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Li S, Lin Y, Jones D, Walker DI, Duarte Folle A, Del Rosario I, Yu Y, Zhang K, Keener AM, Bronstein J, Ritz B, Paul KC. Untargeted serum metabolic profiling of diabetes mellitus among Parkinson's disease patients. NPJ Parkinsons Dis 2024; 10:100. [PMID: 38730245 PMCID: PMC11087477 DOI: 10.1038/s41531-024-00711-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson's disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.
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Affiliation(s)
- Shiwen Li
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Yuyuan Lin
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Dean Jones
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, USA
| | - Douglas I Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Aline Duarte Folle
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Irish Del Rosario
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Yu Yu
- Center for Health Policy Research, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Keren Zhang
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Adrienne M Keener
- Department of Neurology, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jeff Bronstein
- Department of Neurology, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Beate Ritz
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Kimberly C Paul
- Department of Neurology, David Geffen School of Medicine, Los Angeles, CA, USA.
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Abdik E, Çakır T. Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling. Sci Rep 2024; 14:585. [PMID: 38182712 PMCID: PMC10770157 DOI: 10.1038/s41598-023-51034-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.
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Affiliation(s)
- Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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Pinki F, Costello DA, Stewart G. Regional investigation of UT-B urea transporters in the rat brain. Biochem Biophys Rep 2023; 36:101563. [PMID: 37929290 PMCID: PMC10624589 DOI: 10.1016/j.bbrep.2023.101563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
Recent studies have reported increased levels of urea in the aging brain and various neurological disorders. Additionally, these diseased tissues also have increased expression of the UT-B transporter that regulates urea transport in the brain. However, little is known regarding the actual UT-B protein distribution across the brain in either normal or diseased states. This current study investigated UT-B protein abundance across three regions of the rat brain - anterior, posterior and cerebellum. Endpoint RT-PCR experiments showed that there were no regional differences in UT-B RNA expression (NS, N = 3, ANOVA), whilst Western blotting confirmed no difference in the abundance of a 35 kDa UT-B protein (NS, N = 3-4, ANOVA). In contrast, there was a significant variation in a non-UT-B 100 kDa protein (P < 0.001, N = 3-4, ANOVA), which was also detected by anti-UT-B antibodies. Using the C6 rat astrocyte cell line, Western blot analysis showed that 48-h incubation in either 5 mM or 10 mM significantly increased a 30-45 kDa UT-B protein signal (P < 0.05, N = 3, ANOVA). Furthermore, investigation of compartmentalized C6 protein samples showed the 30-45 kDa signal in the membrane fraction, whilst the 100 kDa non-UT-B signal was predominantly in the cytosolic fraction. Finally, immunolocalization studies gave surprisingly weak detection of rat UT-B, except for strong staining of red blood cells in the cerebellum. In conclusion, this study confirmed that RNA expression and protein abundance of UT-B were equal across all regions of the rat brain, suggesting that urea levels were also similar. However, it also highlighted some of the technical challenges of studying urea transporters at the protein level.
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Affiliation(s)
- Farhana Pinki
- UCD School of Biology and Environmental Science, University College Dublin, Dublin 4, Ireland
| | - Derek A Costello
- UCD School of Biomolecular and Biomedical Science, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Gavin Stewart
- UCD School of Biology and Environmental Science, University College Dublin, Dublin 4, Ireland
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Islam M, Behura SK. Role of caveolin-1 in metabolic programming of fetal brain. iScience 2023; 26:107710. [PMID: 37720105 PMCID: PMC10500482 DOI: 10.1016/j.isci.2023.107710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/10/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Mice lacking caveolin-1 (Cav1), a key protein of plasma membrane, exhibit brain aging at an early adult stage. Here, integrative analyses of metabolomics, transcriptomics, epigenetics, and single-cell data were performed to test the hypothesis that metabolic deregulation of fetal brain due to the ablation of Cav1 is linked to brain aging in these mice. The results of this study show that lack of Cav1 caused deregulation in the lipid and amino acid metabolism in the fetal brain, and genes associated with these deregulated metabolites were significantly altered in the brain upon aging. Moreover, ablation of Cav1 deregulated several metabolic genes in specific cell types of the fetal brain and impacted DNA methylation of those genes in coordination with mouse epigenetic clock. The findings of this study suggest that the aging program of brain is confounded by metabolic abnormalities in the fetal stage due to the absence of Cav1.
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Affiliation(s)
- Maliha Islam
- Division of Animal Sciences, 920 East Campus Drive, University of Missouri, Columbia, MO 65211, USA
| | - Susanta K. Behura
- Division of Animal Sciences, 920 East Campus Drive, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
- Interdisciplinary Reproduction and Health Group, University of Missouri, Columbia, MO, USA
- Interdisciplinary Neuroscience Program, University of Missouri, Columbia, MO, USA
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Philbert SA, Xu J, Scholefield M, Patassini S, Church SJ, Unwin RD, Roncaroli F, Cooper GJS. Extensive multiregional urea elevations in a case-control study of vascular dementia point toward a novel shared mechanism of disease amongst the age-related dementias. Front Mol Neurosci 2023; 16:1215637. [PMID: 37520429 PMCID: PMC10372345 DOI: 10.3389/fnmol.2023.1215637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Vascular dementia (VaD) is one of the most common causes of dementia among the elderly. Despite this, the molecular basis of VaD remains poorly characterized when compared to other age-related dementias. Pervasive cerebral elevations of urea have recently been reported in several dementias; however, a similar analysis was not yet available for VaD. Methods Here, we utilized ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to measure urea levels from seven brain regions in post-mortem tissue from cases of VaD (n = 10) and controls (n = 8/9). Brain-urea measurements from our previous investigations of several dementias were also used to generate comparisons with VaD. Results Elevated urea levels ranging from 2.2- to 2.4-fold-change in VaD cases were identified in six out of the seven regions analysed, which are similar in magnitude to those observed in uremic encephalopathy. Fold-elevation of urea was highest in the basal ganglia and hippocampus (2.4-fold-change), consistent with the observation that these regions are severely affected in VaD. Discussion Taken together, these data not only describe a multiregional elevation of brain-urea levels in VaD but also imply the existence of a common urea-mediated disease mechanism that is now known to be present in at least four of the main age-related dementias.
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Affiliation(s)
- Sasha A. Philbert
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, School of Medical Sciences, Centre for Advanced Discovery and Experimental Therapeutics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Jingshu Xu
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, School of Medical Sciences, Centre for Advanced Discovery and Experimental Therapeutics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Melissa Scholefield
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, School of Medical Sciences, Centre for Advanced Discovery and Experimental Therapeutics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Stefano Patassini
- Faculty of Science, School of Biological Sciences, The University of Auckland, Auckland, New Zealand
| | - Stephanie J. Church
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, School of Medical Sciences, Centre for Advanced Discovery and Experimental Therapeutics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Richard D. Unwin
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, School of Medical Sciences, Centre for Advanced Discovery and Experimental Therapeutics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Federico Roncaroli
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, School of Biology, Geoffrey Jefferson Brain Research Centre, The University of Manchester, Manchester, United Kingdom
| | - Garth J. S. Cooper
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, School of Medical Sciences, Centre for Advanced Discovery and Experimental Therapeutics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
- Faculty of Science, School of Biological Sciences, The University of Auckland, Auckland, New Zealand
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Kalecký K, Bottiglieri T. Targeted metabolomic analysis in Parkinson's disease brain frontal cortex and putamen with relation to cognitive impairment. NPJ Parkinsons Dis 2023; 9:84. [PMID: 37270646 DOI: 10.1038/s41531-023-00531-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 05/22/2023] [Indexed: 06/05/2023] Open
Abstract
We performed liquid chromatography tandem mass spectrometry analysis with the targeted metabolomic kit Biocrates MxP Quant 500, in human brain cortex (Brodmann area 9) and putamen, to reveal metabolic changes characteristic of Parkinson's disease (PD) and PD-related cognitive decline. This case-control study involved 101 subjects (33 PD without dementia, 32 PD with dementia (cortex only), 36 controls). We found changes associated with PD, cognitive status, levodopa levels, and disease progression. The affected pathways include neurotransmitters, bile acids, homocysteine metabolism, amino acids, TCA cycle, polyamines, β-alanine metabolism, fatty acids, acylcarnitines, ceramides, phosphatidylcholines, and several microbiome-derived metabolites. Previously reported levodopa-related homocysteine accumulation in cortex still best explains the dementia status in PD, which can be modified by dietary supplementation. Further investigation is needed to reveal the exact mechanisms behind this pathological change.
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Affiliation(s)
- Karel Kalecký
- Institute of Biomedical Studies, Baylor University, Waco, TX, 76712, USA.
- Center of Metabolomics, Institute of Metabolic Disease, Baylor Scott & White Research Institute, Dallas, TX, 75204, USA.
| | - Teodoro Bottiglieri
- Center of Metabolomics, Institute of Metabolic Disease, Baylor Scott & White Research Institute, Dallas, TX, 75204, USA
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Philbert SA, Schönberger SJ, Xu J, Church SJ, Unwin RD, Cooper GJ. Elevated hippocampal copper in cases of type 2 diabetes. EBioMedicine 2022; 86:104317. [DOI: 10.1016/j.ebiom.2022.104317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/06/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
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Zhang P, Wang S, Liu M, Li W, Li L, Zhu S, Nie Q, Li Q, Schmalz G. UPLC/Q-TOF MS-Based Urine Metabonomics Study to Identify Diffuse Axonal Injury Biomarkers in Rat. Disease Markers 2022; 2022:1-9. [PMID: 36188427 PMCID: PMC9519327 DOI: 10.1155/2022/2579489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 11/22/2022]
Abstract
Diffuse axonal injury (DAI) represents a frequent traumatic brain injury (TBI) type, significantly contributing to the dismal neurological prognosis and high mortality in TBI patients. The increase in mortality can be associated with delayed and nonspecific initial symptoms in DAI patients. Additionally, the existing approaches for diagnosis and monitoring are either low sensitivity or high cost. Therefore, novel, reliable, and objective diagnostic markers should be developed to diagnose and monitor DAI prognosis. Urine is an optimal sample to detect biomarkers for DAI noninvasively. Therefore, the DAI rat model was established in this work. Meanwhile, the ultraperformance liquid chromatography quadrupole-time-of-flight hybrid mass spectrometry- (UPLC/Q-TOF MS-) untargeted metabolomics approach was utilized to identify the features of urine metabolomics to diagnose DAI. This work included 57 metabolites with significant alterations and 21 abnormal metabolic pathways from the injury groups. Three metabolites, viz., urea, butyric acid, and taurine, were identified as possible biomarkers to diagnose DAI based on the great fold changes (FCs) and biological functions during DAI. The present study detected several novel biomarkers for noninvasively diagnosing and monitoring DAI and helped understand the DAI-associated metabolic events.
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