Role of the metabolism of branched-chain amino acids in the development of Alzheimer's disease and other metabolic disorders

Polis, Baruh; Samson, Abraham O.

doi:10.4103/1673-5374.274328

Cited by 90 publications

(74 citation statements)

References 112 publications

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“…Suppressed glycolysis and dysfunctional TCA cycle that may lead to increased glucose and other intermediate metabolites, and elevated glutathione in response to reactive oxygen species, have been reported in AD (Atamna and Frey II 2007; Mandal et al 2019; Le Douce et al 2020). On the other hand, molecules involve in DNA synthesis and valine/leucine/isoleucine metabolism are most depleted in the AD neuron cells, which are consistent to the recently reported observations of suppressed DNA synthesis and valine metabolism in AD (Yurov et al 2011; Polis and Samson 2020). More interestingly, we predicted aspartate and metabolites involved in glycosaminoglycan synthesis are greatly depleted in the AD neuron cells.…”

Section: Resultssupporting

confidence: 91%

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

Alghamdi

Chang

Dang

et al. 2020

Preprint

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The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. Our understanding of the intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations is unfortunately quite limited, without a mature single cell metabolomics technology. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reorganized human metabolic map as focused metabolic modules, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolites in the matched metabolomics data. We also applied scFEA on publicly available single cell melanoma and head and neck cancer datasets, and discovered different metabolic landscapes between cancer and stromal cells. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

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Section: Resultssupporting

confidence: 91%

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

Alghamdi

Chang

Dang

et al. 2020

Preprint

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“…The total consumption of L-glutamine and L-methionine is a fingerprint of immortalized replicating cells. The decreasing trend for L-histidine, L-tyrosine, L-phenylalanine, and L-tryptophane concentrations is compatible with the employment of these amino acids as a precursor of L-glutamic acid, which is the principal excitatory neurotransmitter of the mammalian brain (Polis and Samson, 2020), dopamine, a monoamine neurotransmitter involved in the stability of hippocampaldependent memory (McNamara et al, 2014), and several neuroactive compounds such as 5-hydroxytryptamine, kynurenines, and melatonin, respectively (Ruddick et al, 2006;Höglund et al, 2019). Dopamine concentration increases in the CCM after neuron culturing reaches indeed 3.8 μM.…”

Section: Discussionmentioning

confidence: 66%

“…The detection of SCFAs at millimolar and submillimolar concentration levels in the CCM after 4 days of HN9.10e cell line culturing is interesting and intriguing (formic acid, 0.337 ± 0.06; acetic acid, 6.1 ± 0.82 mM; propionic acid, 0.936 ± 0.127; isobutyric acid, 0.909 ± 0.092; and butyric acid, 0.600 ± 0.158 mM). SCFAs can be derived from the mitochondrial metabolism of L-valine, L-leucine, and L-isoleucine amino acids (de Koning et al, 2007;Polis and Samson, 2020). Their intermediate metabolites can serve as substrates in various vital biological processes, such as cholesterol, fatty acid synthesis, and Kreb's cycle (Freeman and Learning, 2017).…”

Section: Discussionmentioning

confidence: 99%

Unraveling the Extracellular Metabolism of Immortalized Hippocampal Neurons Under Normal Growth Conditions

et al. 2021

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Metabolomic profiling of cell lines has shown many potential applications and advantages compared to animal models and human subjects, and an accurate cellular metabolite analysis is critical to understanding both the intracellular and extracellular environments in cell culture. This study provides a fast protocol to investigate in vitro metabolites of immortalized hippocampal neurons HN9.10e with minimal perturbation of the cell system using a targeted approach. HN9.10e neurons represent a reliable model of one of the most vulnerable regions of the central nervous system. Here, the assessment of their extracellular metabolic profile was performed by studying the cell culture medium before and after cell growth under standard conditions. The targeted analysis was performed by a direct, easy, high-throughput reversed-phase liquid chromatography with diode array detector (RP-HPLC-DAD) method and by headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS) for the study of volatile organic compounds (VOCs). The analysis of six different batches of cells has allowed to investigate the metabolic reproducibility of neuronal cells and to describe the metabolic “starting” conditions that are mandatory for a well-grounded interpretation of the results of any following cellular treatment. An accurate study of the metabolic profile of the HN9.10e cell line has never been performed before, and it could represent a quality parameter before any other targeting assay or further exploration.

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“…Glutamate and glutamine are the most abundant amino acids in the human brain [ 129 ]. Glutamate is produced from the transamination of α-ketoglutarate where branched-chain amino acids likely serve as the amine donor resulting in the formation of their respective α-keto acids, which could be further catabolized to yield propionyl-CoA, succinyl-CoA, and acetyl-CoA [ 130 ]. Glutamine is produced from glutamate by the activity of glutamine synthetase (GS) [ 124 , 131 ].…”

Section: Implications Of Mitochondrial Oxidative and Nitrosative Smentioning

confidence: 99%

Mitochondrial Oxidative and Nitrosative Stress and Alzheimer Disease

Butterfield

Boyd‐Kimball

2020

Antioxidants

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Oxidative and nitrosative stress are widely recognized as critical factors in the pathogenesis and progression of Alzheimer disease (AD) and its earlier stage, amnestic mild cognitive impairment (MCI). A major source of free radicals that lead to oxidative and nitrosative damage is mitochondria. This review paper discusses oxidative and nitrosative stress and markers thereof in the brain, along with redox proteomics, which are techniques that have been pioneered in the Butterfield laboratory. Selected biological alterations in—and oxidative and nitrosative modifications of—mitochondria in AD and MCI and systems of relevance thereof also are presented. The review article concludes with a section on the implications of mitochondrial oxidative and nitrosative stress in MCI and AD with respect to imaging studies in and targeted therapies toward these disorders. Taken together, this review provides support for the notion that brain mitochondrial alterations in AD and MCI are key components of oxidative and nitrosative stress observed in these two disorders, and as such, they provide potentially promising therapeutic targets to slow—and hopefully one day stop—the progression of AD, which is a devastating dementing disorder.

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Role of the metabolism of branched-chain amino acids in the development of Alzheimer's disease and other metabolic disorders

Cited by 90 publications

References 112 publications

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

Unraveling the Extracellular Metabolism of Immortalized Hippocampal Neurons Under Normal Growth Conditions

Mitochondrial Oxidative and Nitrosative Stress and Alzheimer Disease

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