CNC-bZIP Protein Nrf1-Dependent Regulation of Glucose-Stimulated Insulin Secretion

Zheng, Honghua; Fu, Jingqi; Xue, Peng; Zhao, Rui; Dong, Jian; Liu, Dianxin; Yamamoto, Masayuki; Tong, Qingchun; Wang, Teng; Qu, Weidong; Zhang, Qiang; Andersen, Melvin E.; Pi, Jingbo

doi:10.1089/ars.2014.6017

Cited by 67 publications

(75 citation statements)

References 51 publications

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“…Thus, the putative demand for glucose is positively correlated with the subcutaneous tumorigenicility of distinct cancer xenografts in nude mice, as reported by Qiu et al [30]. Indeed, this is also corroborated by the previous finding that glucose uptake is substantially increased by silencing of Nrf1 in pancreatic isle -cells [29].…”

Section: Discussionsupporting

confidence: 79%

“…Moreover, Nrf1 was also found to contribute to negative regulation of the Cystine/Glutamate transporter and lipid-metabolizing enzymes [27]. Interestingly, Nrf1 was also positively involved in glycolysis pathway by regulating the Slc2a2, Gck [28] and HK1 genes [29]. However, it is not clear about a role of Nrf1 in mediating the cancer cellular response to metabolic stress, especially stimulated by glucose deprivation.…”

Section: Introductionmentioning

confidence: 99%

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Glucose starvation to rapid death of Nrf1, but not Nrf2, deficient hepatoma cells from its fatal defects in the redox metabolism reprogramming

Zhu

Zheng

Xiang

et al. 2019

Preprint

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Metabolic reprogramming exists in a variety of cancer cells, with the most relevance to glucose as a source of energy and carbon for survival and proliferation. Of note, Nrf1 was shown to be essential for regulating glycolysis pathway, but it is unknown whether it plays a role in cancer metabolic reprogramming, particularly in response to glucose starvation.Herein, we discover that Nrf1α / -derived hepatoma cells are sensitive to rapid death induced by glucose deprivation, such cell death appears to be rescued by Nrf2 interference, but Nrf1/2 / or Nrf2 / -derived cells are roughly unaffected by glucose starvation. Further evidence revealed that Nrf1α / cell death is resulted from severe oxidative stress arising from abberrant redox metabolism. Strikingly, altered gluconeogenesis pathway was aggravated by glucose starvation of Nrf1α / cells, as also accompanied by weakened pentose phosphate pathway, dysfunction of serine-to-glutathione synthesis, and accumulation of reactive oxygen species (ROS) and damages, such that the intracellular GSH and NADPH were exhausted. These demonstrate that glucose starvation leads to acute death of Nrf1α / , rather than Nrf2 / , cells resulting from its fatal defects in the redox metabolism reprogramming. This is owing to distinct requirements of Nrf1 and Nrf2 for regulating key genes involved in the redox metabolic reprogramming. Altogether, this work substantiates the preventive and therapeutic strategies against Nrf1α-deficient cancer by limiting its glucose and energy demands.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Glucose starvation to rapid death of Nrf1, but not Nrf2, deficient hepatoma cells from its fatal defects in the redox metabolism reprogramming

Zhu

Zheng

Xiang

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…In this light, the identification of the NFE2L1:MAFG composite motif as significantly over-represented in adipocyte-specific peaks is especially interesting. While NFE2L1 has been reported to have metabolic functions in liver and pancreatic β-cells (Hirotsu et al, 2012; Zheng et al, 2015), our results suggest that these or related factors may play an important role in adipocyte biology. In this study, we focused on adipocyte-specific profiles in healthy conditions.…”

Section: Discussionmentioning

confidence: 52%

Simultaneous Transcriptional and Epigenomic Profiling from Specific Cell Types within Heterogeneous Tissues In Vivo

et al. 2017

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SUMMARY Epigenomic mechanisms direct distinct gene expression programs for different cell types. Various in vivo tissues have been subjected to epigenomic analysis; however, these studies have been limited by cellular heterogeneity resulting in composite gene expression and epigenomic profiles. Here, we introduce “NuTRAP”, a transgenic mouse that allows simultaneous isolation of cell type-specific translating mRNA and chromatin from complex tissues. Using NuTRAP, we successfully characterize gene expression and epigenomic states of various adipocyte populations in vivo, revealing significant differences compared to either whole adipose tissue or in vitro adipocyte cell lines. We find that ChIP-seq using NuTRAP is highly efficient, scalable, and robust with even limited cell input. We further demonstrate the general utility of NuTRAP by analyzing hepatocyte-specific epigenomic states. The NuTRAP mouse is a resource that provides a powerful system for cell type-specific gene expression and epigenomic profiling.

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“…Specifically, Nrf1 inactivation in beta-cells results in a pre-T2D phenotype because of impairment of insulin secretion and disruption of glucose metabolism [48]. In the study from Hirotsu et al, Nrf1 over-expression has a negative impact on both glucose utilization and production in the liver by suppressing the genes related to both glycolysis and gluconeogenesis [49].…”

Section: Resultsmentioning

confidence: 99%

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

Tang

Yang

et al. 2016

BMC Genomics

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BackgroundDiabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.ResultsIn this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.ConclusionsThe results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

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CNC-bZIP Protein Nrf1-Dependent Regulation of Glucose-Stimulated Insulin Secretion

Abstract: Nrf1 plays critical roles in regulating glucose metabolism, mitochondrial function, and insulin secretion, suggesting that Nrf1 may be a novel target to improve the function of insulin-secreting β-cells.

Cited by 67 publications

References 51 publications

Glucose starvation to rapid death of Nrf1, but not Nrf2, deficient hepatoma cells from its fatal defects in the redox metabolism reprogramming

Glucose starvation to rapid death of Nrf1, but not Nrf2, deficient hepatoma cells from its fatal defects in the redox metabolism reprogramming

Simultaneous Transcriptional and Epigenomic Profiling from Specific Cell Types within Heterogeneous Tissues In Vivo

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

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