Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec 1:13:1025681.
doi: 10.3389/fimmu.2022.1025681. eCollection 2022.

STXBP3 and GOT2 predict immunological activity in acute allograft rejection

Qinfan Yao  1   2   3   4 , Cuili Wang  1   2   3   4 , Yucheng Wang  1   2   3   4 , Wenyu Xiang  1   2   3   4 , Yin Chen  1   2   3   4 , Qin Zhou  1   2   3   4 , Jianghua Chen  1   2   3   4 , Hong Jiang  1   2   3   4 , Dajin Chen  1   2   3   4
Affiliations

STXBP3 and GOT2 predict immunological activity in acute allograft rejection

Qinfan Yao et al. Front Immunol. .

Abstract

Background: Acute allograft rejection (AR) following renal transplantation contributes to chronic rejection and allograft dysfunction. The current diagnosis of AR remains dependent on renal allograft biopsy which cannot immediately detect renal allograft injury in the presence of AR. In this study, sensitive biomarkers for AR diagnosis were investigated and developed to protect renal function.

Methods: We analyzed pre- and postoperative data from five databases combined with our own data to identify the key differently expressed genes (DEGs). Furthermore, we performed a bioinformatics analysis to determine the immune characteristics of DEGs. The expression of key DEGs was further confirmed using the real-time quantitative PCR (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunohistochemical (IHC) staining in patients with AR. ROC curves analysis was used to estimate the performance of key DEGs in the early diagnosis of AR.

Results: We identified glutamic-oxaloacetic transaminase 2 (GOT2) and syntaxin binding protein 3 (STXBP3) as key DEGs. The higher expression of STXBP3 and GOT2 in patients with AR was confirmed using RT-qPCR, ELISA, and IHC staining. ROC curve analysis also showed favorable values of STXBP3 and GOT2 for the diagnosis of early stage AR.

Conclusions: STXBP3 and GOT2 could reflect the immunological status of patients with AR and have strong potential for the diagnosis of early-stage AR.

Keywords: GOT2; STXBP3; acute rejection; expression; kidney transplantation.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart depicting the overall study design. A 58-gene dataset was aligned with those expressed by 64 immune cells in xCell and the key genes were identified. Fifteen key DEGs were identified for further validation (KIF3B, FH, EIF4G1, SMARCD1, ITGAL, HNRNPUL1, MAP4K5, ELP3, AVIL, HNRNPL, PRPF19, GOT2, STXBP3, CLIC3, and PPM1G).
Figure 2
Figure 2
Construction of Venn diagrams for key DEGs identification in AR and NAR group. First method for identifying 28 co-expressed genes. The intersection of GSE112927 and GSE120396 yielded the first group of 198 co-expressed genes. The 357 co-expressed genes at the intersection of our center and GSE120649, 758 co-expressed genes at the intersection of our center and GSE131179, and 257 co-expressed genes at the intersection of our center and GSE145503 were obtained. The following three groups of genes were then intersected with the first group of 198 co-expressed genes. Three groups of co-expressed genes were merged to obtain 28 co-expressed genes (FH, TKT, SMARCD1, CLIC3, XRCC6, KHSRP, PPP1R3B, TOE1, ZFAND6, ITGAL, KIF3B, EIF4G1, FGFRL1, PRPF19, CD58, NABP1, PPM1G, TBCD, CCDC92, MDH2, PDIA4, SMARCAL1, GOT2, DNAJC11, MED24, BCL6, IFRD1, and STXBP3).
Figure 3
Figure 3
Cell type enrichment analysis of six RNA sequencing datasets. (A) GSE145503, (B) GSE131179, (C) GSE120649, (D) GSE120396, (E) GSE112927, and (F) our center) were performed using the xCell website. The x-axis lists the 64 cell types, and the y-axis depicts the xCell enrichment score (FDR < 0.1) in the AR group compared to the NAR group.
Figure 4
Figure 4
Bioinformatics analysis of our center RNA sequencing dataset. (A) The expression levels of STXBP3, GOT2, and MAP4K5 illustrated using a heatmap. (B) GO analysis of DEGs in our center dataset. (C) KEGG pathway analysis of DEGs in our center dataset.
Figure 5
Figure 5
Validation of three key DEGs with RT-qPCR, ELISA, and IHC staining. (A–C) STXBP3, GOT2, and MAP4K5 expression was measured in 10 healthy controls, four patients with without AR episodes (NAR), and 10 patients with acute rejection (AR). (A) The expression of STXBP3 was compared between healthy controls, patients with NAR, and patients with AR (n = 24). (B) The expression of GOT2 was compared between healthy controls, patients with NAR, and patients with AR (n = 24). (C) The expression of MAP4K5 was compared between healthy controls, patients with NAR, and patients with AR (n = 24). (D–F) ELISA validation of STXBP3 and GOT2 expression in four patients with NAR and 10 patients with AR. (G, H) ROC curve was constructed to estimate the diagnostic power of STXBP3 and GOT2 for early AR. STXBP3: AUC = 0.989 (p < 0.0001), cut-off value = 7.840, sensitivity: 0.929, specificity: 0.944; GOT2: AUC = 0.966 (p < 0.0001), cut-off value = 13.147, sensitivity: 0.929, specificity: 0.889; and the combination of STXBP3 and GOT2: AUC = 1.000(p < 0.0001). (I, J) Immunohistochemical staining of kidney tissues showed that both STXBP3 and GOT2 were increased in AR group compared with that in NAR group. The scale bars in i–j = 100 μm.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Lee Y, Kwon S, Moon JJ, Han K, Paik NJ, Kim WS. The effect of health-related behaviors on disease progression and mortality in early stages of chronic kidney disease: A Korean nationwide population-based study. J Clin Med (2019) 8:1100. doi: 10.3390/jcm8081100 - DOI - PMC - PubMed
    1. Dai Q, Zhang Y, Liao X, Jiang Y, Lv X, Yuan X, et al. . Fluorofenidone alleviates renal fibrosis by inhibiting necroptosis through RIPK3/MLKL pathway. Front Pharmacol (2020) 11:534775. doi: 10.3389/fphar.2020.534775 - DOI - PMC - PubMed
    1. Shehata M, Mahmoud A, Soliman A, Khalifa F, Ghazal M, Abou El-Ghar M, et al. . 3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary. PLoS One (2018) 13:e0200082. doi: 10.1371/journal.pone.0200082 - DOI - PMC - PubMed
    1. Knobbe TJ, Douwes RM, Kremer D, Swarte JC, Eisenga MF, Gomes-Neto AW, et al. . Altered gut microbial fermentation and colonization with methanobrevibacter smithii in renal transplant recipients. J Clin Med (2020) 9:518. doi: 10.3390/jcm9020518 - DOI - PMC - PubMed
    1. Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. . Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med (1999) 341:1725–30. doi: 10.1056/nejm199912023412303 - DOI - PubMed

Publication types