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Rath S, Hawsawi YM, Alzahrani F, Khan MI. Epigenetic regulation of inflammation: The metabolomics connection. Semin Cell Dev Biol 2024; 154:355-363. [PMID: 36127262 DOI: 10.1016/j.semcdb.2022.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/12/2022] [Accepted: 09/12/2022] [Indexed: 10/14/2022]
Abstract
Epigenetic factors are considered the regulator of complex machinery behind inflammatory disorders and significantly contributed to the expression of inflammation-associated genes. Epigenetic modifications modulate variation in the expression pattern of target genes without affecting the DNA sequence. The current knowledge of epigenetic research focused on their role in the pathogenesis of various inflammatory diseases that causes morbidity and mortality worldwide. Inflammatory diseases are categorized as acute and chronic based on the disease severity and are regulated by the expression pattern of various genes. Hence, understanding the role of epigenetic modifications during inflammation progression will contribute to the disease outcomes and therapeutic approaches. This review also focuses on the metabolomics approach associated with the study of inflammatory disorders. Inflammatory responses and metabolic regulation are highly integrated and various advanced techniques are adopted to study the metabolic signature molecules. Here we discuss several metabolomics approaches used to link inflammatory disorders and epigenetic changes. We proposed that deciphering the mechanism behind the inflammation-metabolism loop may have immense importance in biomarkers research and may act as a principal component in drug discovery as well as therapeutic applications.
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Affiliation(s)
- Suvasmita Rath
- Center of Environment, Climate Change and Public Health, Utkal University, Vani Vihar, Bhubaneswar 751004, Odisha, India
| | - Yousef M Hawsawi
- Research Center, King Faisal Specialist Hospital and Research Center, P.O. Box 40047, Jeddah 21499, Saudi Arabia; College of Medicine, Al-Faisal University, P.O. Box 50927, Riyadh 11533, Saudi Arabia.
| | - Faisal Alzahrani
- Department of Biochemistry, King Abdulaziz University (KAU), Jeddah 21577, Saudi Arabia; Embryonic Stem Cells Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohammad Imran Khan
- Department of Biochemistry, King Abdulaziz University (KAU), Jeddah 21577, Saudi Arabia; Centre of Artificial Intelligence for Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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Chen Q, Jiao Y, Yin Z, Fu X, Guo S, Zhou Y, Wang Y. Establishment of a novel glycolysis-immune-related diagnosis gene signature for endometriosis by machine learning. J Assist Reprod Genet 2023; 40:1147-1161. [PMID: 36930359 PMCID: PMC10239430 DOI: 10.1007/s10815-023-02769-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/02/2023] [Indexed: 03/18/2023] Open
Abstract
PURPOSE The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model. METHODS A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-based semantic similarity and logistic regression model analyses were used to investigate core genes. Reverse real-time quantitative PCR (RT-qPCR) of 5 target genes was analysed. RESULTS The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test sets, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) prediction model was 0.777, 0.824, and 0.774. Additionally, there was a remarkable difference in the immune environment between the EMS and control groups. Eventually, the five target genes were verified by RT-qPCR. CONCLUSION The glycolysis-immune-based predictive model was established to forecast EMS patients' diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.
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Affiliation(s)
- Qizhen Chen
- Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yufan Jiao
- Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhe Yin
- Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiayan Fu
- Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shana Guo
- Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yuhua Zhou
- Department of Ultrasonography, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Yanqiu Wang
- Reproductive Medical Center, Department of Gynecology and Obstetrics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
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Li H, Ma L, Li W, Zheng B, Wang J, Chen S, Wang Y, Ge F, Qin B, Zheng X, Deng Y, Zeng R. Proline metabolism reprogramming of trained macrophages induced by early respiratory infection combined with allergen sensitization contributes to development of allergic asthma in childhood of mice. Front Immunol 2022; 13:977235. [PMID: 36211408 PMCID: PMC9533174 DOI: 10.3389/fimmu.2022.977235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/02/2022] [Indexed: 11/13/2022] Open
Abstract
Background Infants with respiratory syncytial virus (RSV)-associated bronchiolitis are at increased risk of childhood asthma. Recent studies demonstrated that certain infections induce innate immune memory (also termed trained immunity), especially in macrophages, to respond more strongly to future stimuli with broad specificity, involving in human inflammatory diseases. Metabolic reprogramming increases the capacity of the innate immune cells to respond to a secondary stimulation, is a crucial step for the induction of trained immunity. We hypothesize that specific metabolic reprogramming of lung trained macrophages induced by neonatal respiratory infection is crucial for childhood allergic asthma. Objective To address the role of metabolic reprogramming in lung trained macrophages induced by respiratory virus infection in allergic asthma. Methods Neonatal mice were infected and sensitized by the natural rodent pathogen Pneumonia virus of mice (PVM), a mouse equivalent strain of human RSV, combined with ovalbumin (OVA). Lung CD11b+ macrophages in the memory phase were re-stimulated to investigate trained immunity and metabonomics. Adoptive transfer, metabolic inhibitor and restore experiments were used to explore the role of specific metabolic reprogramming in childhood allergic asthma. Results PVM infection combined with OVA sensitization in neonatal mice resulted in non-Th2 (Th1/Th17) type allergic asthma following OVA challenge in childhood of mice. Lung CD11b+ macrophages in the memory phage increased, and showed enhanced inflammatory responses following re-stimulation, suggesting trained macrophages. Adoptive transfer of the trained macrophages mediated the allergic asthma in childhood. The trained macrophages showed metabolic reprogramming after re-stimulation. Notably, proline biosynthesis remarkably increased. Inhibition of proline biosynthesis suppressed the development of the trained macrophages as well as the Th1/Th17 type allergic asthma, while supplement of proline recovered the trained macrophages as well as the allergic asthma. Conclusion Proline metabolism reprogramming of trained macrophages induced by early respiratory infection combined with allergen sensitization contributes to development of allergic asthma in childhood. Proline metabolism could be a well target for prevention of allergic asthma in childhood.
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Affiliation(s)
- Hanglin Li
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
| | - Linyan Ma
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
| | - Wenjian Li
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
- Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Hebei Medical University, Shijiazhuang, China
| | - Boyang Zheng
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
- The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang, China
| | - Junhai Wang
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
| | - Shunyan Chen
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
| | - Yang Wang
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
| | - Fei Ge
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
| | - Beibei Qin
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
- Clinical Lab, Hebei Provincial People’s Hospital, Shijiazhuang, China
| | - Xiaoqing Zheng
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
- Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Hebei Medical University, Shijiazhuang, China
| | - Yuqing Deng
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
- Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Hebei Medical University, Shijiazhuang, China
| | - Ruihong Zeng
- Department of Immunology, Hebei Medical University, Shijiazhuang, China
- Key Laboratory of Immune Mechanism and Intervention on Serious Disease in Hebei Province, Hebei Medical University, Shijiazhuang, China
- *Correspondence: Ruihong Zeng,
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He L, Wang X, Chen J, Li Y, Wang L, Xiong C, Nie Z. Biofluids Metabolic Profiling Based on PS@Fe 3O 4-NH 2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening. Anal Chem 2022; 94:10367-10374. [PMID: 35839421 DOI: 10.1021/acs.analchem.2c00654] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH2-coated polystyrene@Fe3O4 magnetic beads (PS@Fe3O4-NH2 MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe3O4-NH2 MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.
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Affiliation(s)
- Liuying He
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiao Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Junyu Chen
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yuze Li
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Liping Wang
- Centre of Reproductive Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Caiqiao Xiong
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
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