In vivo and in silico dynamics of the development of Metabolic Syndrome

Single high-dose intravenous injection of Wharton's jelly-derived mesenchymal stem cell exerts protective effects in a rat model of metabolic syndrome

BACKGROUND

Metabolic syndrome (MetS) is a significant epidemiological problem worldwide. It is a pre-morbid, chronic and low-grade inflammatory disorder that precedes many chronic diseases. Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) could be used to treat MetS because they express high regenerative capacity, strong immunomodulatory properties and allogeneic biocompatibility. This study aims to investigate WJ-MSCs as a therapy against MetS in a rat model.

METHODS

Twenty-four animals were fed with high-fat high-fructose (HFHF) diet ad libitum. After 16 weeks, the animals were randomised into treatment groups (n = 8/group) and received a single intravenous administration of vehicle, that is, 3 × 106 cells/kg or 10 × 106 cells/kg of WJ-MSCs. A healthy animal group (n = 6) fed with a normal diet received the same vehicle as the control (CTRL). All animals were periodically assessed (every 4 weeks) for physical measurements, serum biochemistry, glucose tolerance test, cardiovascular function test and whole-body composition. Post-euthanasia, organs were weighed and processed for histopathology. Serum was collected for C-reactive protein and inflammatory cytokine assay.

RESULTS

The results between HFHF-treated groups and healthy or HFHF-CTRL did not achieve statistical significance (α = 0.05). The effects of WJ-MSCs were masked by the manifestation of different disease subclusters and continuous supplementation of HFHF diet. Based on secondary analysis, WJ-MSCs had major implications in improving cardiopulmonary morbidities. The lungs, liver and heart show significantly better histopathology in the WJ-MSC-treated groups than in the untreated CTRL group. The cells produced a dose-dependent effect (high dose lasted until week 8) in preventing further metabolic decay in MetS animals.

CONCLUSIONS

The establishment of safety and therapeutic proof-of-concept encourages further studies by improving the current therapeutic model.

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.

METHODS

Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.

RESULTS

Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.

DISCUSSION

AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.

HIGHLIGHTS

There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Bariatric surgery improves postprandial VLDL kinetics and restores insulin-mediated regulation of hepatic VLDL production

Dyslipidemia in obesity results from excessive production and impaired clearance of triglyceride-rich (TG-rich) lipoproteins, which are particularly pronounced in the postprandial state. Here, we investigated the impact of Roux-en-Y gastric bypass (RYGB) surgery on postprandial VLDL1 and VLDL2 apoB and TG kinetics and their relationship with insulin-responsiveness indices. Morbidly obese patients without diabetes who were scheduled for RYGB surgery (n = 24) underwent a lipoprotein kinetics study during a mixed-meal test and a hyperinsulinemic-euglycemic clamp study before the surgery and 1 year later. A physiologically based computational model was developed to investigate the impact of RYGB surgery and plasma insulin on postprandial VLDL kinetics. After the surgery, VLDL1 apoB and TG production rates were significantly decreased, whereas VLDL2 apoB and TG production rates remained unchanged. The TG catabolic rate was increased in both VLDL1 and VLDL2 fractions, but only the VLDL2 apoB catabolic rate tended to increase. Furthermore, postsurgery VLDL1 apoB and TG production rates, but not those of VLDL2, were positively correlated with insulin resistance. Insulin-mediated stimulation of peripheral lipoprotein lipolysis was also improved after the surgery. In summary, RYGB resulted in reduced hepatic VLDL1 production that correlated with reduced insulin resistance, elevated VLDL2 clearance, and improved insulin sensitivity in lipoprotein lipolysis pathways.

A Systems Analysis of Phenotype Heterogeneity in APOE*3Leiden.CETP Mice Induced by Long-Term High-Fat High-Cholesterol Diet Feeding

Within the human population, considerable variability exists between individuals in their susceptibility to develop obesity and dyslipidemia. In humans, this is thought to be caused by both genetic and environmental variation. APOE*3-Leiden.CETP mice, as part of an inbred mouse model in which mice develop the metabolic syndrome upon being fed a high-fat high-cholesterol diet, show large inter-individual variation in the parameters of the metabolic syndrome, despite a lack of genetic and environmental variation. In the present study, we set out to resolve what mechanisms could underlie this variation. We used measurements of glucose and lipid metabolism from a six-month longitudinal study on the development of the metabolic syndrome. Mice were classified as mice with either high plasma triglyceride (responders) or low plasma triglyceride (non-responders) at the baseline. Subsequently, we fitted the data to a dynamic computational model of whole-body glucose and lipid metabolism (MINGLeD) by making use of a hybrid modelling method called Adaptations in Parameter Trajectories (ADAPT). ADAPT integrates longitudinal data, and predicts how the parameters of the model must change through time in order to comply with the data and model constraints. To explain the phenotypic variation in plasma triglycerides, the ADAPT analysis suggested a decreased cholesterol absorption, higher energy expenditure and increased fecal fatty acid excretion in non-responders. While decreased cholesterol absorption and higher energy expenditure could not be confirmed, the experimental validation demonstrated that the non-responders were indeed characterized by increased fecal fatty acid excretion. Furthermore, the amount of fatty acids excreted strongly correlated with bile acid excretion, in particular deoxycholate. Since bile acids play an important role in the solubilization of lipids in the intestine, these results suggest that variation in bile acid homeostasis may in part drive the phenotypic variation in the APOE*3-Leiden.CETP mice.

A hierarchical dynamic model used for investigating feed efficiency and its relationship with hepatic gene expression in APOE*3-Leiden.CETP mice

BACKGROUND

Feed efficiency (FE) is an important trait for livestock and humans. While the livestock industry focuses on increasing FE, in the current obesogenic society it is more of interest to decrease FE. Hence, understanding mechanisms involved in the regulation of FE and particularly how it can be decreased would help tremendously in counteracting the obesity pandemic. However, it is difficult to accurately measure or calculate FE in humans. In this study, we aimed to address this challenge by developing a hierarchical dynamic model based on humanized mouse data.

METHODS

We analyzed existing experimental data derived from 105 APOE*3-Leiden.CETP (E3L.CETP) mice fed a high-fat high-cholesterol (HFHC) diet for 1 (N = 20), 2 (N = 19), 3 (N = 20), and 6 (N = 46) month. We developed an ordinary differential equation (ODE) based model to estimate the FE based on the longitudinal data of body weight and food intake. Since the liver plays an important role in maintaining metabolic homeostasis, we evaluated associations between FE and hepatic gene expression levels. Depending on the feeding duration, we observed different relationships between FE and hepatic gene expression levels.

RESULTS

After 1-month feeding of HFHC diet, we observed that FE was associated with vitamin A metabolism, arachidonic acid metabolism, and the PPAR signaling pathway. After 3- and 6-month feeding of HFHC diet, we observed that FE was associated most strongly with expression levels of Spink1 and H19, genes involved in cell proliferation and glucose metabolism, respectively.

CONCLUSIONS

In conclusion, our analysis suggests that various biological processes such as vitamin A metabolism, hepatic response to inflammation, and cell proliferation associate with FE at different stages of diet-induced obesity.

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis

Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.

Ameliorative activity of Adansonia digitata fruit on high sugar/high fat diet-simulated Metabolic Syndrome model in male Wistar rats

Metabolic syndrome is a complex of metabolic disorders characterized by oxidative stress which compromises cell functions and entails multiple organs pathologies. We investigated the therapeutic and protective potential of Adansonia digitata fruit -a potent antioxidant- in high sugar/high fat diet-simulated metabolic syndrome in Wistar rats. 42 male rats (140-200 g) were randomly divided into 7 groups. G1 was kept on standard laboratory diet (SLD) for all 9 weeks (negative control). 5 groups were fed high Sugar/high fat diet for 6 weeks then switched to SLD for another 3 weeks + oral treatment as follows: G2+ no treatment (positive control), G3-G5 + 200, 400 and 800 mg/kg/day aqueous A. digitata fruit respectively, G6 + 10 mg/kg/day Simvastatin. G7 + HS/HFD + 400 mg/kg/day A. digitata fruit simultaneously and was terminated at W6. Our results showed that G2-G6 develops dyslipidemia, hyperglycaemia, weight gain, elevated hepatic biomarkers, elevated creatinine and urea plus pathological derangements in the heart, liver and kidney tissues compared to negative control at W6. 200 mg/kg/day A. digitata fruit significantly ameliorated the induced dyslipidemia (P ≤ 0.001), hyperglycaemia (P ≤ 0.001) with a significant reduction in the Atherogenic Index of Plasma (P ≤ 0.000) after 3 weeks treatment. The fruit normalized the elevated hepatic biomarkers as well as creatinine and urea. A dose dependent partial reduction in lesion intensity was observed in the hepatic tissue while the heart and kidney showed mostly reversed to normal histology. The inflammatory infiltration was eliminated. Relevant results were observed for the two higher doses. The simultaneous treatment showed significant lower levels in all biomarkers investigated compared to positive control which could be interpreted as protective activity. A reduction of 4-11% in whole body weight was achieved. CONCLUSION: MetS was successfully simulated with a HS/HFD formula in male Wistar rats. Treatment with aqueous A. digitata fruit showed anti-Metabolic Syndrome potential reflected by weight loss, anti-inflammatory, hypolipidemic, hypoglycaemic, renal, hepatic and cardio-protective activities.

Systems engineering the organ preservation process for transplantation

Improving organ preservation and extending the preservation time would have game-changing effects on the current practice of organ transplantation. Machine perfusion has emerged as an improved preservation technology to expand the donor pool, assess graft viability and ensure adequate graft function. However, its efficacy in extending the preservation time is limited. Subzero organ preservation does hold the promise to significantly extend the preservation time and recent advances in cryobiology bring it closer to clinical translation. In this review, we aim to broaden the perspective in the field from a focus on these individual technologies to that of a systems engineering. This would enable the creation of a preservation process that integrates the benefits of machine perfusion with those of subzero preservation, with the ultimate goal to provide on demand availability of donor organs through organ banking.

Diet-Induced Rabbit Models for the Study of Metabolic Syndrome

Obesity and metabolic syndrome (MetS) have become a growing problem for public health and clinical practice, given their increased prevalence due to the rise of sedentary lifestyles and excessive caloric intake from processed food rich in fat and sugar. There are several definitions of MetS, but most of them describe it as a cluster of cardiovascular and metabolic alterations such as abdominal obesity, reduced high-density lipoprotein (HDL) and elevated low-density lipoprotein (LDL) cholesterol, elevated triglycerides, glucose intolerance, and hypertension. Diagnosis requires three out of these five criteria to be present. Despite the increasing prevalence of MetS, the understanding of its pathophysiology and relationship with disease is still limited. Indeed, the pathological consequences of MetS components have been reported individually, but investigations that have studied the effect of the combination of MeS components on organ pathological remodeling are almost nonexistent. On the other hand, animal models are a powerful tool in understanding the mechanisms that underlie pathological processes such as MetS. In the first part of the review, we will briefly overview the advantages, disadvantages and pathological manifestations of MetS in porcine, canine, rodent, and rabbit diet-induced experimental models. Then, we will focus on the different dietary regimes that have been used in rabbits to induce MetS by means of high-fat, cholesterol, sucrose or fructose-enriched diets and their effects on physiological systems and organ remodeling. Finally, we will discuss the use of dietary regimes in different transgenic strains and special rabbit breeds.

Network Medicine in Pathobiology

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

Computational modelling of energy balance in individuals with Metabolic Syndrome

Background

A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations.

Results

We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified.

In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment.

Conclusion

The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure – for instance by cold exposure to activate brown adipose tissue (BAT) – could resolve MetS symptoms.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0705-z) contains supplementary material, which is available to authorized users.