Association of Nuclear Receptor Coactivators with Hypoxia-Inducible Factor-1<i>α</i> in the Serum of Patients with Chronic Kidney Disease

Zhou, Tianbiao; Lin, Wenshan; Lin, Shujun; Zhong, Zhiqing; Luo, Yuanyuan; Lin, Zhijun; Xie, Weiji; Shen, Weitao; Hong, Kai

doi:https://doi.org/10.1155/2020/1587915

BioMed Research International

On this page

Abstract Introduction Materials and Methods Results Discussion Abbreviations Data Availability Ethical Approval Consent Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2020 | Article ID 1587915 | https://doi.org/10.1155/2020/1587915

Association of Nuclear Receptor Coactivators with Hypoxia-Inducible Factor-1α in the Serum of Patients with Chronic Kidney Disease

Tianbiao Zhou,¹Wenshan Lin,¹Shujun Lin,¹Zhiqing Zhong,¹Yuanyuan Luo,¹Zhijun Lin,¹Weiji Xie,¹Weitao Shen,²and Kai Hong²

Academic Editor: George Seki

Received03 Mar 2020

Revised29 May 2020

Accepted04 Aug 2020

Published20 Aug 2020

Abstract

Nuclear receptor coactivators (NCOAs), consisting of coactivators and corepressors, dramatically enhance the transcriptional activity of nuclear receptors. Hypoxia-inducible factor-1α (HIF-1α) is a transcription factor that plays a major role under hypoxic conditions. This study was performed with the focus on the association of NCOAs with HIF-1α in the serum of chronic kidney disease (CKD) patients. Sixty patients with stage 5 CKD and 30 healthy controls from The Second Affiliated Hospital of Shantou University Medical College, between March 21, 2019, and October 30, 2019, were recruited in this prospective cohort study. We analyzed the serum levels of NCOAs (NCOA1, NCOA2, and NCOA3), HIF-1α, vascular endothelial growth factor (VEGF), etc. and assessed whether there was any relationship between these parameters and CKD disease. We found that circulating NCOA1 was positively associated with circulating NCOA2, NCOA3, and HIF-1α. A positive correlation was also observed between NCOA2 and NCOA1, NCOA3, HIF-1α, and VEGF. Furthermore, statistically significant correlations between NCOA3 and NCOA1, NCOA2, and HIF-1α were observed. The serum levels of VEGF in the CKD group were higher than those of the healthy control group. Circulating NCOA1 and circulating NCOA2 were negatively associated with procalcitonin. In conclusion, there was an association between circulating NCOA1, NCOA2, NCOA3, and circulating HIF-1α, and circulating VEGF was a risk factor for CKD disease. However, more studies should be performed to confirm this hypothesis.

1. Introduction

Nuclear receptors (NRs) are ligand sequence-specific regulated transcription factors that activate (or repress) steroid, retinoid, and thyroid hormone signals into hormone-regulated gene expression, which can regulate gene transcription via a classic, genomic mechanism [1, 2]. A growing appreciation of the complexity of NR-mediated gene transcription led to the concept of existence of coregulators (e.g., coactivators and corepressors) that modulate the NR function [1]. Successful activation of transcription by NRs requires “coregulators” of transcription, and these coregulators include coactivators that accomplish reactions required for activation of transcription and corepressors that suppress transcription [3]. NR coactivators (NCOAs), consisting of coactivators and corepressors, are recruited to DNA by NRs, potentiating NR-dependent gene transcription [1, 2], and dramatically enhancing the transcriptional activity of NRs. NCOAs influence receptor transcription through a variety of mechanisms, including histone acetyltransferase activity, methylation, phosphorylation, and chromatin remodeling [1, 4]. The NCOA’s family consists of three members: NCOA1 (also known as SRC-1), NCOA2 (also known as SRC-2, TIF2, and GRIP1), and NCOA3 (also known as SRC-3, AIB1, ACTR, RAC3, p/CIP, and TRAM-1) [5–7]. In a previous study, NCOA2 (SRC-2) was detected in renal proximal tubular cells (HK-2) [8]. The kidney is an organ comprising of various NRs, and there has been no study regarding the role of NCOAs in renal disease.

Hypoxia-inducible factor-1α (HIF-1α) is a transcription factor that plays a major role under hypoxic conditions. Various studies reported that the increased expression of HIF-1α was associated with cancer risk and cancer prognosis [9–12]. However, currently, the effects of HIF and vascular endothelial growth factor (VEGF) are not well known. Some studies on renal diseases found that HIF-1α played a protective role [13–15], but others reported that it was a risk factor [16–18]. Current evidence indicated that HIF-1α could be regulated by some NR [19–21]; however, there was no report showing the relationship between NCOAs and HIF-1α. The VEGF is a target gene of HIF-1α [22].

Chronic kidney disease (CKD) is defined when the glomerular filtration rate is less than 60 mL/min/1.73 m² for more than 3 months [23]. It is caused by dysfunctional kidneys and has a complex pathogenesis. The molecular mechanism of kidney disease is extremely complex, and many factors, such as NCOAs, HIF-1α, and VEGF, may be involved in its pathogenesis and progression. In this study, we studied the role of circulating NCOAs, HIF-1α, and VEGF and the relationship between them in patients with stage 5 CKD.

2. Materials and Methods

2.1. Study Population

Sixty patients with stage 5 CKD and 30 healthy controls from The Second Affiliated Hospital of Shantou University Medical College, between March 21, 2019, and October 30, 2019, were included in this prospective cohort study. Stage 5 CKD was defined as an estimated glomerular filtration rate (eGFR) less than 15 mL/min/1.73m² [24]. The equation derived from the Chronic Kidney Disease Epidemiology Collaboration was used to calculate the eGFR [25]. The stage 5 CKD patients were divided into those not on dialysis (nondialysis group), those on hemodialysis (hemodialysis group), and those on peritoneal dialysis (peritoneal dialysis group). Stage 5 CKD patients with stable vital functions, who had been treated with haemodialysis or peritoneal dialysis for more than 3 months, were included in this study. The healthy control group comprised of people who visited our hospital’s medical examination center for a health examination. All works in this study were conducted in accordance with the Declaration of Helsinki (1964), and the study was approved by the ethics committee of The Second Affiliated Hospital of Shantou University Medical College (SUMC2H: 2019-1), and written informed consent was obtained from all the included participants.

2.2. Measurement of Parameters

The clinical testing parameters were identified and extracted prospectively. The serum NCOA1, NCOA2, NCOA3, HIF-1α, and VEGF levels were detected by the enzyme-linked immunosorbent assay method, and all the reagents were purchased from Abcam, Co., USA (HIF-1α: ab171577; VEFG: ab222510) and Dogesce (NCOA1: DG95699Q; NCOA2: DG95703Q; and NCOA3: DG95709Q). The absorbance of the samples of CKD patients and healthy controls was determined at 450 nm by an enzyme-labeling measuring instrument (Fermi automatic enzyme-linked immunosorber, Switzerland). The creatinine, blood urea nitrogen, white blood cells, red blood cells, hemoglobin, platelet (PLT), albumin, magnesium (Mg), procalcitonin (PCT), calcium (Ca), phosphorus, immunoreactive parathyroid hormone, B-type natriuretic peptide, high-sensitivity C reactive protein, and creatine kinase isoenzyme MB (CK-MB) values were also determined.

2.3. Statistical Analysis

This study used Statistical Package for the Social Sciences version 25.0 statistical software for statistical analysis. The measurement data were compared among the three subgroups of CKD patients. If the data were normally distributed, analysis of variance was used, and the data were expressed as the deviation. If the data were not distributed normally, then the Kruskal-Wallis test was used and the results were expressed in quartiles [P50 (P25, P75)]. Correlation analysis was used to analyze the correlation between NCOA1, NCOA2, NCOA3, HIF-1α, VEGF, and other indicators, while regression analysis was used to analyze the influencing factors of NCOA1, NCOA2, NCOA3, HIF-1α, and VEGF. The receiver operating characteristic (ROC) curve was used to analyze the indicators of NCOA1, NCOA2, NCOA3, HIF-1α, and VEGF status. The significance level for all tests was set at .

3. Results

3.1. Baseline Clinical Data

This study enrolled 60 stage 5 CKD patients and 30 healthy controls. The sex distribution of male/female in the CKD group and healthy control group was 48.33% and 50%, respectively. The mean age of the participants in the CKD and healthy control groups was and , respectively. The leading cause of CKD was glomerulonephritis (19/60, 31.67%), followed by diabetic nephropathy (16/60, 26.67%), hypertensive nephropathy (11/60, 18.33%), obstructive nephropathy (6/60, 10%), lupus nephritis (4/60, 6.67%), immunoglobulin A nephropathy (2/60, 3.33%), and light chain deposition-associated nephropathy (1/60, 1.67%). The CKD patient distribution according to (hemodialysis/peritoneal dialysis/nondialysis) status was 20/20/20.

3.2. Comparison of NCOA1, NCOA2, NCOA3, HIF-1α, and VEGF Levels between the CKD Group and the Healthy Control Group

The VEGF level in the CKD group was notably higher than that in the healthy control group (; Table 1). Furthermore, in the subgroup analysis of the CKD group, we found that the serum levels of VEGF in the patients of the nondialysis, hemodialysis, and peritoneal dialysis groups were notably higher than those in the healthy control group (; Table 1). However, no differences in the serum levels of NCOA1, NCOA2, NCOA3, and HIF-1α between the CKD group and the healthy control group were found. Even in the subgroup analysis, we did not find any difference in the above parameters between the healthy control group and the nondialysis, hemodialysis, and peritoneal dialysis groups (all ; Table 1).

3.3. Subgroup Analysis

3.3.1. Correlation Analysis between Circulating NCOA1 and Other Indicators in CKD Patients

According to the correlation analysis, Table 2 shows that circulating HIF-1α was positively associated with circulating NCOA1. There was a significant positive correlation between circulating NCOA2 and circulating NCOA1 and a notable positive correlation between circulating NCOA3 and circulating NCOA1 (Table 2). Ca was positively associated with circulating NCOA1 (Table 2). There was a negative correlation between PCT and circulating NCOA1; however, there were no positive correlations between circulating NCOA1 and other indicators (Table 2).

3.3.2. Correlation Analysis between Circulating NCOA2 and Other Indicators in CKD Patients

According to the correlation analysis, Table 3 shows that age was positively associated with circulating NCOA2. A positive association was observed between VEGF and NCOA2 in the serum, and circulating HIF-1α was notably associated with circulating NCOA2 (Table 3). There was a significant positive correlation between NCOA1 and NCOA2, as well as between NCOA3 and NCOA2 in the serum (Table 3). PLT was positively associated with NCOA2, but there was a negative correlation between PCT and NCOA2 in the serum (Table 3). CKMB was positively associated with NCOA2, but there were no positive correlations between NCOA2 and other indicators in the serum (Table 3).

3.3.3. Correlation Analysis between Circulating NCOA3 and Other Indicators in CKD Patients

According to the correlation analysis, Table 4 shows that HIF-1α was positively associated with NCOA3 in the serum. A significant positive correlation between NCOA1 and NCOA3 was found, along with a notable positive correlation between NCOA2 and NCOA3 in the serum (Table 4). However, there were no positive correlations between NCOA3 and other indicators in the serum (Table 4).

3.3.4. Correlation Analysis between HIF-1α and Other Indicators in the Serum of CKD Patients

According to the correlation analysis, Table 5 shows that NCOA1 was positively associated with HIF-1α in the serum. A significant positive correlation was found between NCOA2 and HIF-1α and between NCOA3 and HIF-1α in the serum (Table 5). However, there were no positive correlations between serum HIF-1α and other indicators (Table 5).

3.3.5. Correlation Analysis between VEGF and Other Indicators in the Serum of CKD Patients

According to the correlation analysis, it can be seen from Table 6 that age was positively associated with VEGF in the serum. There was a significant positive correlation between NCOA2 and VEGF, and a marked positive correlation was observed between PLT and VEGF in the serum (Table 6). Mg was positively associated with VEGF, and there was a notable positive correlation between PCT and VEGF in the serum (Table 6). However, there were no positive correlations between VEGF and other indicators in serum (Table 6).

3.4. ROC Analysis for CKD Disease

In this ROC curve analysis, the area under curve (AUC) for the VEGF curve was represented as 0.973, at (<0.05), which indicates that the ROC was statistically significant and the results had high accuracy. The sensitivity was 0.967, and the specificity was 0.893. Thus, the ability to identify patients correctly as ‘true positive and false positive was 96.7% and 89.3%, respectively (Figure 1).

(a)

(b)

Figure 1

ROC curve of indicators for CKD and predictive value of indicators. (a) ROC curve of indicators for CKD. (b) ROC curve analysis of predictive value of indicators for CKD. Note: according to the ROC curve analysis, it can be seen from the above table that the AUC is closer to 1 when the , which indicates that the diagnosis effect is better. AUC has a low accuracy at 0.5 to 0.7, AUC has a certain accuracy at 0.7 to 0.9, AUC has a high accuracy at above 0.9, and , indicating that the diagnostic method is completely ineffective and has no diagnostic value.

The AUC for the serum HIF-1α curve was 0.675, at (<0.05), indicating that the ROC was statistically significant and the results had lower accuracy. The sensitivity and specificity were 0.750 and 0.571, respectively, thus indicating that the ability to identify the true positives and false positives was 75.0% and 57.1%, respectively (Figure 1).

The AUC for the serum NCOA1 curve was 0.633 at (<0.05), indicating that the ROC was statistically significant and the results had lower accuracy. The sensitivity and specificity was 0.433 and 0.821, respectively. This indicates that the ability to correctly identify the true positive and false positive was 43.3%, and 82.1%, respectively (Figure 1).

The AUC for the serum NCOA2 curve was 0.682 at (<0.05), indicating that the ROC was statistically significant and the results had lower accuracy. The sensitivity was 0.367 and the specificity was 0.929, indicating that the ability to correctly identify true positive and false positive was 36.7% and 92.9%, respectively (Figure 1).

The AUC for serum NCOA3 was 0.663 at (<0.05), indicating that the ROC was statistically significant and the results had lower accuracy. The sensitivity and specificity was 0.450 and 0.857, respectively. This indicated that the ability to correctly identify the true positive and false positive was 45.0% and 85.7%, respectively (Figure 1).

4. Discussion

In this study, we found positive correlations between NCOA1, NCOA2, NCOA3, and HIF-1α in the serum. Additionally, NCOA2 also showed a positive correlation with VEGF in the serum. The results indicated that there might be a signal pathway of NCOAs (NCOA1, NCOA2, and NCOA3) HIF-1α in the serum of CKD disease. NCOAs play crucial roles in the regulation of downstream gene expression. No other study until now has explored the expression or role of NCOAs in renal diseases. Our study is the first to reveal that in CKD disease, the NCOA1, NCOA2, and NCOA3 were associated with HIF-1α in the serum. The HIF-1α has been reportedly associated with anemia, as well as with renal interstitial fibrosis and glomerulosclerosis. However, the role of HIF-1α in the renal fibrosis and extracellular matrix (ECM) accumulation has been conflicting. Ke et al. [26] demonstrated that mitochondrial uncoupling protein-2 regulates renal tubulointerstitial fibrosis by stimulating the accumulation of lipid deposition and ECM, and that HIF-1α inhibition by siRNA can suppress the accumulation of lipid and ECM, as well as suppression of collagen I and fibronectin expression in proximal renal tubular cells. Li et al. [27] used the method of weighted gene coexpression network analysis to link the renal tubulointerstitial gene expression profile of diabetic nephropathy to eGFR values and found that there was a negative correlation between HIF-1α and eGFR, as well as samples with increased HIF-1α expression were enriched with the signaling pathways of renal fibrosis. However, Fang et al. [28] reported that knockdown of HIF-1α or HIF-2α can inhibit the miR-29c expression in renal interstitial fibrosis in humans and rats and the activation of HIF-α can attenuate the renal fibrosis. However, in our study, we found that the difference in HIF-1α between CKD patients and healthy controls was not notable. More studies should be conducted to assess the detailed role of HIF-1α in renal disease.

However, we found that the serum levels of VEGF in the CKD group were higher than those of the healthy control group, and this indicated that VEGF could be a risk factor for CKD disease. We also found that the serum levels of VEGF in the nondialysis group, hemodialysis group, and peritoneal dialysis group were higher than those in the healthy control group. Deletion of VEGF in podocytes destroyed the maintenance of renal vascular integrity; on the other hand, increased levels of VEGF in podocytes can cause the glomerular basement membrane thickening and mesangial expansion [29]. Zuo et al. [30] reported that VEGF is the risk factor for the diabetic CKD. Anderson et al. [31] reported that plasma VEGF level increased in patients with CKD. However, Pfister et al. [32] reported that anti-VEGF therapy induced segmental glomerular capillary microaneurysms and segmental hyalinosis. Furthermore, Erturk et al. [33] included end-stage renal disease patients and healthy participants in their study and reported that the VEGF levels in the end-stage renal disease group were not significantly higher than those in the healthy control group. The result of our study was similar to that of the studies by Zou et al. [30] and Anderson et al. [31]. Interestingly, our study was the first to report that VEGF was positively associated with NCOA2 in CKD patients. We speculated that NCOA2, as a nuclear regulatory factor, could induce the VEGF expression, which can induce renal interstitial fibrosis and glomerulosclerosis. However, further studies should be conducted to investigate this relationship.

In previous reports, it was found that HIF-1α was an upstream signal factor for VEGF, and HIF-1α could induce the VEGF expression [22, 34, 35]. However, in this clinical study, we did not find this relationship. We speculated that the underlying diseases in CKD patients might affect these results, and a positive relationship could not be found. Future studies with larger sample sizes should be conducted to study this association.

In this study, there were some limitations. First, the sample size of this study was small; hence, subgroup analysis based on the causes of CKD could not be performed. Second, sources of NCOAs in blood of CKD patients remained unclear, but it is necessary to clarify any significance of NCOA expression in renal tissues. In this study, the renal biopsy was not performed because it was considered a nonessential inspection in stage 5 CKD patients. Furthermore, associations between HIF-1α and NCOAs in the kidney could not be effectively determined because renal biopsy was not performed. Using animal models with CKD could be a good choice to assess this relationship.

This study was also the first to report that NCOA1 and NCOA2 were negatively associated with PCT in the serum (a validation factor predicts bacterial infection). This probably shows that NCOA1 and NCOA2 might be protective factors against bacterial infection. However, there was rare evidence regarding this, and further research should be conducted to explore it.

In conclusion, we established an association between NCOA1, NCOA2, NCOA3, and HIF-1α in the serum, and the circulating VEGF in CKD patients was higher than that in healthy controls. However, more studies may be needed in the future.

Abbreviations

CKD:	Chronic kidney disease
NCOA1:	Nuclear receptor coactivator 1
NCOA2:	Nuclear receptor coactivator 2
NCOA3:	Nuclear receptor coactivator 3
HIF-1α:	Hypoxia-inducible factor-1α
VEFG:	Vascular endothelial growth factor
Cr:	Creatinine
BUN:	Blood urea nitrogen
WBC:	White blood cells
RBC:	Red blood cells
Hb:	Haemoglobin
PLT:	Platelet
Alb:	Albumin
Mg:	Magnesium
PCT:	Procalcitonin
Ca:	Calcium
P:	Phosphorus
PTH:	Immunoreactive parathyroid
BNP:	B-type natriuretic peptide
hsCRP:	High-sensitivity C reactive protein
CK-MB:	Creatine kinase isoenzyme MB.

Data Availability

All data generated of this study are included in the published article. The data used in this study are available on request from the corresponding author.

Ethical Approval

This study was approved by the ethics committee of The Second Affiliated Hospital, Shantou University Medical College. Written informed consent was given to all the participants prior to the collection of samples. This study has been performed in accordance with the ethical standards laid down in “Declaration of Helsinki 1964” and its later amendments or comparable ethical standards.

All authors consent to the submission of this manuscript to the BioMed Research International.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Acknowledgments

We would like to express our special appreciation for all patients participating in this study. This study was supported by the Natural Science Foundation of Guangdong Province (no. 2015A030310386), Guangdong Medical Science and Technology Research Fund Project (nos. A2018336 and B2020017), and Shantou Science and Technology Project (Shanfuke (2019) 106-4: 190606165268433).

References

S. J. Han, D. M. Lonard, and B. W. O’Malley, “Multi-modulation of nuclear receptor coactivators through posttranslational modifications,” Trends in Endocrinology and Metabolism, vol. 20, no. 1, pp. 8–15, 2009.
View at: Publisher Site | Google Scholar
M. J. Tetel, “Modulation of steroid action in the central and peripheral nervous systems by nuclear receptor coactivators,” Psychoneuroendocrinology, vol. 34, pp. S9–19, 2009.
View at: Publisher Site | Google Scholar
Y. A. Bulynko and B. W. O'Malley, “Nuclear receptor coactivators: structural and functional biochemistry,” Biochemistry, vol. 50, no. 3, pp. 313–328, 2011.
View at: Publisher Site | Google Scholar
K. D. Acharya, S. D. Finkelstein, E. P. Bless et al., “Estradiol preferentially induces progestin receptor-a (PR-A) over PR-B in cells expressing nuclear receptor coactivators in the female mouse hypothalamus,” eNeuro, vol. 2, no. 4, pp. ENEURO.0012–ENEU15.2015, 2015.
View at: Publisher Site | Google Scholar
S. Dasgupta, D. M. Lonard, and B. W. O'Malley, “Nuclear receptor coactivators: master regulators of human health and disease,” Annual Review of Medicine, vol. 65, no. 1, pp. 279–292, 2014.
View at: Publisher Site | Google Scholar
S. van der Laan, E. Golfetto, J. M. Vanacker, and D. Maiorano, “Cell cycle-dependent expression of Dub3, Nanog and the p160 family of nuclear receptor coactivators (NCoAs) in mouse embryonic stem cells,” PLoS One, vol. 9, no. 4, article e93663, 2014.
View at: Publisher Site | Google Scholar
M. M. Hossain, D. Barua, V. Arabkari, N. Islam, A. Gupta, and S. Gupta, “Hyperactivation of nuclear receptor coactivators induces PERK-dependent cell death,” Oncotarget, vol. 9, no. 14, pp. 11707–11721, 2018.
View at: Publisher Site | Google Scholar
Y. Wang, A. H. Moser, J. K. Shigenaga, C. Grunfeld, and K. R. Feingold, “Downregulation of liver X receptor-alpha in mouse kidney and HK-2 proximal tubular cells by LPS and cytokines,” Journal of Lipid Research, vol. 46, no. 11, pp. 2377–2387, 2005.
View at: Publisher Site | Google Scholar
L. Zhu, J. Mu, Y. Wu et al., “Role of HIF-1α in cold ischemia injury of rat donor heart via the miR-21/PDCD4 pathway,” Transplantation Proceedings, vol. 52, no. 1, pp. 383–391, 2020.
View at: Publisher Site | Google Scholar
X. Gai, P. Zhou, M. Xu, Z. Liu, X. Zheng, and Q. Liu, “Hyperactivation of IL-6/STAT3 pathway leaded to the poor prognosis of post-TACE HCCs by HIF-1α/SNAI1 axis-induced epithelial to mesenchymal transition,” Journal of Cancer, vol. 11, no. 3, pp. 570–582, 2020.
View at: Publisher Site | Google Scholar
J. Wan, X. Ling, Z. Rao, B. Peng, and G. Ding, “Independent prognostic value of HIF-1α expression in radiofrequency ablation of lung cancer,” Oncology Letters, vol. 19, no. 1, pp. 849–857, 2020.
View at: Publisher Site | Google Scholar
X. Hu, J. Lin, M. Jiang et al., “HIF-1α promotes the metastasis of esophageal squamous cell carcinoma by targeting SP1,” Journal of Cancer, vol. 11, no. 1, pp. 229–240, 2020.
View at: Publisher Site | Google Scholar
H. Huang, Y. Fan, Z. Gao et al., “HIF-1α contributes to Ang II-induced inflammatory cytokine production in podocytes,” BMC Pharmacology & Toxicology, vol. 20, no. 1, p. 59, 2019.
View at: Publisher Site | Google Scholar
X. Xu, K. You, and R. Bu, “Proximal tubular development is impaired with downregulation of MAPK/ERK signaling, HIF-1α, and catalase by hyperoxia exposure in neonatal rats,” Oxidative Medicine and Cellular Longevity, vol. 2019, Article ID 9219847, 16 pages, 2019.
View at: Publisher Site | Google Scholar
L. Lu, J. Li, Y. Le, and H. Jiang, “Inhibitor of growth 4 (ING4) inhibits hypoxia-induced EMT by decreasing HIF-1α and snail in HK2 cells,” Acta Histochemica, vol. 121, no. 6, pp. 695–703, 2019.
View at: Publisher Site | Google Scholar
B. Shen, M. Mei, Y. Pu et al., “Necrostatin-1 attenuates renal ischemia and reperfusion injury via meditation of HIF-1α/mir-26a/TRPC6/PARP1 signaling,” Molecular Therapy - Nucleic Acids, vol. 17, pp. 701–713, 2019.
View at: Publisher Site | Google Scholar
Y. Wang, L. Zhang, H. Jin, and D. Wang, “Based on HIF-1α/Wnt/β-catenin pathway to explore the effect of Qingshen granules on chronic renal failure patients: a randomized controlled trial,” Evidence-Based Complementary and Alternative Medicine, vol. 2019, Article ID 7656105, 7 pages, 2019.
View at: Publisher Site | Google Scholar
Y. He, X. Lang, D. Cheng, and Z. Yang, “Curcumin ameliorates chronic renal failure in 5/6 nephrectomized rats by regulation of the mTOR/HIF-1α/VEGF signaling pathway,” Biological & Pharmaceutical Bulletin, vol. 42, no. 6, pp. 886–891, 2019.
View at: Publisher Site | Google Scholar
L. Wang, R. P. Zhao, X. Y. Song, and W. F. Wu, “Targeting ERβ in macrophage reduces crown-like structures in adipose tissue by inhibiting osteopontin and HIF-1α,” Scientific Reports, vol. 9, no. 1, article 15762, 2019.
View at: Publisher Site | Google Scholar
A. L. Frump, M. Selej, J. A. Wood et al., “Hypoxia upregulates estrogen receptor β in pulmonary artery endothelial cells in a HIF-1α-dependent manner,” American Journal of Respiratory Cell and Molecular Biology, vol. 59, no. 1, pp. 114–126, 2018.
View at: Publisher Site | Google Scholar
L. Bialesova, L. Xu, J. Å. Gustafsson, L. A. Haldosen, C. Zhao, and K. Dahlman-Wright, “Estrogen receptor β2 induces proliferation and invasiveness of triple negative breast cancer cells: association with regulation of PHD3 and HIF-1α,” Oncotarget, vol. 8, no. 44, pp. 76622–76633, 2017.
View at: Publisher Site | Google Scholar
H. J. Kim, H. S. Cho, H. S. Ban, and H. Nakamura, “Suppression of HIF-1α accumulation by betulinic acid through proteasome activation in hypoxic cervical cancer,” Biochemical and Biophysical Research Communications, vol. 523, no. 3, pp. 726–732, 2020.
View at: Publisher Site | Google Scholar
J. Kim, S. E. Choi, K. H. Lee, H. J. Jeong, J. I. Shin, and B. J. Lim, “Tubulointerstitial infiltration of M2 macrophages in Henoch-Schönlein Purpura nephritis indicates the presence of glomerular crescents and bad clinical parameters,” BioMed Research International, vol. 2019, Article ID 8579619, 10 pages, 2019.
View at: Publisher Site | Google Scholar
S. A. Woolen, P. R. Shankar, J. J. Gagnier et al., “Risk of nephrogenic systemic fibrosis in patients with stage 4 or 5 chronic kidney disease receiving a group II gadolinium-based contrast agent: a systematic review and meta-analysis,” JAMA Internal Medicine, vol. 180, no. 2, pp. 223–230, 2019.
View at: Google Scholar
A. S. Levey, L. A. Stevens, C. H. Schmid et al., “A new equation to estimate glomerular filtration rate,” Annals of Internal Medicine, vol. 150, no. 9, pp. 604–612, 2009.
View at: Publisher Site | Google Scholar
Q. Ke, Q. Yuan, N. Qin et al., “UCP2-induced hypoxia promotes lipid accumulation and tubulointerstitial fibrosis during ischemic kidney injury,” Cell Death & Disease, vol. 11, no. 1, p. 26, 2020.
View at: Publisher Site | Google Scholar
X. Li, S. Yang, M. Yan et al., “Interstitial HIF1A induces an estimated glomerular filtration rate decline through potentiating renal fibrosis in diabetic nephropathy,” Life Sciences, vol. 241, article 117109, 2020.
View at: Publisher Site | Google Scholar
Y. Fang, X. Yu, Y. Liu et al., “miR-29c is downregulated in renal interstitial fibrosis in humans and rats and restored by HIF-α activation,” American Journal of Physiology-Renal Physiology, vol. 304, no. 10, pp. F1274–F1282, 2013.
View at: Publisher Site | Google Scholar
K. Tanabe, Y. Sato, and J. Wada, “Endogenous antiangiogenic factors in chronic kidney disease: potential biomarkers of progression,” International Journal of Molecular Sciences, vol. 19, no. 7, p. 1859, 2018.
View at: Publisher Site | Google Scholar
M. H. Zuo, J. Tang, M. M. Xiang et al., “Clinical observation of the reduced glutathione in the treatment of diabetic chronic kidney disease,” Journal of Cellular Biochemistry, vol. 120, no. 5, pp. 8483–8491, 2018.
View at: Publisher Site | Google Scholar
C. E. Anderson, L. L. Hamm, G. Batuman et al., “The association of angiogenic factors and chronic kidney disease,” BMC Nephrology, vol. 19, no. 1, p. 117, 2018.
View at: Publisher Site | Google Scholar
F. Pfister, K. Amann, C. Daniel, M. Klewer, A. Büttner, and M. Büttner-Herold, “Characteristic morphological changes in anti-VEGF therapy-induced glomerular microangiopathy,” Histopathology, vol. 73, no. 6, pp. 990–1001, 2018.
View at: Publisher Site | Google Scholar
I. Erturk, F. Yesildal, R. Acar, T. Ozgurtas, and K. Saglam, “Vascular endothelial growth factor and soluble vascular endothelial growth factor receptor-1 in patients with end-stage renal disease. Associations with laboratory findings, comorbidities, and medications,” Saudi Medical Journal, vol. 39, no. 6, pp. 586–591, 2018.
View at: Publisher Site | Google Scholar
X. Tian, N. Zhou, J. Yuan et al., “Heat shock transcription factor 1 regulates exercise-induced myocardial angiogenesis after pressure overload via HIF-1α/VEGF pathway,” Journal of Cellular and Molecular Medicine, vol. 24, no. 3, pp. 2178–2188, 2020.
View at: Publisher Site | Google Scholar
S. Liang, K. Ren, B. Li et al., “LncRNA SNHG1 alleviates hypoxia-reoxygenation-induced vascular endothelial cell injury as a competing endogenous RNA through the HIF-1α/VEGF signal pathway,” Molecular and Cellular Biochemistry, vol. 465, no. 1-2, pp. 1–11, 2020.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Tianbiao Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

317

Downloads

961

Citations