Comparative analysis and modeling of the severity of steatohepatitis in DDC-treated mouse strains.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. METHODOLOGY AND RESULTS: In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. CONCLUSIONS: We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other.

IL-7, IL-18, MCP-1, MIP1-ß, and OPG as biomarkers for pain treatment response in patients with cancer.

BACKGROUND: Pain is one of the most common symptoms in patients suffering from advanced cancer and receiving palliative care and is often responsible for a poor quality of life. To date, there exists no published correlation between biological, measurable biomarkers and pain intensity. OBJECTIVES: The primary objective was to search and identify pain-associated cytokines (biomarkers) correlating with changes in numeric rating scale (NRS) pain scores in patients with cancer before and after pain treatment. The secondary objectives were to assess cytokine serum level differences between patients and healthy controls and to evaluate possible relationships between pain entities, pain intensity (in NRS), gender, location of primary tumor, and the patients' cytokine baseline concentrations. STUDY DESIGN: Controlled, prospective study. SETTING: University medical center. METHODS: Eligible patients with exacerbated cancer-related pain (NRS = 5) and healthy controls with no pain were included. Serum level changes of 19 cytokines were analyzed before and during opioid treatment. RESULTS: Of 19 analyzed biomarkers, 5 (IL-7, IL-18, MCP-1, MIP-1α, MIP-1ß and OPG) turned out to correlate significantly with pain relief. In healthy controls, all analyzed cytokines showed no significant differences. In the secondary analysis, only one significant correlation was detected between OPG and pain entities. Furthermore, IL-4, IL-7, IFN-γ and OPG appeared to account for the ability to predict a patient's gender. LIMITATIONS: Our findings should be considered as preliminary and need to be confirmed in further studies. CONCLUSION: Our results provide preliminary evidence of a significant correlation of pain relief in patients with cancer and at least 5 cytokines. These biomarkers may serve as the basis for development of diagnostic tools for pain assessment and could serve as potential new targets for pain control.

Dynamic simulations on the mitochondrial fatty acid beta-oxidation network.

BACKGROUND: The oxidation of fatty acids in mitochondria plays an important role in energy metabolism and genetic disorders of this pathway may cause metabolic diseases. Enzyme deficiencies can block the metabolism at defined reactions in the mitochondrion and lead to accumulation of specific substrates causing severe clinical manifestations. Ten of the disorders directly affecting mitochondrial fatty acid oxidation have been well-defined, implicating episodic hypoketotic hypoglycemia provoked by catabolic stress, multiple organ failure, muscle weakness, or hypertrophic cardiomyopathy. Additionally, syndromes of severe maternal illness (HELLP syndrome and AFLP) have been associated with pregnancies carrying a fetus affected by fatty acid oxidation deficiencies. However, little is known about fatty acids kinetics, especially during fasting or exercise when the demand for fatty acid oxidation is increased (catabolic stress). RESULTS: A computational kinetic network of 64 reactions with 91 compounds and 301 parameters was constructed to study dynamic properties of mitochondrial fatty acid beta-oxidation. Various deficiencies of acyl-CoA dehydrogenase were simulated and verified with measured concentrations of indicative metabolites of screened newborns in Middle Europe and South Australia. The simulated accumulation of specific acyl-CoAs according to the investigated enzyme deficiencies are in agreement with experimental data and findings in literature. Investigation of the dynamic properties of the fatty acid beta-oxidation reveals that the formation of acetyl-CoA - substrate for energy production - is highly impaired within the first hours of fasting corresponding to the rapid progress to coma within 1-2 hours. LCAD deficiency exhibits the highest accumulation of fatty acids along with marked increase of these substrates during catabolic stress and the lowest production rate of acetyl-CoA. These findings might confirm gestational loss to be the explanation that no human cases of LCAD deficiency have been described. CONCLUSION: In summary, this work provides a detailed kinetic model of mitochondrial metabolism with specific focus on fatty acid beta-oxidation to simulate and predict the dynamic response of that metabolic network in the context of human disease. Our findings offer insight into the disease process (e.g. rapid progress to coma) and might confirm new explanations (no human cases of LCAD deficiency), which can hardly be obtained from experimental data alone.