Influence of plasma macronutrient levels on hepatic metabolism: role of regulatory networks in homeostasis and disease states

Somvanshi, Pramod R.; Patel, Anilkumar K.; Bhartiya, Sharad; Venkatesh, Kareenhalli V.

doi:10.1039/c5ra18128c

Cited by 9 publications

(15 citation statements)

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“…[ 43 ] developed a model focussed in detail on the GLUT proteins responsible for glucose uptake and output. Additionally, representations of glucose regulation vary from black box models, for example with the purpose of calculating optimal insulin input for insulin responsive diabetic patients [ 44 ], to mechanistic models aiming to understand the metabolic changes occurring in disease in detail [ 45 – 50 ]. Of these mechanistic models, the vast majority of existing models of the liver are single hepatic compartment models, and zonation in the context of hepatic energy metabolism has yet to be addressed.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD

2016

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In non-alcoholic fatty liver disease (NAFLD), lipid build-up and the resulting damage is known to occur more severely in pericentral cells. Due to the complexity of studying individual regions of the sinusoid, the causes of this zone specificity and its implications on treatment are largely ignored. In this study, a computational model of liver glucose and lipid metabolism is presented which treats the sinusoid as the repeating unit of the liver rather than the single hepatocyte. This allows for inclusion of zonated enzyme expression by splitting the sinusoid into periportal to pericentral compartments. By simulating insulin resistance (IR) and high intake diets leading to the development of steatosis in the model, we identify key differences between periportal and pericentral cells accounting for higher susceptibility to pericentral steatosis. Secondly, variation between individuals is seen in both susceptibility to steatosis and in its development across the sinusoid. Around 25% of obese individuals do not show excess liver fat, whilst 16% of lean individuals develop NAFLD. Furthermore, whilst pericentral cells tend to show higher lipid levels, variation is seen in the predominant location of steatosis from pericentral to pan-sinusoidal or azonal. Sensitivity analysis was used to identify the processes which have the largest effect on both total hepatic triglyceride levels and on the sinusoidal location of steatosis. As is seen in vivo, steatosis occurs when simulating IR in the model, predominantly due to increased uptake, along with an increase in de novo lipogenesis. Additionally, concentrations of glucose intermediates including glycerol-3-phosphate increased when simulating IR due to inhibited glycogen synthesis. Several differences between zones contributed to a higher susceptibility to steatosis in pericentral cells in the model simulations. Firstly, the periportal zonation of both glycogen synthase and the oxidative phosphorylation enzymes meant that the build-up of glucose intermediates was less severe in the periportal hepatocyte compartments. Secondly, the periportal zonation of the enzymes mediating β-oxidation and oxidative phosphorylation resulted in excess fats being metabolised more rapidly in the periportal hepatocyte compartments. Finally, the pericentral expression of de novo lipogenesis contributed to pericentral steatosis when additionally simulating the increase in sterol-regulatory element binding protein 1c (SREBP-1c) seen in NAFLD patients in vivo. The hepatic triglyceride concentration was predicted to be most sensitive to inter-individual variation in the activity of enzymes which, either directly or indirectly, determine the rate of free fatty acid (FFA) oxidation. The concentration was most strongly dependent on the rate constants for β-oxidation and oxidative phosphorylation. It also showed moderate sensitivity to the rate constants for processes which alter the allosteric inhibition of β-oxidation by acetyl-CoA. The predominant sinusoidal location of steatosis meanwhile was most s...

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Section: Introductionmentioning

confidence: 99%

A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD

2016

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“…However, in our current study, the inhibition of ATP citrate lyase and activation of carnitine palmitoyl transferase-I by HCA has been quantitatively analysed by a hepatic metabolic model. 25 Along with this micro level study, clinical data analysis is also reported with respect to various anthropometric and physiological variables which changed signicantly due to HCA dosage. These variables also give insights into macro level parameters related to body composition like fat mass, fat free mass with the help of empirical relations.…”

Section: Introductionmentioning

confidence: 95%

A clinical and computational study on anti-obesity effects of hydroxycitric acid

Tomar

Rao²,

Dorairaj³

et al. 2019

RSC Adv.

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“…To study the regulatory effects of the HPA axis and inflammation on metabolism, we further integrated the metabolic simulator with the models for HPA axis, inflammation and hypoxia. We adopted four sub models published in literature, namely hepatic metabolism 65 , HPA axis and inflammation model 66 , glucocorticoid receptor model 67 , and hypoxia signaling by 68 . These models were integrated by linking the regulatory nodes according to the interactions reported in the literature.…”

Section: Methodsmentioning

confidence: 99%

Mechanistic insights on metabolic dysfunction in PTSD: Role of glucocorticoid receptor sensitivity and energy deficit

Somvanshi

Mellon

Flory

et al. 2018

Preprint

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PTSD is associated with metabolic comorbidities; however it is not clear how the neuroendocrine disturbances affect metabolism. To analyze this we employed a systems biological approach using an integrated mathematical model of metabolism, HPA axis and inflammation. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD, to characterize the differences in regulatory effects. We used the pattern of fold change in metabolites representing pathway level differences as reference for metabolic control analysis (MCA) using the model. MCA revealed parameters constituting the HPA axis, inflammation and GPCR pathway that yielded metabolic dysfunction consistent with PTSD. To support this, we performed causal analysis between regulatory components and the significantly different metabolites in our sample. Causal inference revealed that the changes in glucocorticoid receptor sensitivity were mechanistically associated with metabolic dysfunction and the effects were jointly mediated by insulin resistance, inflammation, oxidative stress and energy deficit.Post-traumatic stress disorder (PTSD) is defined by a complex set of criteria including intrusive reminders, fear memories, emotional distress, hypervigilance and exaggerated startle responses that develop and persist after an exposure to trauma 1 . Multiple physiological systems at the neuronal, metabolic, inflammatory, genomic and epigenomic levels are known to be affected in PTSD 2,3 . Previous studies from our PTSD Systems Biology consortium reported associations between PTSD and insulin resistance 4 , inflammation 5 , reduced mitochondrial copy number 6 , and lower methylation of the glucocorticoid receptor (NR3C1) gene 7 in the same cohorts assessed in the present study. The data from animal models and humans with PTSD reveal association between inflammation and metabolic syndrome 8 . Analysis of metabolomics have revealed metabolic changes consistent with dysregulation in mitochondrial functioning in PTSD 9 . Another study reported differences in the mitochondrial DNA (SNPs) located in NADH dehydrogenase and ATP synthase genes in PTSD 10 . Variants of mitochondrial genes and dysregulation of their associated networks have been reported in the postmortem brains of patients with PTSD 11 .One of the major physiological regulatory axes, Hypothalamic-Pituitary-Adrenal (HPA) axis is implicated in the pathogenesis of PTSD and associated metabolic disorders 12,13 . The HPA axis relays stress signals from the brain to peripheral parts through the release of glucocorticoids (GCs) and catecholamine hormones. GCs orchestrate the activities of several physiological functions through glucocorticoid receptor (GRs) signaling. Therefore, the feedback sensitivity of GC signaling among other factors can influence downstream effects of stress exposure. GR signaling is regulated at transcriptional and epigenetic levels and is dysregulated in HPA axis associated disorders 14 . Recent studies have highlighted the asso...

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Influence of plasma macronutrient levels on hepatic metabolism: role of regulatory networks in homeostasis and disease states

Abstract: Multilevel regulations by metabolic, signaling and transcription pathways form a complex network that works to provide robust metabolic regulation in the liver. This analysis indicates that dietary perturbations in these networks can lead to insulin resistance.

Cited by 9 publications

References 128 publications

A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD

A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD

A clinical and computational study on anti-obesity effects of hydroxycitric acid

Mechanistic insights on metabolic dysfunction in PTSD: Role of glucocorticoid receptor sensitivity and energy deficit

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