Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network

Yang, Wen; Wang, Xia; Li, Kenan; Liu, Yuanru; Liu, Ying; Wang, Rui; Li, Honglin

doi:10.1007/s40484-018-0163-4

Cited by 3 publications

(3 citation statements)

References 72 publications

(83 reference statements)

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“…In ferroptosis tissues, immuno uorescence staining show the presence of signi cantly activated macrophages 42 . There are certain in ammatory mediators produced by peroxide metabolism and arachidonic acid metabolism in ferroptosis tissues 43 . p53 can alter macrophage function by regulating ferroptosis and plays an important role in regulating host immune responses to bacterial and malaria infections 44 .…”

Section: Discussionmentioning

confidence: 99%

The Role of Ferroptosis in Major Depressive Disorder

Li,

Miao,

et al. 2023

Preprint

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Objective: To study the relationship between ferroptosis genes and Major Depressive Disorder (MDD). Methods: The GEO database was utilized to obtain chip data and clinical information from three datasets, namely GSE98793, GSE39653, and GSE52790. To identify differentially expressed ferroptosis genes, an analysis was conducted on genes that showed differential expression between individuals with Major Depressive Disorder (MDD) and healthy controls. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) enrichment analyses were performed on the differentially expressed ferroptosis genes. Screening of these genes was carried out using the Lasso Regression and Support Vector Machine (SVM) methods. A diagnostic model for depression was established using logistic regression with the screened genes. The model was validated using Receiver Operating Characteristic (ROC) Curve analysis. Furthermore, the Gene Set Enrichment Analysis (GSEA) method was applied to analyze the genes included in the model. The immune infiltration of MDD and healthy controls was assessed using the Cibersort method, along with an analysis of the correlation between immune cells and ferroptosis genes. The ferroptosis gene interaction network was examined using Cytoscape software. Additionally, the DGIdb website was consulted to predict potentially effective therapeutic drugs for ferroptosis genes closely associated with MDD. Results: A total of 18 genes involved in ferroptosis were identified through differential expression analysis comparing individuals with major depressive disorder (MDD) and healthy controls. Enrichment analysis, including GO and KEGG, revealed concentrated pathways related to oxidative stress response, hydrogen peroxide response, reactive oxygen species response, FoxO signaling pathway, fluid shear stress, and atherosclerosis. To identify key genes related to ferroptosis in MDD, Lasso regression and SVM techniques were employed, resulting in the selection of 10 genes. The depression diagnostic model, which utilized these 10 genes, achieved an area under the curve (AUC) of 0.773. The Gene Set Enrichment Analysis (GSEA) focusing on individual genes demonstrated that Parkinson's disease, Huntington's disease, and oxidative phosphorylation pathways were highly enriched. The analysis of immune infiltration further revealed significant differences in the resting NK cells and M2 macrophages between individuals with MDD and control subjects. Specifically, PHF21A was found to be closely associated with resting NK cells in MDD, whereas METTL14 and MAPK14 were closely related to M2 macrophages. The RNA interactions network of ferroptosis genes indicated a complex regulatory process, providing valuable insights for future research in this field. In terms of potential therapeutic options, ALOX15B, MAPK14, PRKAA1, and MICU1 among the 10 ferroptosis-related genes were found to have potential for effective therapeutic drugs. Conclusion: Among all ferroptosis genes, ALOX15B, MAPK14, PRKAA1, PHF21A, MICU1, KLF2, METTL14, TP63, PARK7, PARP4 are closely related to MDD and have diagnostic value. Among them, ALOX15B, MAPK14, PRKAA1 and MICU1 may have potential effective therapeutic drugs.

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Section: Discussionmentioning

confidence: 99%

The Role of Ferroptosis in Major Depressive Disorder

Li,

Miao,

et al. 2023

Preprint

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“…In comparison, published models of the AA cascade perform model refinement against experimental data of a single stimulus and cell type. As a result, they cannot adapt the dynamics and magnitude of parameters such as AA release, to reflect the behaviour of different cell types and stimuli [21][22][23][24][25][26][27][28] . To overcome this limitation, Yang et al…”

Section: Adaptations To Represent Responses To Stimuli and Inhibitors...mentioning

confidence: 99%

“…Overall, the prevalence and profiles of eicosanoids are cell and/or organ-specific, depending on the expression of the relevant biosynthetic enzymes. Given the important role of eicosanoids in physiological and pathophysiological cellular responses, there is a strong interest in developing computational models that can be reliably used to assess hypotheses, predict adverse reactions, and support the development of novel therapeutics [21][22][23][24][25][26][27][28] . Currently, the majority of such in silico metabolic models focus on the AA cascade, as AA is the most abundant cellular C20-PUFA and the bioactivities of its metabolites are best understood.…”

Section: Introductionmentioning

confidence: 99%

An adaptable in silico ensemble model of the arachidonic acid cascade

Uttley,

Horne,

Tsigkinopoulou

et al. 2024

Mol. Omics

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Mathematical Modeling of Eicosanoid Metabolism in Macrophage Cells: Cybernetic Framework Combined with Novel Information-Theoretic Approaches

et al. 2023

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Cellular response to inflammatory stimuli leads to the production of eicosanoids—prostanoids (PRs) and leukotrienes (LTs)—and signaling molecules—cytokines and chemokines—by macrophages. Quantitative modeling of the inflammatory response is challenging owing to a lack of knowledge of the complex regulatory processes involved. Cybernetic models address these challenges by utilizing a well-defined cybernetic goal and optimizing a coarse-grained model toward this goal. We developed a cybernetic model to study arachidonic acid (AA) metabolism, which included two branches, PRs and LTs. We utilized a priori biological knowledge to define the branch-specific cybernetic goals for PR and LT branches as the maximization of TNFα and CCL2, respectively. We estimated the model parameters by fitting data from three experimental conditions. With these parameters, we were able to capture a novel fourth independent experimental condition as part of the model validation. The cybernetic model enhanced our understanding of enzyme dynamics by predicting their profiles. The success of the model implies that the cell regulates the synthesis and activity of the associated enzymes, through cybernetic control variables, to accomplish the chosen biological goal. The results indicated that the dominant metabolites are PGD2 (a PR) and LTB4 (an LT), aligning with their corresponding known prominent biological roles during inflammation. Using heuristic arguments, we also infer that eicosanoid overproduction can lead to increased secretion of cytokines/chemokines. This novel model integrates mechanistic knowledge, known biological understanding of signaling pathways, and data-driven methods to study the dynamics of eicosanoid metabolism.

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Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network

Cited by 3 publications

References 72 publications

The Role of Ferroptosis in Major Depressive Disorder

The Role of Ferroptosis in Major Depressive Disorder

An adaptable in silico ensemble model of the arachidonic acid cascade

Mathematical Modeling of Eicosanoid Metabolism in Macrophage Cells: Cybernetic Framework Combined with Novel Information-Theoretic Approaches

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