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Effects of Essential Amino Acid Deficiency on General Control Nonderepressible 2/Eukaryotic Initiation Factor 2 Signaling and Proteomic Changes in Primary Bovine Mammary Epithelial Cells. Curr Issues Mol Biol 2022; 44:1075-1086. [PMID: 35723294 PMCID: PMC8947524 DOI: 10.3390/cimb44030071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 11/16/2022] Open
Abstract
We hypothesized that the general control nonderepressible 2 (GCN2)/eukaryotic initiation factor 2 (eIF2) signaling pathway and intracellular protein synthesis (PS) are regulated to maintain milk PS in primary bovine mammary epithelial cells (MECs) under essential amino acid (EAA) starvation conditions. We cultured MECs with 0%, 2% (depletion), and 100% (control) EAA for two exposure times (8 and 24 h), followed by three refeeding (RF) times with 100% EAA (0, 8, and 24 h). Subsequently, we measured cell viability, total protein concentration, and proliferation. Western blotting was used to quantify the levels of casein and the expression of total GCN2 and eIF2, as well as phosphorylated GCN2 (GCN2P) and eIF2 (eIF2P). The ISOQuant method was used to assess MEC proteomes, and the resultant data were analyzed using the Kruskal−Wallis test, nonpaired Wilcoxon rank post-hoc test, and ANOVA−Tukey test, as well as principal component analyses and multiple regressions models. Differences in cell viability were observed between the control versus the depleted and repleted MECs, respectively, where 97.2−99.8% viability indicated low cell death rates. Proliferation (range, 1.02−1.55 arbitrary units (AU)) was affected by starvation for 12 and 24 h and repletion for 24 h, but it was not increased compared with the control. Total protein expression was unaffected by both depletion and repletion treatments (median 3158 µg/mL). eIF2P expression was significantly increased (p < 0.05) after treatment with 2% EAA for 8 and 24 h compared with 2% EAA with 8 h + 24 h RF and 2% EAA with 24 h + 8 h RF. GCN2P also showed significantly increased expression (p < 0.05) after treatment with 2% EAA for 24 h compared with the control and 2% EAA with 24 h + 8 h RF. Intracellular casein/α-tubulin expression was unaffected by 2% EAA compared with control (0.073 ± 0.01 AU versus 0.086 ± 0.02 AU, respectively). We studied 30 of the detected 1180 proteins, 16 of which were differentially expressed in starved and refed MECs. Cells faced with EAA deficiency activated the GCN2P/eIF2P pathway, and the lack of change in the levels of casein and other milk proteins suggested that the EAA deficit was mitigated by metabolic flexibility to maintain homeostasis.
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Ren Y, Yang L, Li M, Wang J, Yan H, Ma N, Liu W, Wang L, Gao X, Gao P, Li T, Liu D. 4210 Da and 1866 Da polypeptides as potential biomarkers of liver disease progression in hepatitis B virus patients. Sci Rep 2021; 11:16982. [PMID: 34417517 PMCID: PMC8379215 DOI: 10.1038/s41598-021-96581-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 08/12/2021] [Indexed: 12/18/2022] Open
Abstract
HBV infection is recognized as a serious global health problem, and hepatitis B virus infection is a complicated chronic disease leading to liver cirrhosis (LC) and hepatocellular carcinoma (HCC). New biochemical serum markers could be used to advance the diagnosis and prognosis of HBV-associated liver diseases during the progression of chronic hepatitis B into cirrhosis and HCC. We determined whether the 4210 Da and 1866 Da polypeptides are serum metabolite biomarkers of hepatopathy with hepatitis B virus. A total of 570 subjects were divided into five groups: healthy controls, those with natural clearance, and patients with CHB, LC, and HCC. The 1866 Da and 4210 Da polypeptides were measured by Clin-ToF II MALDI-TOF-MS. There were significant differences in 4210 Da and 1866 Da levels among the five groups (P < 0.001). For the differential diagnosis of CHB from normal liver, the areas under the receiver operating characteristic (ROC) curve of 4210 Da and 1866 Da and their combination via logistic regression were 0.961, 0.849 and 0.967. For the differential diagnosis of LC from CHB, the areas under the ROC curve were 0.695, 0.841 and 0.826. For the differential diagnosis of HCC from CHB, the areas under the ROC curve were 0.744, 0.710 and 0.761, respectively. For the differential diagnosis of HCC from LC, the areas under the ROC curve of 4210 Da and 1866 Da were 0.580 and 0.654. The positive rate of 1866 Da was 45.5% and 69.0% in AFP-negative HCC patients and that of 4210 Da was 60.6% 58.6% in AFP-negative HCC patients of the study HCC vs. CHB and HCC vs. LC. The 4210 Da and 1866 Da polypeptide levels were positively correlated with HBV DNA levels (P < 0.001, r = 0.269; P < 0.001, r = 0.285). The 4210 Da and 1866 Da polypeptides had good diagnostic value for the occurrence and progression of HBV-related chronic hepatitis, liver cirrhosis and hepatocellular carcinoma and could serve to accurately guide treatment management and predict clinical outcomes.
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Affiliation(s)
- Yuanyuan Ren
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
- Department of Food Quality and Safety, College of Food Science and Biology, Hebei University of Science and Technology, Shijiazhuang, 050018, China
| | - Lei Yang
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Man Li
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Jian Wang
- Department of Epidemiology, Hebei North University, Zhangjiakou, 075000, China
| | - Huimin Yan
- Clinical Research Center, Shijiazhuang Fifth Hospital, Shijiazhuang, 050021, China
| | - Ning Ma
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Wenxuan Liu
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Liqin Wang
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xia Gao
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Ping Gao
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Tao Li
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Dianwu Liu
- Hebei Key Laboratory of Environment and Human Health, Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China.
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