RNA-sequence reveals differentially expressed genes affecting the secondary hyperparathyroidism

doi:10.21203/rs.3.rs-3567207/v1

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RNA-sequence reveals differentially expressed genes affecting the secondary hyperparathyroidism

https://doi.org/10.21203/rs.3.rs-3567207/v1

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Background/aim

Secondary hyperparathyroidism (SHPT) is a common complication of chronic kidney disease (CKD), the mechanisms of cell proliferation leading to which has not been fully understood in recent years. And this study investigated the potential genes of tissue proliferation in secondary hyperparathyroidism.

Materials and methods

RNA sequencing (RNA-Seq) analysis was conducted to explore the potentially related genes of SHPT. Patients with secondary hyperparathyroidism indicated for surgical parathyroidectomy in our institution were reviewed and the parathyroid glands were removed for RNA-Seq analysis to measure the differential changes of RNA expression. Furthermore, we validated the differential genes expression by quantitative real-time PCR (qRT-PCR) in tissue samples and enzyme-linked immunosorbent assay (ELISA) in the serum of patients with or without SHPT.

Results

Our study identified 24 DEGs by RNA-Seq, among which 16 were upregulated and 8 were downregulated. Through the qRT-PCR, we further found that U2AF1L5, LTBP2, RGN in the hyperplasia group were significantly downregulated and RAP1GAP2 was significantly upregulated compared to the control group. And only Serum U2AF1L5 concentrations in patients with SHPT were significantly higher than those in healthy controls (0.360.27ng/ml vs 0.150.07ng/ml, P<0.05) as ELISA showed.

Conclusion

The genes including LTBP2, RGN, RAP1GAP2 and U2AF1L5 differentially expressed in tissues and serum in patients with SHPT.

secondary hyperparathyroidism

RNA-Sequencing

Gene expression

U2AF1L5

Secondary hyperparathyroidism (SHPT), as a serious complication of chronic kidney disease (CKD) which occurs early and continues to progress as renal function declines, is associated with severe consequences such as abnormalities in regulation of mineral metabolism, bone fracture and the propensity to vascular and soft tissue calcifications, all of which has a serious impact on cardiovascular or all-cause mortality^[1–4]. About 20%-80% patients with chronic kidney disease of all severities were found to have SHPT^[5]. The management of SHPT can be extremely challenging. At present, calcimimetics such as cinacalcet and vitamin D receptor activators (VDRAs) like calcitriol are the key strategies in the management of SHPT and patients are referred to a surgeon when considered having failed medical management severe SHPT. In spite of recent advances in therapy, medical or surgical treatment failure still occurs in a significant number of patients who have a worse prognosis.

There is a progression from diffuse to nodular hyperplasia in parathyroid glands (PTGs). In patients with hyperparathyroidism, the PTGs grow diffusely and polyclonally in early and then followed by nodular and monoclonal transformation of cells^[6]. In CKD patients, SHPT is characterized by elevated PTH secretion and parathyroid gland hyperplasia which are caused by multiple factors, including hypocalcemia, hyperphosphatemia, vitamin D deficiency, reduced density of vitamin D receptor (VDR) and calcium-sensing receptor (CaR)^[7]. But the mechanisms of cell proliferation leading to SHPT has not been fully understood in recent years. Our understanding of the molecular mechanisms underlying the development of SHPT is rather limited, relying mainly on imaging and hemodynamic assessment of the condition^{[8, 9]}. Therefore, a better understanding of disease-related changes at the molecular level is necessary in order to find potential targets for intervention or treatment.

Previous studies have focused on biological indicators of serum proteins, which have the drawbacks of poor stability and the need for fresh specimens with short storage times. In recent years, by integrating data from multiple centers for research from databases with lower cost and higher accuracy, the RNA sequencing (RNA-seq) has been widely used for researches on the mechanism of diseases and circular RNAs (circRNAs), which have tissue specificity and existing in the collected blood samples stably, are expected to be suitable biomarkers for diagnosis of diseases^[10–12]. RNA-seq has also reshaped biomedical research by analyzing a vast range of biological data, the application of which has increased diagnostic rates by 8–36% and improved the understanding of molecular pathological and genetic mechanisms^{[13, 14]}.

In our study, in order to explore the potentially related genes of SHPT, we first quantified the differential changes of RNA expression in tissue of parathyroid from SHPT patients who underwent parathyroidectomy by RNA sequencing (RNA-Seq) and the selected genes were then verified by quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA). We intended to reveal the potential markers for early diagnosis of SHPT which might be considered as drug treatment targets with further study.

Patients and Tissue Sample Collection

The ESRD patients with secondary hyperparathyroidism indicated for surgical parathyroidectomy in our institution, the First Affiliated Hospital, College of Medicine, Zhejiang University, between 1 February 2021 and 30 April 2021 were enrolled as participants in this study. The Inclusion criteria were as follows: (1) the age was \(\ge\) 18 years old; (2) Patients with end-stage renal disease conforming to surgical indications for hyperparathyroidism. The exclusion criteria were as follows: (1) Patients underwent secondary surgery; (2) Patients underwent kidney transplant before.

The indications of the surgery referred to the 2009 KDIGO guidelines were as follows: (1) Intact PTH > 800 pg/ml; (2) patients with uncontrolled hypercalcemia or/and hyperphophatemia who resistant to medication such as active vitamin D; (3) symptomatic patients (e.g. bone pain, pruritus and other relative symptoms affecting the quality of life); (4) patients had at least one enlarged parathyroid gland with a diameter larger than 1 cm and abundant blood flow signals that was clearly shown on ultrasound.

Surgical procedures were performed by dedicated surgeons. Patients underwent parathyroidectomy and parathyroid glands were removed, the pieces of tissue of which were immediately immersed in RNAlater Stabilization Solution (Ambion™) at 4ฒC and stored at -80ฒC until use for subsequent genetic analysis. The remaining tissue was used to extract RNA for subsequent tests.

RNA-Seq Experiments and Data Analysis

RNA-seq was examined by Beijing CapitalBio Corporation (Beijing, China). The image data of the sequenced fragments were converted into sequence numbers (reads) via Illumina base recognition to generate a file in FASTQ format. Clean data (clean reads) were obtained by removing reads containing adapters, reads containing poly-N and low-quality reads from raw data. All downstream analyses were based on clean data with high quality. Basic analysis mainly included expression analysis and differential gene analysis. Differential gene expression analysis was performed for the identification by R LIMMA package. Genes with the cut-off value of adjusted p-value < 0.05 and absolute log2 fold change (FC) > 1 were considered as significant differentially expressed genes (DEGs). The roles of the genes in the pathways were analyzed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) database.

RNA Extraction, cDNA Synthesis, and Real time Quantitative PCR

PBMCs RNA was prepared from small portions of parathyroid gland tissue sample using the TRIzol^@ regent (Invitrogen, CA, USA). PrimeScipt™ ⅡReverse Transcriptase (Takara, Shiga, Japan) was used to reverse transcribe 500ng of RNA to cDNA for each sample. Before qPCR, we diluted first-strand cDNA synthesis products 1:4 for each sample. A two-step PCR reaction was performed as follows: pre-denaturation, 95°C for 30 seconds; PCR reaction, denaturation at 95°C for 5 s, annealing at 60°C for 31 s for 40 cycles; dissociation at 95°C for 15 s, 60°C for 1 min, and 95°C for 15 s. The primer sequences used are shown in Supplementary table 1. The relative mRNA expression of target genes was normalized to ACTIN in the same sample. Real time qPCR reactions were carried out using SYBR Green reagent and the CFX96™ Real-Time PCR Detection System (Bio-rad, CA, USA). Results were expressed as fold change in expression, and values were calculated as the ratio of target gene expression to control gene expression.

Serum sampling and measurement of serum concentrations of gene expressions

Serum samples were obtained from another group of patients with secondary hyperparathyroidism before the time of surgery and also from patients underwent maintenance hemodialysis in our institution as controls. Samples were stored at -80°C for analysis. All serum concentrations of gene expressions were quantitated using ELISA including U2AF1L5 (abx552022, abbexa), LTBP2 (LS-F4878-1, Lifespan), RGN (LS-F12500-1, Lifespan) and RAP1GAP2 (MBS9364174, MYBiosource).

The studies involving human participants were approved by the Research Ethics Board of the First Affiliated Hospital of Zhejiang University (IIT20230208A). All patients who participated in the current study provided written informed consent. The study protocol is performed in accordance with the relevant guidelines.

Statistical Analysis

Normally distributed variables were expressed as mean ± SD and skewed data are expressed as median (interquartile range); and categorical variables are expressed as frequency. Relative gene expression levels were calculated vis the 2^−∆∆Ct method, using SPSS 26 (IBM) to estimate the correlations between the mRNA expression levels of DEGs. The clinical diagnostic value of DEGs for SHPT was assessed using the area under the curve (AUC) obtained from the ROC curve analysis. Statistical analysis and relevant charts were performed using R version 4.0.4, SPSS 26 (IBM), and GraphPad Prism 8.4.2. P < 0.05 was considered statistically significant.

Table 1

The primer sequences used in the quantitative polymerase chain reactions
Gene Name	Forward primer	Reverse primer
RGN	AAGATTGAGTGTGTTTTGCCAGA	GTCTACAAAGAGCAGAGAGTTGG
IGHG1	GCAGAGCATCACAACGTCAGC	GAGCTAGTCCTGCTCACCTTC
LRP2	GGCCTGCTATAACACCAGTCA	ACTCATTGTGCAAGCATATCTCA
AOX1	TGTCGATCCTGAAACAATGCTG	GGTGATGGGGTTGTATCGTGA
APOD	GAATCAAATCGAAGGTGAAGCCA	ACACGAGGGCATAGTTCTCAT
LTBP2	AGCACCAACCACTGTATCAAAC	CTCATCGGGAATGACCTCCTC
SMOC2	TTCTCGGCGCTCACGTTTTT	GTTGAAATTCACAACGGGAAAGG
ADH1B	GGGGCTGTTTATGGTGGCTT	ACGTCAGGACGGTACGGATA
PTPRN2	GCAATGGACTTTTACCGCTACG	CCTCAGGTAGGTTTTCGGGAG
PCDHA10	AGTGCTTGTTCTGCGGAAG	TCTGTCAAAGATAGGGGCGTT
C7	CTGAGTGGAAATGTCCTGTCC	CGCTTCCGACTAGATGATGTGT
RIPOR3	CCTGTCTGCCACCAAAGGTGA	TTTGCAGGCATTCGCGATCT
ABCA8	TTCATGTTGGCATTTGACACTTG	GGATCGGCATCCATTTCATCT
FMO2	GGAGTGTGGAGGTTCAAAGAG	TGCTGGTGTTGGTAACGACAG
SBSPON	AACTACCTCTGCATTCAACAAGG	TCCATACAGTATCCAGCATCCTC
U2AF1L5	CCGACGTTTAGCCAGACCAT	CCCAGGTTGTCACAGACGTT

Patient characteristics and the Research Design Summary

A total of 17 patients with secondary hyperparathyroidism underwent parathyroidectomy were in our cohort. Table 2 shows the baseline demographics and clinical characteristics of patients. The mean age of patients was 40.00 ± 9.65 years, and the majority 65% were male. All patients received hemodialysis and 16 patients (94%) had the number parathyroid gland resection ≥ 4. All patients were divided into three groups for further experimental analysis according to the parathyroid hyperplasia removed intraoperatively judging from the same experienced surgeons. Group 1 (the Self-control Group, n = 9): the hyperplasia of all parathyroid tissues removed was significantly different and the two tissues with the largest differences were selected as the hyperplasia group and the control group. Group 2 (the Hyperplasia Group, n = 5): All the excised parathyroid tissues hyperplasia obviously and the most obvious hyperplasia was taken. Group 3 (the Non-hyperplasia Group, n = 3): The hyperplasia of parathyroid tissue was not obvious in all excised parathyroid tissues, and the least hyperplasia was selected. We selected three self-control groups from group 1 for RNA sequencing analysis and completed subsequent related experiments according to the sequencing results further. A flow diagram showing the research design is shown in Fig. 1.

Table 2

Demographic and clinical characteristics of patients with secondary hyperparathyroidism
	All patients (n = 17)
Age (years)	49.00 ± 9.65^a
Sex, n (%)
Man, n (%)	11 (65)^b
Dialysis, n (%)
Hemodialysis	17 (100)
Parathyroid glands, n (%)
<4	1 (6)
\(\ge 4\)	16 (94)
Laboratory Values
Ca²⁺ (mmol L^− 1)	2.43 ± 0.23
P (mmol L^− 1)	1.99 ± 0.46
PTH (ng L^− 1)	1389 (1009,2065)^c
Alp (U L^− 1)	390 (130,698)
Cr (mmol L^− 1)	749 (628,986)
Hb (g L^− 1)	105.65 ± 14.40
Alb (g L^− 1)	41.01 ± 6.10
PTH, parathyroid hormone; AlP, alkaline phosphatase; Cr, creatinine; Hb, hemoglobin; Alb, albumin ^aAverage ± SD; ^bNumbers (frequency); ^cMedian (quarter, three quarters)

Identification of DEGs and Validation by qRT-PCR

We performed RNA-seq analysis on three self-control groups of tissue samples. In three self-control groups, the hyperplasia group included: G1, G3, and G5; the control group included: G2, G4, and G6. The expression profile of RNA in parathyroid gland tissue was compared to controls both of which were obtained from patients with secondary hyperparathyroidism using a genome-wide analysis. As shown in Fig. 2, the volcano plots presented all the expressed genes in each self-control group as the red (up regulated) and blue (down regulated) dots mean that the genes have significant difference (P-value ≤ 0.05 and│log2(fold change) │≥1). The roles of the genes in the pathways of above three self-control groups were also presented in GO enrichment and KEGG database. We showed the top ten enriched GO pathways in each control group and there was no KEGG pathway enriched of the downregulated DEGs (Fig. 2d-f).

Venn diagrams were used to show the number of up-and down-regulated differential genes in the three groups (Fig. 3a-b). Finally, we found that 8 DEGs were downregulated and 16 were upregulated which are shared in all groups. The cluster analysis showed that there were significant differences between the hyperplasia group and the control group while the DEGs were also shown in the heat map that the up-regulated mRNA expression was represented by red color and down-regulated was blue (Fig. 4c-d). We listed 24 candidate genes for further analysis to validate by qRT-PCR, which was performed in tissue samples from the three categories mentioned above (Group 1, Group 2, and Group 3). Samples from group 1 (n = 8, one sample was ruled out as ineligible) were compared by own control, while samples from groups 2 (n = 5) and 3 (n = 3) were compared between the two groups. Through the above comparison, we found that, as shown in Fig. 4e, U2AF1L5, LTBP2, RGN in the hyperplasia group were significantly downregulated and RAP1GAP2 was significantly upregulated compared to the control group.

Serum U2AF1L5, LTBP2, RGN and RAP1GAP2 in patients with secondary hyperparathyroidism

ELISA had been used to detect the concentrations of U2AF1L5, LTBP2, RGN and RAP1GAP2 in the serum of patients with or without SHPT. Serum samples were obtained from 14 patients with secondary hyperparathyroidism and 26 controls, the baseline characteristics of which were summarized in Table 3. As shown in table, the PTH and Alp was higher in the secondary hyperparathyroidism group whereas Hb and UA was significantly lower. And no significant differences in age, gender ratio and dialysis vintage were observed between the two groups. There were no significant differences in serum concentrations of LTBP2, RGN and RAP1GAP2 between the two groups except U2AF1L5. Serum U2AF1L5 concentrations in patients with SHPT were significantly higher than those in healthy controls (Fig. 4; 0.36\(\pm\)0.27ng/ml vs 0.15\(\pm\)0.07ng/ml, P<0.05).

Table 3

Demographic and clinical characteristics of the patients of SHPT group and the control group
	The SHPT Group (n = 14)	The Control Group (n = 26)
Age (years)	52.71 ± 14.19^a	54.28 ± 6.28
Sex, n (%)
Man, n (%)	6 (43)^b	10 (40)
Dialysis vintage (year)	7.50 ± 3.16	7.00 ± 2.10
Laboratory Values
Ca²⁺ (mmol L^− 1)	2.36 ± 0.24	2.36 ± 0.16
P (mmol L^− 1)	1.83 ± 0.70	1.68 ± 0.40
Parathyroid hormone (ng L^− 1)	2143.00 (880.50, 2552.00)^c	172.00 (115.00, 211.00)^**
Alp (U L^− 1)	524.00 (319.50, 715.50)	89.00 (63.00, 103.00)^**
Cr (mmol L^− 1)	617.50 (495.00, 827.00)	1011.00 (869.00, 1061.00)^**
Hb (g L^− 1)	97.07 ± 13.11	115.88 ± 9.92^*
Alb (g L^− 1)	37.84 ± 3.77	40.10 ± 2.60
BUN (mmol L^− 1)	18.12 ± 7.51	26.82 ± 7.50^**
UA (umol L^− 1)	371.43 ± 85.07	456.64 ± 121.86^**
PTH, parathyroid hormone; AlP, alkaline phosphatase; Cr, creatinine; Hb, hemoglobin; Alb, albumin; UA, Uric acid; BUN, blood urea nitrogen ^aAverage ± SD; ^bNumbers (frequency); ^cMedian (quarter, three quarters); ^P < 0.05; ^*P < 0.01

The development of SHPT which is a common consequence in CKD patients is influenced by a variety of factors. The elevated concentration of extracellular phosphate and decreased of ionized calcium, a result of low calcitriol levels and decreased kidney function, leads to SHPT which is characterized by elevated PTH secretion and parathyroid gland hyperplasia. Meanwhile, in response to systemic hypocalcemia, PTH production increases mediated by the CaR. It has been found that when the expression of CaR and VDR was assessed, the presence of both receptors was significantly reduced in parathyroid glands from patients with SHPT compared to normal individuals^[15]. The down-regulation of CaR and VDR is associated with parathyroid cell proliferation, which may limit the ability of calcium and calcitriol to regulate PTH secretion as SHPT progresses^[16–18]. In addition, the disturbance of fibroblast growth factor 23 (FGF23)-Klotho axis also affects PTH synthesis^[19]. A correlation between high levels of FGF-23 and increased mortality in dialysis patients and refractory SHPT patients has also been reported^[20].

At present, the mechanism of cell proliferation in SHPT is not completely clear. We have used the RNA-Seq technique to analyze the differentially expressed genes in parathyroid gland tissue of the patients who underwent parathyroidectomy. In our study, we constructed a 4-mRNA signature considering U2AF1L5, LTBP2, RGN and RAP1GAP2. The signature could divide secondary hyperparathyroidism patients into high-risk and low-risk groups with different prognoses and to the best of our knowledge, no studies evaluating above genes as biomarkers for SHPT have been reported.

LTBP2, an extracellular matrix (ECM) protein with a molecular weight of 195–240 kDa, is part of the Latent TGFβ1-Binding Protein family which have been shown to participate in the regulation of TGFβ signaling. But unlike LTBP1, LTBP3, and LTBP4, the function of LTBP2 has not been fully clarified. Several studies suggested that LTBP2 which had an indirect role in the storage and activation of TGF-β may be associated with fibrosis or tissue remodeling in several organs, including the skin, heart, and kidneys^[21–23]. The results from Park S, et al. supported the possibility of LTBP2 being used as a marker for myocardial fibrosis^[24]. In addition, Toshihide et al.^[25] reported that LTBP2 can be used to evaluate acute-phase and ongoing fibrogenesis and they also found the upregulation of LTBP2 induced by TGF-β1 at the mRNA and protein levels in human lung myofibroblasts was consistent with a previous study which suggested that Ras or another related small GTP-binding protein in the signaling pathway might be involved^[26].

Regucalcin (RGN) otherwise known as the senescence marker protein 30 (SMP30) is a calcium-binding protein which preferentially expressed in cytosol of hepatocytes and renal tubular epithelia and plays a crucial role in protective effects against apoptosis and oxidative stress. In some previous studies, the anti-RGN antibody level was considered a biomarker for diagnosing hepatocellular carcinoma (HCC) especially for the patients with AFP negative while the expression of RGN decreases in HCC-infected tissues but increases in adjacent cancerous tissues^{[27, 28]}. RGN, functioning as a tumor suppressor, could protect cells from various injuries by enhancement of membrane calcium-pump activity thus rescue effect from cell death caused by calcium influx and effectively inhibit cell proliferation by suppressing DNA and RNA synthesis in liver proliferative cells^[29–31].

Rap1 is a guanine nucleotide binding protein that regulates integrin function and plays a key role in adhesion of many cell types^[32–34]. Rap1GAP2, the name of which means RAP1 GTPase activating protein 2, is a modular protein encoding a GTPase-activating protein that activates the small guanine-nucleotide-binding protein Rap1 in platelets and regulates secretion of dense granules from platelets at sites of endothelial damage. Smolenski et al. demonstrated that Rap1GAP2, or rather at least part of this C-terminal region, is involved in protein-protein interactions. Then found that Rap1GAP2 interacts with Slp1 and the binding of them increases the release of dense granule^[35]. The 14-3-3 proteins also interact with phosphorylated serine 9 at the N terminus of Rap1GAP2, the progress of which is enhanced by ADP and thrombin and disrupted by endothelium-derived factors nitric oxide and prostacyclin^[36].

We then compared the serum concentrations of the four differentially identified genes between two groups of patients with secondary hyperparathyroidism and controls. The serum U2AF1L5 concentrations in patients with SHPT were significantly higher than those in healthy controls. Few studies related to the gene of U2AF1L5 have been reported. Gao et al. reported that U2AF1L5 was one of the 4-mRNA signature which could predict the prognosis of nasopharyngeal carcinoma patients and furthermore, the signature was closely associated with cell proliferation and the immune response^[37]. U2AF1L5 was also found one of the most up-regulated 10 genes which were regarded as hearing loss related genes and enriched in pathways related to apoptosis such as tumor necrosis factor^[38]. In our study, we found that the U2AF1L5 was downregulated in the parathyroid tissue samples and upregulated in the serum compared to the control group.

Our study has several limitations. Firstly, we had a relatively small number of samples available for clinical validation due to the limited availability of positive specimens. Secondly, we did not further investigate the mechanism of the genes and pathways involved. These can be future research topics.

Through RNA-Seq and verified by qRT-PCR and ELISA, we finally identified the genes including LTBP2, RGN, RAP1GAP2 and U2AF1L5 differentially expressed in tissues and serum in patients with SHPT.

Acknowledgments

None.

Authors Contributions

Research idea and study design: Ping Zhang, Hong Jiang; Sample collection and testing: Siyu Chen, Yang Chen, Bin Pan, Wenjun Zhao; data analysis/interpretation: Siyu Chen, Yang Chen; supervision or mentorship: Jianghua Chen. Ping Zhang takes responsibility that this study has been reported honestly, accurately and transparently, and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Funding

None.

Availability of data and materials

All data generated or analyzed during this study are included in this published article. Further information can be obtained from the corresponding author on reasonable request.

Ethics approval and consent to participate

The study was conducted according to the guidelines of the declaration of Helsinki, and approved by the Research Ethics Board of the First Affiliated Hospital of Zhejiang University (IIT20230208A). All procedures were carried out in accordance with the Helsinki Declaration.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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RNA-sequence reveals differentially expressed genes affecting the secondary hyperparathyroidism

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Abstract

Background/aim

Materials and methods

Results

Conclusion

Figures

Introduction

Methods

Patients and Tissue Sample Collection

RNA-Seq Experiments and Data Analysis

RNA Extraction, cDNA Synthesis, and Real time Quantitative PCR

Serum sampling and measurement of serum concentrations of gene expressions

Statistical Analysis

Results

Patient characteristics and the Research Design Summary

Identification of DEGs and Validation by qRT-PCR

Serum U2AF1L5, LTBP2, RGN and RAP1GAP2 in patients with secondary hyperparathyroidism

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1