut microbes through the generation of mdx germ-free animal model, as well as modulation of the microbial community structure by antibiotic treatment, influenced muscle immunity and fibrosis. Intestinal colonization of mdx mice with eubiotic microbiota was sufficient to reduce inflammation and improve muscle pathology and function. This work identifies a potential role for the gut microbiota in the pathogenesis of DMD.

Putting the Personalized Metabolic Avatar into Production: A Comparison between Deep-Learning and Statistical Models for Weight Prediction

Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent solution for prevention and treatment of many diseases. In this context, we have already developed a data-driven model of metabolism, called a "Personalized Metabolic Avatar" (PMA), using gated recurrent unit (GRU) neural networks for weight forecasting. However, putting a digital twin into production to make it available for users is a difficult task that as important as model building. Among the principal issues, changes to data sources, models and hyperparameters introduce room for error and overfitting and can lead to abrupt variations in computational time. In this study, we selected the best strategy for deployment in terms of predictive performance and computational time. Several models, such as the Transformer model, recursive neural networks (GRUs and long short-term memory networks) and the statistical SARIMAX model were tested on ten users. PMAs based on GRUs and LSTM showed optimal and stable predictive performances, with the lowest root mean squared errors (0.38 ± 0.16-0.39 ± 0.18) and acceptable computational times of the retraining phase (12.7 ± 1.42 s-13.5 ± 3.60 s) for a production environment. While the Transformer model did not bring a substantial improvement over RNNs in term of predictive performance, it increased the computational time for both forecasting and retraining by 40%. The SARIMAX model showed the worst performance in term of predictive performance, though it had the best computational time. For all the models considered, the extent of the data source was a negligible factor, and a threshold was established for the number of time points needed for a successful prediction.

Targeted and untargeted metabolomic approach for GDM diagnosis

Gestational diabetes mellitus (GDM) is a disorder which manifests itself for the first time during pregnancy and is mainly connected with glucose metabolism. It is also known that fatty acid profile changes in erythrocyte membranes and plasma could be associated with obesity and insulin resistance. These factors can lead to the development of diabetes. In the reported study, we applied the untargeted analysis of plasma in GDM against standard glucose-tolerant (NGT) women to identify the differences in metabolomic profiles between those groups. We found higher levels of 2-hydroxybutyric and 3-hydroxybutyric acids. Both secondary metabolites are associated with impaired glucose metabolism. However, they are products of different metabolic pathways. Additionally, we applied lipidomic profiling using gas chromatography to examine the fatty acid composition of cholesteryl esters in the plasma of GDM patients. Among the 14 measured fatty acids characterizing the representative plasma lipidomic cluster, myristic, oleic, arachidonic, and α-linoleic acids revealed statistically significant changes. Concentrations of both myristic acid, one of the saturated fatty acids (SFAs), and oleic acid, which belong to monounsaturated fatty acids (MUFAs), tend to decrease in GDM patients. In the case of polyunsaturated fatty acids (PUFAs), some of them tend to increase (e.g., arachidonic), and some of them tend to decrease (e.g., α-linolenic). Based on our results, we postulate the importance of hydroxybutyric acid derivatives, cholesteryl ester composition, and the oleic acid diminution in the pathophysiology of GDM. There are some evidence suggests that the oleic acid can have the protective role in diabetes onset. However, metabolic alterations that lead to the onset of GDM are complex; therefore, further studies are needed to confirm our observations.

Molecular response of Anoxybacillus sp. PDR2 under azo dye stress: An integrated analysis of proteomics and metabolomics

Treating azo dye wastewater using thermophilic bacteria is considered a more efficient bioremediation strategy. In this study, a thermophilic bacterial strain, Anoxybacillus sp. PDR2, was regarded as the research target. This strain was characterized at different stages of azo dye degradation by using TMT quantitative proteomic and non-targeted metabolome technology. A total of 165 differentially expressed proteins (DEPs) and 439 differentially metabolites (DMs) were detected in comparisons between bacteria with and without azo dye. It was found that Anoxybacillus sp. PDR2 can degrade azo dye Direct Black G (DBG) through extracellular electron transfer with glucose serving as electron donors. Most proteins related to carbohydrate metabolism, including acetoacetate synthase, and malate synthase G, were overexpressed to provide energy. The bacterium can also self-synthesize riboflavin as a redox mediator of in vitro electron transport. These results lay a theoretical basis for industrial bioremediation of azo dye wastewater.

Personalized Metabolic Avatar: A Data Driven Model of Metabolism for Weight Variation Forecasting and Diet Plan Evaluation

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.

Study of the influence of hyperglycemia on the abundance of amino acids, fatty acids, and selected lipids in extracellular vesicles using TOF-SIMS

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) with the Bi₃⁺ liquid metal ion gun was used to investigate the content of lipids and amino acids (AAs) in extracellular vesicles (EVs). We induced metabolic changes in human pancreatic β-cells by stimulation with high glucose concentrations (35 mM) and tested the hypothesis of hyperglycemia (HG) has a detrimental effect on lipids and AAs in released EV subpopulations: ectosomes and exosomes. As a result of HG treatment, selected fatty acids (FAs) such as arachidonic, myristic and palmitic acids, changed their abundance in ectosomes and exosomes. Also, intensities of the characteristic peaks for cholesterol (m/z 95.09; 147.07; 161.11; 369.45) along with the molecular ion m/z 386.37 [C₂₇H₄₆O⁺] under HG conditions, both for ectosomes and exosomes, have changed significantly. Comparative analysis of HG EVs and normoglycemic (NG) ones showed statistically significant differences in the signal intensities of four AAs: valine (m/z 72.08 and 83.05), isoleucine (m/z 86.10), phenylalanine (m/z 120.08 and 132.05) and tyrosine (m/z 107.05 and 136.09). We confirmed that ToF-SIMS is a useful technique to study selected AAs and lipid profiles in various EV subpopulations. Our study is the first demonstration of changes in FAs and AAs in exosomes and ectosomes derived from β-cells under the influence of HG.

Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data

Self-monitoring of weight, diet and physical activity is a valuable component of behavioral weight loss treatment. The validation and user-friendliness of this approach is not optimal since users are selected from homogeneous pools and rely on different applications, increasing the burden and achieving partial, generic and/or unrelated information about their metabolic state. Moreover, studies establishing type, time, duration, and adherence criteria for self-monitoring are lacking. In this study, we developed a digital web-based application (ArmOnIA), which integrates dietary, anthropometric, and physical activity data and provides a personalized estimation of energy balance. Moreover, we determined type, time, duration, and adherence criteria for self-monitoring to achieve significant weight loss in a highly heterogeneous group. A single-arm, uncontrolled prospective study on self-monitored voluntary adults for 7 months was performed. Hierarchical clustering of adherence parameters yielded three behavioral approaches: high (HA), low (LA), and medium (MA) adherence. Average BMI decrease is statistically significant between LA and HA. Moreover, we defined thresholds for the minimum frequencies and duration of dietary and weight self-monitoring. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim of achieving long-lasting results when pursuing a healthy lifestyle.

Biomimetic Radical Chemistry and Applications

Some of the most interesting aspects of free radical chemistry that emerged in the last two decades are radical enzyme mechanisms, cell signaling cascades, antioxidant activities, and free radical-induced damage of biomolecules. In addition, identification of modified biomolecules opened the way for the evaluation of in vivo damage through biomarkers. When studying free radical-based chemical mechanisms, it is very important to establish biomimetic models, which allow the experiments to be performed in a simplified environment, but suitably designed to be in strict connection with cellular conditions. The 28 papers (11 reviews and 17 articles) published in the two Special Issues of Molecules on "Biomimetic Radical Chemistry and Applications (2019 and 2021)" show a remarkable range of research in this area. The biomimetic approach is presented with new insights and reviews of the current knowledge in the field of radical-based processes relevant to health, such as biomolecular damages and repair, signaling and biomarkers, biotechnological applications, and novel synthetic approaches.