Glucose metabolism pathways disorders -
The most widespread alterations were in galactitol, which was observed to be elevated in all regions except the CG, HP and PVC, with increases of up to 3. Pantothenic acid was observed to be decreased in the CB, MED, MTG and pons of PDD cases here, similar to the results found in a previous targeted analysis of pantothenic acid in PDD brains carried out by our group Pantothenic acid is essential to the formation of CoA, which is involved in almost all metabolic pathways, including the TCA cycle and glycolysis see Fig.
Decreased pantothenic acid levels may lead to insufficient levels of CoA being produced, as has been previously observed in the PD brain 8 , with potential downstream effects on the metabolic pathways in which it is involved.
The primary limitation of this study is the relatively small sample size employed, which may increase the number of type II statistical errors when investigating large numbers of metabolites.
This may have also contributed to the lack of separation observed in the PCA plots, despite a number of statistically significant results being observed in individual metabolites. The current sample size was selected based on previous experiments in which these numbers were sufficient to obtain significant case—control results in AD and HD brains 33 , 34 —many of which have been replicable in further targeted studies and investigations performed by other groups 35 , 36 , 37 , 38 , However, the results of future studies would be greatly strengthened with the use of more robust sample sizes—although this can be difficult in studies of the brain due to the limited availability of suitable tissues.
Alternatively, targeted, quantitative analyses of the analytes of interest identified here would be at less risk of such type II errors; such quantitation is not feasible when looking at high numbers of analytes simultaneously. As such, the primary purpose of the results obtained in this study is to act as a discovery dataset, highlighting analytes of interest for future, more targeted studies.
Additionally, when selecting the cohort, a deliberate distinction was made between PD with and without dementia, whereas most PD cohorts are either mixed or do not distinguish between PD cases with and without cognitive decline; this could allow for comparisons of PD and PDD in potential future studies.
The greatest strength of this study is the analysis of ten brain regions obtained from the same cohort, covering areas of the brain with varying levels of neurodegeneration in HD. As such, this study has some of the most extensive coverage of PDD brain metabolomics in the current literature. Overall, this report represents a comprehensive systematic, multi-regional, semi-targeted metabolic investigation of the PDD brain—highlighting several metabolic pathways for future targeted studies and potential therapeutic targets, including glucose and purine metabolism, among others.
The number of these alterations reflected the α-synuclein Braak stage in which these brain regions are first affected by Lewy body deposition in traditional PD Braak staging, indicating that metabolic changes may reflect the temporal spread of PD neuropathology rather than the degree of neuronal loss in PDD.
Tissues were obtained from nine brain regions, including the middle temporal gyrus MTG ; motor cortex MCX ; primary visual cortex PVC ; hippocampus HP ; anterior cingulate gyrus CG ; cerebellum, at the level of the dentate nucleus CB ; SN; pons and medulla oblongata MED.
These regions were selected in order to cover areas of the brain that are usually considered to be severely and moderately affected, as well as relatively spared in PDD; having these regions allows for the investigation of whether metabolic alterations in the PDD brain are localised or widespread, and to identify whether changes in particular metabolic pathways are localised to areas of the brain associated with different PDD symptoms e.
motor dysfunction or cognitive decline. Tissues were obtained from nine cases with neuropathologically-confirmed diagnoses of PDD and eight controls from the Miami Brain Endowment Bank, Miami, FL, USA part of the National Institute of Health NeuroBioBank network.
All patient metadata available from the NeuroBioBank, including the cause of death, comorbidities and neuropathological findings, were obtained and are presented in Supplementary Material 1 and Table 1. Board-certified neuropathologists at the Miami Brain Endowment Bank diagnosed all donor tissue.
All PDD cases were diagnosed to be the α-synucleinopathy neocortical type, consistent with the clinical phenotype of PDD. Controls did not show any neuropathological or clinical features of neurodegenerative disease or vascular pathology.
Brains were assessed according to standardised diagnostic criteria, e. All available clinical data, including medical records, autopsy reports, family interviews, etc.
Unfortunately, data regarding the severity of the cognitive decline in cases was not available from the brain bank. Untargeted metabolic analysis was carried out on the brain samples using HPLC—MS.
The system was controlled by SCIEX Analyst 1. Separation was performed by gradient chromatography at a flow rate of 0. The total cycle time was 1. Samples were given a non-classifying label at the beginning of the experiment, run in a randomised order, and run with the investigator blinded to disease status.
The acquired data were processed in MultiQuant 3. Peaks from the MS1 and MS2 data were picked and matched against a metabolite library of standards based on retention time and mass error of ±0. Data exported from MultiQuant 3. Multiple t -tests were applied to each brain region individually resulting in 55—63 t -tests per region, after the exclusion of high-blank metabolites.
For graphs, peak areas were transformed by log 10 and q values are shown. A list of all identified signals is included in Supplementary Material 2 , along with fold changes, p values and q values.
Fold changes between cases and controls were determined using non-logged peak area data. Heat maps of all metabolite fold changes in each investigated region were created in GraphPad v9.
using z -score-normalised data. PCA analyses were carried out in MetaboAnalyst, with normalisation by a median, log transformation and auto-scaling mean-centred and divided by the standard deviation of each variable for each individual region.
Correlations between significantly altered metabolites were determined using non-parametric Spearman correlation coefficients in GraphPad v. Further information on research design is available in the Nature Research Reporting Summary linked to this article. The datasets supporting the conclusions of this article are included within the article and its Supplementary Material s.
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Human tissue was obtained through the NIH Neurobiobank from the University of Miami Brain Endowment Bank. We thank both the bank and donors for the supply of these tissues. Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9NT, UK.
Melissa Scholefield, Stephanie J. Church, Richard D. Biological Mass Spectrometry Core Research Facility, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK.
School of Biological Sciences, Faculty of Science, University of Auckland, Private Bag 92 , Auckland, , New Zealand. You can also search for this author in PubMed Google Scholar.
Conceptualisation, G. and M. and G. carried out HPLC—MS of PDD tissues; writing—original draft preparation, review, and editing, M. and R. All authors have read and agreed to the published version of the manuscript. Correspondence to Melissa Scholefield. Open Access This article is licensed under a Creative Commons Attribution 4.
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Download PDF. Subjects Cognitive ageing Dementia Parkinson's disease. Results Cohort characteristics Details of individual donor samples can be found in Supplementary Material A and Table 1 , with a summary shown in Supplementary Material A and Table 2.
HPLC—MS analysis A total of 64 metabolites were identified in the samples see Supplementary Material 2 for the full list. Full size image. The official blog of Cell Signaling Technology CST where we discuss what to expect from your time at the bench, share tips, tricks, and information.
Metabolism plays a central role in many diseases including diabetes, cardiovascular disease, obesity, and other inherited and acquired metabolic disorders. Broadly, metabolic disorders occur when the body produces too much or fails to produce enough of the essential nutrients required for normal metabolism to support cellular health and function.
These conditions may result from the lack of a key enzyme or hormone, abnormal metabolic reactions, disease affecting key organs including the liver and pancreas, or nutritional deficiencies. Diabetes type I and type II are metabolic disorders characterized blood glucose levels that are consistently elevate above healthy physiological levels.
Both the lack of insulin in Type I Diabetes and insulin resistance in Type II Diabetes have profound consequences on insulin signaling and ultimately glucose metabolism. Cardiovascular Disease, which includes heart disease, heart attack, stroke, and coronary artery disease, is a leading cause of death worldwide.
Obesity is defined by an accumulation of excess of body fat as determine using the body-mass index, a calculation of body weight divided by the square of body height and scaled according to age and sex. Due to its role as a central regulator of lipid and glucose metabolism, AMPK is a potential therapeutic target for obesity, diabetes, and cancer.
In addition to diabetes and obesity, many additional inborn metabolic disorders have been identified and are the current focus of extensive research. These include lysosomal storage diseases, amino acid metabolism disorders, and carbohydrate metabolism disorders. Carbohydrate metabolism disorders are genetic conditions that affect the catabolism and anabolism of carbohydrates primarily due to the dysfunction of key enzymes required for proper carbohydrate metabolism.
Examples include glycogen storage diseases and glycogenphosphate dehydrogenase G6PD deficiency. Automated IHC ChIP ELISA Flow IF-IC IHC Western Blot Workflow mIHC. Autophagy Cancer Immunology Cancer Research Cell Biology Developmental Biology Epigenetics Immunology Immunotherapy Medicine Metabolism Neurodegeneration Neuroscience Post Translational Modification Proteomics.
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All Posts. Diabetes Diabetes type I and type II are metabolic disorders characterized blood glucose levels that are consistently elevate above healthy physiological levels.
Type II Diabetes is the result of insulin resistance, the inability of cells of the body to properly respond to insulin and is commonly caused by a combination of genetics and environmental factors.
Official websites use. gov A. gov website Diabetic meal suggestions to Diabetic meal suggestions official pathwsys organization in the United States. gov website. Share sensitive information only on official, secure websites. Metabolism is the process your body uses to make energy from the food you eat. Thank you for visiting pathhways. You are using a Speed optimization tools version disotders Glucose metabolism pathways disorders support payhways CSS. To obtain Glucosf best experience, Omega- dosage recommend Diabetic meal suggestions use a Gluclse up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. In mammals, the white adipocyte is a cell type that is specialized for storage of energy in the form of triacylglycerols and for energy mobilization as fatty acids. White adipocyte metabolism confers an essential role to adipose tissue in whole-body homeostasis.
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