Comparative Metabolomics Analysis Reveals Key Metabolic Mechanisms and Protein Biomarkers in Alzheimer’s Disease

Zhao, Dachun; Tian, Huayu; Su, Shijie; Liu, Jinman; Ma, Yinglong; Zhuo, Yue; Fang, Shuhuan; Wang, Qi; Mo, Zhizhun; Pan, Huafeng; Fang, Jiansong

doi:10.3389/fphar.2022.904857

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…It is often reported that metabolite levels change in diseased versus normal states. − This could be the result of one of two possibilities: , Metabolite levels could have changed as a consequence of the disease, or the change in metabolite levels could be a partial driver of the disease. , We suspect that, depending on the metabolite, both situations occur. However, if the change in metabolite levels is partly driving the disease (an example in cancer is found in refs and ), the question is how in reality does this contribute to cancer onset/progression?…”

Section: Discussionmentioning

confidence: 99%

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein–Protein Interactions

Skolnick

Zhou

2022

J. Phys. Chem. B

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Protein–protein interactions (PPIs) and protein–metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody–epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein–protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed “metabolic entanglement”, was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

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Section: Discussionmentioning

confidence: 99%

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein–Protein Interactions

Skolnick

Zhou

2022

J. Phys. Chem. B

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“…In recent years, metabolomics has been widely employed to characterize the metabolic alterations of diseases and provides a potential approach to explore the complex action modes of TCMs. During the past 10 years, over hundreds of metabolomics studies have been conducted to uncover the metabolic changes in multiple biological matrices including plasma, serum, cerebrospinal fluid, urine, feces, and tissues of different AD animal models and patients ( González-Domínguez et al, 2018 ; Dai et al, 2022 ). These studies demonstrated disturbances of energy-related metabolism, fatty acid metabolism, nitrogen metabolism, amino acid metabolism, and many others related to AD.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive metabolomics and lipidomics profiling uncovering neuroprotective effects of Ginkgo biloba L. leaf extract on Alzheimer’s disease

et al. 2022

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Introduction:Ginkgo biloba L. leaf extract (GBLE) has been reported to be effective for alleviating cognitive and memory impairment in Alzheimer’s disease (AD). Nevertheless, the potential mechanism remains unclear. Herein, this study aimed to explore the neuroprotective effects of GBLE on AD and elaborate the underlying therapeutic mechanism.Methods: Donepezil, the most widely prescribed drug for AD, was used as a positive control. An integrated metabolomics and lipidomics approach was adopted to characterize plasma metabolic phenotype of APP/PS1 double transgenic mice and describe the metabolomic and lipidomic fingerprint changes after GBLE intervention. The Morris water maze test and immunohistochemistry were applied to evaluate the efficacy of GBLE.Results: As a result, administration of GBLE significantly improved the cognitive function and alleviated amyloid beta (Aβ) deposition in APP/PS1 mice, showing similar effects to donepezil. Significant alterations were observed in metabolic signatures of APP/PS1 mice compared with wild type (WT) mice by metabolomic analysis. A total of 60 markedly altered differential metabolites were identified, including 28 lipid and lipid-like molecules, 13 organic acids and derivatives, 11 organic nitrogen compounds, and 8 other compounds, indicative of significant changes in lipid metabolism of AD. Further lipidomic profiling showed that the differential expressed lipid metabolites between APP/PS1 and WT mice mainly consisted of phosphatidylcholines, lysophosphatidylcholines, triglycerides, and ceramides. Taking together all the data, the plasma metabolic signature of APP/PS1 mice was primarily characterized by disrupted sphingolipid metabolism, glycerophospholipid metabolism, glycerolipid metabolism, and amino acid metabolism. Most of the disordered metabolites were ameliorated after GBLE treatment, 19 metabolites and 24 lipids of which were significantly reversely regulated (adjusted-p<0.05), which were considered as potential therapeutic targets of GBLE on AD. The response of APP/PS1 mice to GBLE was similar to that of donepezil, which significantly reversed the levels of 23 disturbed metabolites and 30 lipids.Discussion: Our data suggested that lipid metabolism was dramatically perturbed in the plasma of APP/PS1 mice, and GBLE might exert its neuroprotective effects by restoring lipid metabolic balance. This work provided a basis for better understanding the potential pathogenesis of AD and shed new light on the therapeutic mechanism of GBLE in the treatment of AD.

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“…Data obtained from AD experimental models may also be valuable in AD biomarkers studies. Interestingly, in this work, the role of disrupted levels of amino acids and their analogs, such as glycine, alanine, aspartate, glutamate, glutamine, serine, valine, tyrosine, and purine metabolite–hypoxanthine, were highlighted [ 115 ]. Nevertheless, it has to be mentioned that differences in amino acid profiles may be related to different dietary habits and, more specifically, protein intake between the compared groups.…”

Section: Alzheimer’s Diseasementioning

confidence: 99%

“…Interestingly, in the study mentioned above, Dai Z et. al., using a metabolite-target network, predicted potential protein markers in AD [ 115 ]. One may conclude that reverse metabolomics marker typing studies based on proteomic and transcriptomic data may bring new clues and directions for research on metabolites in AD, including metabolites related to impaired energy metabolism.…”

Section: Alzheimer’s Diseasementioning

confidence: 99%

Metabolomic Footprint of Disrupted Energetics and Amino Acid Metabolism in Neurodegenerative Diseases: Perspectives for Early Diagnosis and Monitoring of Therapy

et al. 2023

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The prevalence of neurodegenerative diseases (NDs) is increasing due to the aging population and improved longevity. They are characterized by a range of pathological hallmarks, including protein aggregation, mitochondrial dysfunction, and oxidative stress. The aim of this review is to summarize the alterations in brain energy and amino acid metabolism in Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). Based on our findings, we proposed a group of selected metabolites related to disturbed energy or mitochondrial metabolism as potential indicators or predictors of disease. We also discussed the hidden challenges of metabolomics studies in NDs and proposed future directions in this field. We concluded that biochemical parameters of brain energy metabolism disruption (obtained with metabolomics) may have potential application as a diagnostic tool for the diagnosis, prediction, and monitoring of the effectiveness of therapies for NDs. However, more studies are needed to determine the sensitivity of the proposed candidates. We suggested that the most valuable biomarkers for NDs studies could be groups of metabolites combined with other neuroimaging or molecular techniques. To attain clinically applicable results, the integration of metabolomics with other “omic” techniques might be required.

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Comparative Metabolomics Analysis Reveals Key Metabolic Mechanisms and Protein Biomarkers in Alzheimer’s Disease

Cited by 10 publications

References 47 publications

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein–Protein Interactions

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein–Protein Interactions

Comprehensive metabolomics and lipidomics profiling uncovering neuroprotective effects of Ginkgo biloba L. leaf extract on Alzheimer’s disease

Metabolomic Footprint of Disrupted Energetics and Amino Acid Metabolism in Neurodegenerative Diseases: Perspectives for Early Diagnosis and Monitoring of Therapy

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