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Guo K, Zhou J. Insights into eukaryotic translation initiation factor 5A: Its role and mechanisms in protein synthesis. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2024; 1871:119849. [PMID: 39303786 DOI: 10.1016/j.bbamcr.2024.119849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 08/29/2024] [Accepted: 09/12/2024] [Indexed: 09/22/2024]
Abstract
The protein synthesis within eukaryotic cells is a complex process involving various translation factors. Among these factors, eukaryotic translation initiation factor 5 A (eIF5A) emerges as a crucial translation factor with high evolutionary conservation. eIF5A is unique as it is the only protein in eukaryotic cells containing the hypusine modification. Initially presumed to be a translation initiation factor, eIF5A was subsequently discovered to act mainly during the translation elongation phase. Notably, eIF5A facilitates the translation of peptide sequences containing polyproline stretches and exerts a universal regulatory effect on the elongation and termination phases of protein synthesis. Additionally, eIF5A indirectly affects various physiological processes within the cell by modulating the translation of specific proteins. This review provides a comprehensive overview of the structure, physiological functions, various post-translational modifications of eIF5A, and its association with various human diseases. The comparison between eIF5A and its bacterial homolog, EF-P, extends the discussion to the evolutionary conservation of eIF5A. This highlights its significance across different domains of life.
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Affiliation(s)
- Keying Guo
- Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Jie Zhou
- Life Sciences Institute, Zhejiang University, Hangzhou 310058, China.
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Qi H, Xie YY, Yang XJ, Xia J, Liu K, Zhang FX, Peng WJ, Wen FY, Li BX, Zhang BW, Yao XY, Li BY, Meng HD, Shi ZM, Wang Y, Zhang L. Susceptibility gene identification and risk evaluation model construction by transcriptome-wide association analysis for salt sensitivity of blood pressure. BMC Genomics 2024; 25:612. [PMID: 38890564 PMCID: PMC11184770 DOI: 10.1186/s12864-024-10409-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. METHODS AND RESULTS A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets. CONCLUSIONS Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals.
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Affiliation(s)
- Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Capital Medical University, Beijing, 100088, China
| | - Yun-Yi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xiao-Jun Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Feng-Xu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Wen-Juan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Fu-Yuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bing-Xiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Wen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xin-Yue Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Ya Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Hong-Dao Meng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zu-Min Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Yang Wang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China.
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Liu X, Li X, Li C, Lu M, Xu L, Yan R, Song X, Li X. Toxoplasma gondii eIF-5A Modulates the Immune Response of Murine Macrophages In Vitro. Vaccines (Basel) 2024; 12:101. [PMID: 38276673 PMCID: PMC10819733 DOI: 10.3390/vaccines12010101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
Toxoplasma gondii (T. gondii) is an obligate intracellular protozoan that can elicit a robust immune response during infection. Macrophage cells have been shown to play an important role in the immune response against T. gondii. In our previous study, the eukaryotic translation initiation factor 5A (eIF-5A) gene of T. gondii was found to influence the invasion and replication of tachyzoites. In this study, the recombinant protein of T. gondii eIF-5A (rTgeIF-5A) was incubated with murine macrophages, and the regulatory effect of TgeIF-5A on macrophages was characterized. Immunofluorescence assay showed that TgeIF-5A was able to bind to macrophages and partially be internalized. The Toll-like receptor 4 (TLR4) level and chemotaxis of macrophages stimulated with TgeIF-5A were reduced. However, the phagocytosis and apoptosis of macrophages were amplified by TgeIF-5A. Meanwhile, the cell viability experiment indicated that TgeIF-5A can promote the viability of macrophages, and in the secretion assays, TgeIF-5A can induce the secretion of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and nitric oxide (NO) from macrophages. These findings demonstrate that eIF-5A of T. gondii can modulate the immune response of murine macrophages in vitro, which may provide a reference for further research on developing T. gondii vaccines.
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Affiliation(s)
- Xinchao Liu
- Anhui Province Key Laboratory of Animal Nutritional Regulation and Health, College of Animal Science, Anhui Science and Technology University, Fengyang 233100, China;
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Xiaoyu Li
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Chunjing Li
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Mingmin Lu
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Lixin Xu
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Ruofeng Yan
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Xiaokai Song
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
| | - Xiangrui Li
- MOE Joint International Research Laboratory of Animal Health and Food Safety, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (C.L.); (M.L.); (L.X.); (R.Y.); (X.S.)
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