Project description:Mammals display wide range of variation in their lifespan. Investigating the molecular networks that distinguish long- from short-lived species has proven useful to identify determinants of longevity. Here, we compared the liver of long-lived naked mole-rats (NMRs) and the phylogenetically closely related, shorter-lived, guinea pigs using an integrated omic approach. We found that NMRs livers display a unique expression pattern of mitochondrial proteins that result in distinct metabolic features of their mitochondria. For instance, we observed a generally reduced respiration rate associated with lower protein levels of respiratory chain components, particularly complex I, and increased capacity to utilize fatty acids. Interestingly, we show that the same molecular networks are affected during aging in both NMR and humans, supporting a direct link to the extraordinary longevity of both species. Finally, we identified a novel longevity pathway and validated it experimentally in the nematode C. elegans.
Project description:The naked mole-rat (NMR), Heterocephalus glaber, is a mouse-sized subterranean rodent native to East Africa. Research on NMRs is intensifying in an effort to gain leverage from their unusual physiology, long-life span and cancer resistance for the development of new theraputics. Few studies have attempted to explain the reasons behind the NMR’s cancer resistance, but most prominently Tian et al. reported that NMR cells produce high-molecular weight hyaluronan as a potential cause for the NMR’s cancer resistance. Tian et al. have shown that NMR cells are resistant to transformation by SV40 Large T Antigen (SV40LT) and oncogenic HRAS (HRASG12V), a combination of oncogenes sufficient to transform mouse and rat fibroblasts. We have developed a number of lentiviral vectors to deliver both these oncogenes and generated 106 different cell lines from five different tissues and eleven different NMRs, and report here that contrary to Tian et al.’s observation, NMR cells are susceptible to oncogenic transformation by SV40LT and HRASG12V. Our data thus point to a non-cell autonomous mechanism underlying the remarkable cancer resistance of NMRs. Identifying these non-cell autonomous mechanisms could have significant implications on our understanding of human cancer development.
Project description:The detachment of epithelial cells, but not cancer cells, causes anoikis due to reduced energy production. Invasive tumor cells generate three splice variants of the metastasis gene osteopontin. The cancer-specific form osteopontin-c supports anchorage-independence through inducing oxidoreductases and upregulating intermediates/enzymes in the hexose monophosphate shunt, glutathione cycle, glycolysis, glycerol phosphate shuttle, and mitochondrial respiratory chain. Osteopontin-c signaling upregulates glutathione (consistent with the induction of the enzyme GPX-4), glutamine and glutamate (which can feed into the tricarboxylic acid cycle). Consecutively, the cellular ATP levels are elevated. The elevated creatine may be synthesized from serine via glycine and also supports the energy metabolism by increasing the formation of ATP. Metabolic probing with N-acetyl-L-cysteine, L-glutamate, or glycerol identified differentially regulated pathway components, with mitochondrial activity being redox dependent and the creatine pathway depending on glutamine. The effects are consistent with a stimulation of the energy metabolism that supports anti-anoikis. Our findings imply a synergism in cancer cells between osteopontin-a, which increases the cellular glucose levels, and osteopontin-c, which utilizes this glucose to generate energy. mRNA profiles of MCF-7 cells transfected with osteopontin-a, osteopontin-c and vector control were generated by RNA-Seq, in triplicate, by Illumina HiSeq.
Project description:MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow. We applied this workflow to study heart metabolism by first comparing two different methods of heart removal: the Langendorff heart method (reverse aortic perfusion) and in situ freezing of mouse heart with a modified tissue freeze-clamp approach. We then used the in situ freezing method to study the effects of acute β-adrenergic receptor stimulation (through isoproterenol (ISO) treatment) on heart metabolism. Using our workflow and within minutes, ISO reduced the levels of metabolites involved in glycogen metabolism, glycolysis, and the Krebs cycle, but the levels of pentose phosphate pathway metabolites and of many free amino acids remained unchanged. This observation was coupled to a 6-fold increase in phosphorylated adenosine nucleotide abundance. These results support the notion that ISO acutely accelerates oxidative metabolism of glucose to meet the ATP demand required to support increased heart rate and cardiac output. In summary, our MS-based metabolomics workflow enables improved quantification of cardiac metabolites and may also be compatible with other methods such as LC or capillary electrophoresis.
Project description:IntroductionIt is increasingly common to simultaneously determine a large number of metabolites in order to assess the metabolic state of, or clarify biochemical pathways in, an organism ("metabolomics"). This approach is increasingly used in the investigation of the development of heart failure. Recently, the first reports with respect to a metabolomic approach for the assessment of patients with complex congenital heart disease have been published. Classical statistical analysis of such data is challenging.ObjectiveThis study aims to present an alternative to classical statistics with respect to identifying relevant metabolites in a classification task and numerically estimating their relative impact.MethodsData from two metabolomic studies on 20 patients with complex congenital heart disease and Fontan circulation and 20 controls were reanalysed using random forest (RF) methodology. Results were compared to those of classical statistics.ResultsRF analysis required no elaborate data pre-processing. The ranking of the variables with respect to classification impact (subject diseased, or not) was remarkably similar irrespective of the evaluation method used, leading to identical clinical interpretation.ConclusionIn metabolomic classification in adult patients with complex congenital heart disease, RF analysis as a one-step method delivers the most adequate results with minimum effort. RF may serve as an adjunct to traditional statistics also in this small but crucial-to-monitor patient group.
Project description:BackgroundHeart failure (HF) is characterized by reduced ventricular filling or ejection function due to organic or non-organic cardiovascular diseases. Danhong injection (DHI) is a medicinal material used clinically to treat HF for many years in China. Although prior research has shown that Danhong injection can improve cardiac function and structure, the biological mechanism has yet to be determined.MethodsSerum metabolic analysis was conducted via ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QE/MS) to explore underlying protective mechanisms of DHI in the transverse aortic constriction (TAC)-induced heart failure. Multivariate statistical techniques were used in the research, such as unsupervised principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). MetaboAnalyst and Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed to pinpoint pertinent metabolic pathways.ResultsAfter DHI treatment, cardiac morphology and function as well as the metabolism in model rats were improved. We identified 17 differential metabolites and six metabolic pathways. Two biomarkers, PC(18:3(6Z,9Z,12Z)/24:0) and L-Phenylalanine, were identified for the first time as strong indicators for the significant effect of DHI.ConclusionThis study revealed that DHI could regulate potential biomarkers and correlated metabolic pathway, which highlighted therapeutic potential of DHI in managing HF.
Project description:BackgroundHeart failure (HF) is a leading cause of morbidity and mortality worldwide. Metabolomics may help refine risk assessment and potentially guide HF management, but dedicated studies are few. This study aims at stratifying the long-term risk of death in a cohort of patients affected by HF due to dilated cardiomyopathy (DCM) using serum metabolomics via nuclear magnetic resonance (NMR) spectroscopy.MethodsA cohort of 106 patients with HF due to DCM, diagnosed and monitored between 1982 and 2011, were consecutively enrolled between 2010 and 2012, and a serum sample was collected from each participant. Each patient underwent half-yearly clinical assessments, and survival status at the last follow-up visit in 2019 was recorded. The NMR serum metabolomic profiles were retrospectively analyzed to evaluate the patient's risk of death. Overall, 26 patients died during the 8-years of the study.ResultsThe metabolomic fingerprint at enrollment was powerful in discriminating patients who died (HR 5.71, p = 0.00002), even when adjusted for potential covariates. The outcome prediction of metabolomics surpassed that of N-terminal pro b-type natriuretic peptide (NT-proBNP) (HR 2.97, p = 0.005). Metabolomic fingerprinting was able to sub-stratify the risk of death in patients with both preserved/mid-range and reduced ejection fraction [hazard ratio (HR) 3.46, p = 0.03; HR 6.01, p = 0.004, respectively]. Metabolomics and left ventricular ejection fraction (LVEF), combined in a score, proved to be synergistic in predicting survival (HR 8.09, p = 0.0000004).ConclusionsMetabolomic analysis via NMR enables fast and reproducible characterization of the serum metabolic fingerprint associated with poor prognosis in the HF setting. Our data suggest the importance of integrating several risk parameters to early identify HF patients at high-risk of poor outcomes.