Phenylalanine Metabolism Is Dysregulated in Human Hippocampus with Alzheimer’s Disease Related Pathological Changes

Liu, Pan; Yang, Qian; Ning, Yu; Cao, Yanran; Wang, Xue; Wang, Zhao; Qiu, Wenying; Ma, Chao

doi:10.3233/jad-210461

Cited by 34 publications

(26 citation statements)

References 60 publications

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“…In the brain, CAT and GSH-px were both negatively associated with phenylpyruvic acid ( p < 0.05). Increase in phenylpyruvic acid level could lead to nerve injury nerve and DNA damage in glial cells, as previously observed in Alzheimer’s patients ( Liu et al, 2021 ). In diabetic patients, the high level of phenylpyruvic acid was also observed owing to the impairment of phenylalanine metabolism ( Men et al, 2017 ).…”

Section: Resultssupporting

confidence: 64%

Metabolomics and biochemical insights on the regulation of aging-related diabetes by a low-molecular-weight polysaccharide from green microalga Chlorella pyrenoidosa

et al. 2022

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Section: Resultssupporting

confidence: 64%

Metabolomics and biochemical insights on the regulation of aging-related diabetes by a low-molecular-weight polysaccharide from green microalga Chlorella pyrenoidosa

et al. 2022

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“…Under chronic inflammation, the rise of serum phenylalanine concentration and phenylalanine/tyrosine ratio (Phe/Tyr) may be related to neuropsychiatric symptoms (Wissmann et al, 2013 ). Immune activation in patients with AD is related to high serum phenylalanine concentration, and the disorder of phenylalanine metabolism in the hippocampus may be an essential mechanism of AD pathology (Liu et al, 2021 ). L-arginine (L-Arg) is the precursor of nitric oxide and polyamines.…”

Section: Discussionmentioning

confidence: 99%

Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Coptidis Rhizoma for the Treatment of Alzheimer's Disease

Wang

Cheng

et al. 2022

Front. Aging Neurosci.

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BackgroundAlzheimer's disease (AD) is becoming a more prevalent public health issue in today's culture. The experimental study of Coptidis Rhizoma (CR) and its chemical components in AD treatment has been widely reported, but the principle of multi-level and multi-mechanism treatment of AD urgently needs to be clarified.ObjectiveThis study focuses on network pharmacology to clarify the mechanism of CR's multi-target impact on Alzheimer's disease.MethodsThe Phytochemical-compounds of CR have been accessed from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Symmap database or HPLC determination. The values of Oral Bioavailability (OB) ≥ 30% and Drug Like (DL) ≥ 0.18 or blood ingredient were used to screen the active components of CR; the interactive network of targets and compounds were constructed by STRING and Cytoscape platform, and the network was analyzed by Molecular Complex Detection (MCODE); Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) and metabolic pathway enrichment of targets were carried out with Metascape, the Database for Annotation, Visualization and Integrated Discovery (DAVID) and MetaboAnalyst platform; Based on CytoHubba, the potential efficient targets were screened by Maximal Clique Centrality (MCC) and Degree, the correlation between potential efficient targets and amyloid β-protein (Aβ), Tau pathology was analyzed by Alzdata database, and the genes related to aging were analyzed by Aging Altas database, and finally, the core targets were obtained; the binding ability between ingredients and core targets evaluated by molecular docking, and the clinical significance of core targets was assessed with Gene Expression Omnibus (GEO) database.Results19 active components correspond to 267 therapeutic targets for AD, of which 69 is potentially effective; in module analysis, RELA, TRAF2, STAT3, and so on are the critical targets of each module; among the six core targets, RELA, MAPK8, STAT3, and TGFB1 have clinical therapeutic significance; GO function, including 3050 biological processes (BP), 257 molecular functions (MF), 184 cellular components (CC), whose functions are mainly related to antioxidation, regulation of apoptosis and cell composition; the HIF-1 signaling pathway, glutathione metabolism is the most significant result of 134 KEGG signal pathways and four metabolic pathways, respectively; most of the active components have an excellent affinity in docking with critical targets.ConclusionThe pharmacological target prediction of CR based on molecular network pharmacology paves the way for a multi-level networking strategy. The study of CR in AD treatment shows a bright prospect for curing neurodegenerative diseases.

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“…The best way to understand an organism's internal changes is to conduct a pathway analysis. The disruption of phenylalanine metabolism in the hippocampus could be an important factor in the progression of AD (Liu et al, 2021 ). In AD, the peripheral modulation of tyrosine phosphorylation signaling could be investigated as a potential diagnostic marker (Mallozzi et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Sun

Peng

2022

Front. Aging Neurosci.

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Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.

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Phenylalanine Metabolism Is Dysregulated in Human Hippocampus with Alzheimer’s Disease Related Pathological Changes

Cited by 34 publications

References 60 publications

Metabolomics and biochemical insights on the regulation of aging-related diabetes by a low-molecular-weight polysaccharide from green microalga Chlorella pyrenoidosa

Metabolomics and biochemical insights on the regulation of aging-related diabetes by a low-molecular-weight polysaccharide from green microalga Chlorella pyrenoidosa

Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Coptidis Rhizoma for the Treatment of Alzheimer's Disease

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

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