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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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Wang Y, Ni P, Zhuang D, Zhou P, Zhu F, Yin D, Zhu R, Mei B, Hu S. Early hyperbaric oxygen therapy through regulating the HIF-1α signaling pathway attenuates Neuroinflammation and behavioral deficits in a mouse model of Sepsis-associated encephalopathy. J Neuroimmunol 2024; 391:578367. [PMID: 38735091 DOI: 10.1016/j.jneuroim.2024.578367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/18/2024] [Accepted: 05/05/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND Sepsis-associated encephalopathy (SAE) presents a significant clinical challenge, associated with increased mortality and healthcare expenses. Hyperbaric oxygen therapy (HBOT), involving inhaling pure or highly concentrated oxygen under pressures exceeding one atmosphere, has demonstrated neuroprotective effects in various conditions. However, the precise mechanisms underlying its protective actions against sepsis-associated brain injury remain unclear. This study aimed to determine whether HBOT protects against SAE and to elucidate the impact of the hypoxia-inducible factor-1α (HIF-1α) signaling pathway on SAE. METHODS The experiment consisted of two parts. In the first part, C57BL/6 J male mice were divided into five groups using a random number table method: control group, sham surgery group, sepsis group, HBOT + sepsis group, and HBOT + sham surgery group. In the subsequent part, C57BL/6 J male mice were divided into four groups: sepsis group, HBOT + sepsis group, HIF-1α + HBOT + sepsis group, and HIF-1α + sepsis group. Sepsis was induced via cecal ligation and puncture (CLP). Hyperbaric oxygen therapy was administered at 1 h and 4 h post-CLP. After 24 h, blood and hippocampal tissue were collected for cytokine measurements. HIF-1α, TNF-α, IL-1β, and IL-6 expression were assessed via ELISA and western blotting. Microglial expression was determined by immunofluorescence. Blood-brain barrier permeability was quantified using Evans Blue. Barnes maze and fear conditioning were conducted 14 days post-CLP to evaluate learning and memory. RESULTS Our findings reveal that CLP-induced hippocampus-dependent cognitive deficits coincided with elevated HIF-1α and increased TNF-α, IL-1β, and IL-6 levels in both blood and hippocampus. Observable activation of microglial cells in the hippocampus and increased blood-brain barrier (BBB) permeability were also evident. HBOT mitigated HIF-1α, TNF-α, IL-1β, and IL-6 levels, attenuated microglial activation in the hippocampus, and significantly improved learning and memory deficits in CLP-exposed mice. Additionally, these outcomes were corroborated by injecting a lentivirus that overexpressed HIF-1α into the hippocampal region of the mice. CONCLUSION HIF-1α escalation induced peripheral and central inflammatory factors, promoting microglial activation, BBB impairment, and cognitive dysfunction. However, HBOT ameliorated these effects by reducing HIF-1α levels in Sepsis-Associated Encephalopathy.
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Affiliation(s)
- Yan Wang
- School of Nursing, Anhui Medical University, Hefei, Anhui Province, China; The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Ping Ni
- School of Nursing, Anhui Medical University, Hefei, Anhui Province, China
| | - Dongmei Zhuang
- School of Nursing, Anhui Medical University, Hefei, Anhui Province, China
| | - Peng Zhou
- The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Furong Zhu
- School of Nursing, Anhui Medical University, Hefei, Anhui Province, China
| | - Danqiao Yin
- The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Rui Zhu
- The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Bin Mei
- Department of Anesthesiology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
| | - Shaohua Hu
- Department of Nursing, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
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Le Bars S, Bolteau M, Bourdon J, Guziolowski C. Predicting weighted unobserved nodes in a regulatory network using answer set programming. BMC Bioinformatics 2023; 24:321. [PMID: 37626282 PMCID: PMC10463596 DOI: 10.1186/s12859-023-05429-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely. RESULTS To address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool. CONCLUSIONS MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS' output, thanks to its weight, could easily be integrated with metabolic network modelling.
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Affiliation(s)
- Sophie Le Bars
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
| | - Mathieu Bolteau
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
| | - Jérémie Bourdon
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
| | - Carito Guziolowski
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000 France
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Shi T, Yu H, Blair RH. Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization. Stat Appl Genet Mol Biol 2023; 22:sagmb-2022-0054. [PMID: 37988745 DOI: 10.1515/sagmb-2022-0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 09/28/2023] [Indexed: 11/23/2023]
Abstract
Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.
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Affiliation(s)
- Tiange Shi
- University at Buffalo, Biostatistics, Buffalo, USA
| | - Han Yu
- Roswell Park Comprehensive Cancer Center, Biostatistics and Bioinformatics, Buffalo, USA
| | - Rachael Hageman Blair
- University at Buffalo, Biostatistics, Institute for Artificial Intelligence and Data Science, Buffalo, USA
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Zhao L, Song Y, Zhang Y, Liu H, Shen Y, Fan Y, Li Y, Xie K. HIF-1α/BNIP3L induced cognitive deficits in a mouse model of sepsis-associated encephalopathy. Front Immunol 2022; 13:1095427. [PMID: 36569834 PMCID: PMC9768421 DOI: 10.3389/fimmu.2022.1095427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Objective Sepsis Associated Encephalopathy (SAE) is a common complication in critically ill patients and perioperative period, but its pathogenesis is still unclear. This study aimed to explore the effect of the HIF-1α (hypoxia-inducible factor-1α)/BNIP3L (Bcl-2/adenovirus E1B 19-kDa interaction protein) signaling pathway on SAE. Methods C57BL/6J male mice were divided into four groups, using a random number table method: control group, sham group, sepsis group, sepsis+HIF-1α activity inhibitor (echinomycin) group. Sepsis was induced by cecal ligation and puncture (CLP). At 24 h after surgery, brain tissue was sampled. HE was staining to observe changes in the hippocampus structure. Fluoroscopy observes changes in mitochondrial structure. Western blot, QT-PCR, and immunofluorescence were used to assess the amount of expression of HIF-1α and BNIP3L in the hippocampus and mitochondrion of hippocampus neurons. Observation of neuronal apoptosis by TUNEL staining. Seven days after surgery, mice were tested in a Morris water maze test to assess cognitive function after CLP. Results Our results show that CLP-induced hippocampus-dependent cognitive deficits were accompanied with increased HIF 1a and decreased BNIP3L, increased protein levels of TNF-α, IL-6, and IL-β, and damage to mitochondrial structures and neuronal apoptosis in the hippocampus. In addition, administration of echinomycin rescues cognitive deficits, ameliorates HIF-1α and BNIP3L-mediated neuronal pyroptosis and damaged mitochondrial structures, and decreases the expression of TNF-α and IL-6 in the hippocampus. Conclusions HIF-1α and the BNIP3L promote mitochondrial damage, and neuronal apoptosis and the expression of inflammatory factors may be the mechanism of SAE in critically ill patients and perioperative period.
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Affiliation(s)
- Lina Zhao
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Song
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Zhang
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Haiying Liu
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuehao Shen
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Yan Fan
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Yun Li
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China,*Correspondence: Yun Li, ; Keliang Xie,
| | - Keliang Xie
- Department of Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China,Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Research Institute of Anesthesiology, Tianjin, China,*Correspondence: Yun Li, ; Keliang Xie,
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Myers PJ, Lee SH, Lazzara MJ. MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:100349. [PMID: 35935921 PMCID: PMC9348571 DOI: 10.1016/j.coisb.2021.05.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.
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Affiliation(s)
- Paul J. Myers
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Sung Hyun Lee
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Matthew J. Lazzara
- Department of Chemical Engineering, Charlottesville, VA 22904
- Department of Biomedical Engineering University of Virginia, Charlottesville, VA 22904
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Niu P, Soto MJ, Yoon BJ, Dougherty ER, Alexander FJ, Blaby I, Qian X. TRIMER: Transcription Regulation Integrated with Metabolic Regulation. iScience 2021; 24:103218. [PMID: 34761179 PMCID: PMC8567008 DOI: 10.1016/j.isci.2021.103218] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/22/2021] [Accepted: 09/29/2021] [Indexed: 01/01/2023] Open
Abstract
There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches. TRIMER models transcription-regulated metabolism using Bayesian network modeling; TRIMER integrates prior knowledge (regulatory interaction) with data (expression); TRIMER enables metabolic behavior prediction for general knockout strategies; TRIMER includes a simulator as an evaluation platform for similar hybrid models; TRIMER reliably predicts metabolite yields for both simulated and experimental data.
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Affiliation(s)
- Puhua Niu
- Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA
| | - Maria J. Soto
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Byung-Jun Yoon
- Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA
- Brookhaven National Laboratory, Computational Science Initiative, Upton, NY, 11973, USA
| | - Edward R. Dougherty
- Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA
| | - Francis J. Alexander
- Brookhaven National Laboratory, Computational Science Initiative, Upton, NY, 11973, USA
| | - Ian Blaby
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Corresponding author
| | - Xiaoning Qian
- Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA
- Brookhaven National Laboratory, Computational Science Initiative, Upton, NY, 11973, USA
- Corresponding author
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Heinken A, Basile A, Hertel J, Thinnes C, Thiele I. Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine. Annu Rev Microbiol 2021; 75:199-222. [PMID: 34314593 DOI: 10.1146/annurev-micro-060221-012134] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Almut Heinken
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland;
| | - Arianna Basile
- Department of Biology, University of Padua, Padua 35121, Italy
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland; .,Department of Psychiatry and Psychotherapy, University of Greifswald, 17489 Greifswald, Germany
| | - Cyrille Thinnes
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland;
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, H91 TK33, Ireland; .,Division of Microbiology, National University of Ireland, Galway, H91 TK33, Ireland.,APC Microbiome Ireland, University College Cork, Cork, T12 K8AF, Ireland
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Huang X, Li T, Zhou L, Liu T, Xiong L, Yu C. Analysis of the potential and mechanism of Ginkgo biloba in the treatment of Alzheimer's disease based on network pharmacology. IBRAIN 2021; 7:21-28. [PMID: 37786872 PMCID: PMC10529169 DOI: 10.1002/j.2769-2795.2021.tb00060.x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 12/09/2020] [Accepted: 12/27/2020] [Indexed: 02/05/2023]
Abstract
Objective Study the principle and possible mechanism of Ginkgo biloba in the treatment of Alzheimer's disease (AD) which is based on network pharmacology. Methods The potential targets of active ingredients of Ginkgo biloba were collected by Traditional Chinese Medicine Integrated Database platform (TCMSP). TCMSP is a pharmacological system for drug discovery from Chinese herbal medicine. The disease targets of AD were searched and collected by the database of gene-disease associations (DisGeNET) and literature. The obtained targets were standardized by the UniProt database. STING network platform and Cytoscape were used to construct protein-protein interaction network (PPI) of the key targets. According to Materscape, we clarify the possible mechanism of action including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis. Results The compound-target network contains 27 active ingredients, 191 related targets, 18 key targets, including PLAU, HMOX1, TNF, INSR, MPO, MAOB, IGF2, IL1B, ESR1, BCL2, ACHE, BAX, GSK3B, PPARG, SLC2A4, NOS3, CASP3, VEGFA. GO enrichment analysis has got a total of 640 GO items, including 609 biological process (BP) items (95.1%), 16 molecular function (MF) items (2.5%) and 15 cellular component (CC) items (2.4%). After KEGG enrichment, 44 pathways were obtained. Conclusion Through the construction of "component-target-pathway", GO biological function and KEGG pathway enrichment analysis were performed on core targets, and the possibility of Ginkgo biloba for the treatment of AD was explored from multiple targets and pathways, which provided a new approach for multi-target treatment.
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Affiliation(s)
- Xue‐Yan Huang
- Department of NeurologyAffiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Ting‐Ting Li
- Institute of Neurological Disease and Department of AnesthesiologyTranslational Neuroscience Center, West China Hospital, Sichuan UniversityChengduSichuanChina
| | - Lin Zhou
- Department of AnesthesiologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiChina
| | - Tao Liu
- Department of NeurologyAffiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Liu‐Lin Xiong
- Clinical and Health Sciences, University of South AustraliaAdelaide5000South AustraliaAustralia
| | - Chang‐Yin Yu
- Department of NeurologyAffiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
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Maiese K. Dysregulation of metabolic flexibility: The impact of mTOR on autophagy in neurodegenerative disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 155:1-35. [PMID: 32854851 DOI: 10.1016/bs.irn.2020.01.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Non-communicable diseases (NCDs) that involve neurodegenerative disorders and metabolic disease impact over 400 million individuals globally. Interestingly, metabolic disorders, such as diabetes mellitus, are significant risk factors for the development of neurodegenerative diseases. Given that current therapies for these NCDs address symptomatic care, new avenues of discovery are required to offer treatments that affect disease progression. Innovative strategies that fill this void involve the mechanistic target of rapamycin (mTOR) and its associated pathways of mTOR complex 1 (mTORC1), mTOR complex 2 (mTORC2), AMP activated protein kinase (AMPK), trophic factors that include erythropoietin (EPO), and the programmed cell death pathways of autophagy and apoptosis. These pathways are intriguing in their potential to provide effective care for metabolic and neurodegenerative disorders. Yet, future work is necessary to fully comprehend the entire breadth of the mTOR pathways that can effectively and safely translate treatments to clinical medicine without the development of unexpected clinical disabilities.
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Affiliation(s)
- Kenneth Maiese
- Cellular and Molecular Signaling, New York, NY, United States.
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Disease global behavior: A systematic study of the human interactome network reveals conserved topological features among categories of diseases. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100249] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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