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Article

The Influence of CD28 Gene Polymorphism in Transfusion Reaction after Transfusing Leukoreduced Blood Components

by
Ding-Ping Chen
1,2,3,*,
Wei-Tzu Lin
1,
Wei-Ting Wang
1 and
Tzong-Shi Chiueh
1
1
Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
2
Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan 33302, Taiwan
3
Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(3), 877; https://doi.org/10.3390/jcm9030877
Submission received: 3 February 2020 / Revised: 6 March 2020 / Accepted: 16 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue New Frontiers in Molecular Hematology)
Review Reports Versions Notes

Abstract

:
CTLA-4 and CD28 belong to co-stimulation molecules, the abnormal expression of which can regulate the T cell activation and then affect the degree of immune response. Moreover, blood transfusion reaction (TR) is a kind of immune reaction. Consequently, the hypothesis of this study was that the TR still occurred after transfusing leukoreduced blood components as a result of the sensitivity of immune system, and a small number of immune stimulations were enough to induce an immune response in patients. There were 38 cases and 36 healthy controls included in this study. The selected CD28 gene were sequenced to search single nucleotide polymorphism (SNPs), and the correlation between TR and these SNPs was analyzed. According to our data, only the rs3181097 (promoter, −1059) of CD28 gene polymorphism was associated with TR. The p value of rs3181097 under the co-dominant model was 0.031. GG was used as a reference genotype, the p value of GG vs. AG was 0.010 (OR: 0.210, 95% CI: 0.062–0.712), and GG vs. AG + AA was 0.028 (OR: 0.292, 95% CI: 0.095–0.901). In addition to CTLA-4, CD28 gene was also a crucial SNP in TR, because there was a signification for the haplotype with Grs3181097 (p = 0.015). Consequently, we suggested that the TR was related to CD28 by regulating the degree of immune response.

1. Introduction

Transfusion reaction (TR) is a kind of immune reaction, which is defined as the adverse reaction after transfusion, including of fever, allergy, dyspnea, and so on [1]. There are many causes for TR, which may be induced by red blood cell antigen (ABO antigen), white blood cell antigen (HLA antigen), platelet antigen (HPA antigen) or cytokines (IL-1, IL-6, and so on) [2,3]. When heteroantigen is first recognized by immune system, the antigen-specific antibody will be produced rapidly against the same antigen, as it is recognized again. Therefore, TRs are more common in patients with multiple transfusions [4] and in multiparous women [5]. The TR can develop even after removal of these possible factors by crossmatching and using pre-storage leukocyte-reduced components before infusing. It might be caused by other host factors in blood components, and a few molecules are enough to trigger an immune response.
The effectiveness of hematopoietic stem cell transplantation (HSCT) could be affected by the abnormal expression of CTLA-4 and CD28 [6]. Moreover, hematopoietic stem cells (HSCs) have an ability to differentiate into all the blood cell types [7]. In other words, the components in the blood product are mature cells that are differentiated from HSCs. In addition, both transplantation and blood transfusion inject allogeneic cells into patients. Therefore, we speculated that the mechanism of TR and transplantation rejection was similar. Furthermore, CTLA-4 and CD28 belong to co-stimulation molecules, which regulate the T cell activation and then affect the degree of immune response [8]. Consequently, we thought the expression of CTLA-4 and CD28 should also be related to TR. The hypothesis of this study was that TR still occurred after transfusing leukoreduced blood components as a result of the excessive immune response in patients, and a small number of stimulations could trigger an immune response.
Our previous study indicated that CTLA-4 gene polymorphism was associated with TR after the infusion of leukoreduced blood components [9]. Furthermore, the common points of CD28 and CTLA-4 genes are located in the 2q33, as immune regulator, mutually homologous, and have the same ligands (B7 family) [10]. T cell homeostasis was influenced by an imbalanced expression level of these two molecules [11]. Moreover, there were several autoimmune diseases and cancers simultaneously associated with CTLA-4 and CD28 genes. For example, type 1 diabetes was related to both the CD28 gene [12] and the CTLA-4 gene [13], as well as rheumatoid arthritis [14,15] and cancer risk in Asians [16,17]. Consequently, we conjectured that CD28 might be a genetic risk factor of alloimmunization and associated with TR, besides CTLA-4.
Comparing CD28 and CTLA-4, CD28 is continuously expressed on the surface of naïve and activated T cells [18], which functions to promote T cell activation and increase antibody production of B lymphocytes [19,20]. CD28-deficient mice were unable to activate T cells even when stimulated by antigens and anti-CD3 monoclonal antibodies [21] and resulted in T lymphocyte tolerance and anergy [22]. CTLA-4 is continuously expressed on the surface of regulatory T cells (Treg), and its expression on activated T cell is induced by CD28 and the T cell receptor signal [23], which functions to inhibit T cell activation [24]. CTLA-4-deficient mice might develop large-scale lymphoproliferative diseases or spontaneous autoimmune diseases, and die in 4–5 weeks [25,26].
In addition, the fetal blood enters into the maternal circulation and they achieve mutual tolerance and immune balance during pregnancy [27]. When this immune balance was destroyed, it would cause spontaneous abortion [28]. One of the reasons for spontaneous abortion was regulated by immune-related genes [29]. There were several studies indicating that spontaneous abortion was related to CTLA-4 and CD28 polymorphisms and serum level [30,31,32,33]. To protect the fetus during pregnancy, maternal alloimmunity reactions would be inhibited by the mother’s own Treg in opposition to paternal antigens among embryo cells [34]. Moreover, CD28 was the major factor to promote the proliferation of Treg [11]. Additionally, TR is also an immune response caused by allogeneic blood, which supported us to infer that TR was also related to CD28. Therefore, the aim of the study was to identify the relationship between CD28 genotype and TR and further explore the mechanism of blood TR caused by gene mutation.

2. Experimental Section

2.1. Study Subjects

The study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (CGMH) with the approval ID of 201901246B0. There were 74 participants included in this study at Linkou CGMH from March 2019 to January 2020. The 38 patients were divided into test groups, who had TR after infusing leukoreduced blood products (leukocyte-poor red blood cell, LPR, or leukocyte-poor platelet, LPP). The patients with cancer or autoimmune disease or immune suppression drugs or the use of steroids were not excluded. The diagnostic criteria of TRs were set according to the Hemovigilance Module Surveillance Protocol of National Healthcare Safety Network (NHSN) Biovigilance Component [35]. Another 36 participants were healthy volunteers without autoimmune disease and cancer.

2.2. DNA Extraction

The genomic DNA was extracted from peripheral blood of patients and health controls, and a QIAamp DNA Mini kit (Qiagen GmbH, Hilden, Germany) was used according to the manufacturer’s instructions. DNA concentration was assessed through a UV spectrometer, and the purity of DNA was evaluated by measuring the optical density at 260 and 280 nm.

2.3. PCR Amplification

Analysis of the correlation between TR and SNPs of CD28 was performed in the Department of Laboratory Medicine at Linkou CGMH. The PCR primers used in this study are shown in Table 1. We only focused on the promoter and intron 3 region of CD28, because we wanted to investigate whether the expression level of CD28 was associated with TR, and rs3116496 in the intron 3 region of CD28 was a hotspot in literature. The 25 μL of PCR reaction volume contained 1 μL of DNA, 10 μL of HotStarTaq DNA Polymerase (Qiagen GmbH, Hilden, Germany), 1 μL of 10 μM forward primer, 1 μL of 10 μM reverse primer, and 12 μL of ddH2O. The PCR program was 95 °C for 3 min at initial denaturation step and 30 cycles of 95 °C for 30 sec, 58 °C for 30 sec, and 72 °C for 2 min. The final extension step was 10 min at 72 °C and finally soaking at 10 °C indefinitely. For gel electrophoresis visualization, 5 μL of the PCR products was pipetted onto a 1.5% agarose gel and conducted at 100 V for 30 min, and then subjected to UV illumination to ensure the correctness of PCR products.

2.4. Purification and SNP Analysis

The enzyme containing 2.5 µL shrimp alkaline phosphatase and 0.05 µL exonuclease I (New England Biolabs, UK) was used to purify PCR products, and then the PCR products were sequenced by using ABI PRISM 3730 DNA analyzer (Applied Biosystems, Foster City, CA, USA). The SNP analysis was performed on the promoter and intron 3 region of CD28, wherein rs1879877, rs3181096, rs3181097, rs3181098, rs56228674, and rs3116496 were selected for genotyping. The data of SNP genotyping were not all available because of unclear sequencing information.

2.5. Statistical Analysis

The Hardy–Weinberg equilibrium (HWE) was conducted for the control group to reduce sampling bias in case–control studies. The statistical correlation of allele frequency, genotype frequency, and haplotype was analyzed by SPSS 17.0 through chi-square test and Fisher’s exact test. Haplotype analysis included four SNPs of the CTLA-4 gene (rs4553808, rs62182595, rs16840252, and rs5742909), which were related to TR [9], and the meaningful CD28 SNP in this study.

3. Results

The characteristics of participants are shown in Table 2. The average age of patients was 54.3 years old, and the gender ratio was 27:11, in which women accounted for 71%. A febrile non-hemolytic transfusion reaction (FNHTR) (67.8%) constituted the majority of TR in the study. Furthermore, there were 17 of 38 patients transfused with LPR, and the others were transfused with LPP. In which, there were two patients who simultaneously had an allergic reaction and FNHTR in a transfusion. The average age of the control group was 22.5 years, and women accounted for 62%.
The selected CD28 gene SNPs (rs1879877, rs3181096, rs3181097, rs3181098, and rs3116496) were genotyped in patients and healthy controls. All SNPs complied with HWE, p > 0.05, and there was no SNP showing differences in allele frequency between patients with TR and healthy controls (Table 3).
All genotypes of CD28 were not significantly related to TR, except rs3181097. The p value of rs3181097 under co-dominant model (GG vs. AG vs. AA) was 0.031. GG was used as a reference genotype, and the p value of GG vs. AG was 0.010 (OR: 0.210, 95% CI: 0.062–0.712), and GG vs. AG + AA was 0.028 (OR: 0.292, 95% CI: 0.095–0.901). The complete data are shown in Table 4.
Haplotype analysis was conducted with blood transfusion-related SNPs of CTLA-4 gene in our previous study [9] and the statistically significant SNP of CD28 in this study (Table 5). The p value was 0.007 when the haplotype contained only those four CTLA-4 SNPs (OR: 4.571, 95% CI: 1.417–14.748). Adding the SNP of CD28 into the haplotype, it was statistically significance when rs3191097 had a G allele (p = 0.015, OR: 2.591, 95% CI: 1.197–5.606). However, it was not associated with TR when rs3181097 had an A allele, p = 0.718.

4. Discussion

The co-stimulation pathway is a central factor for inducing effective antigen-specific immune response [36]. Leukoreduced components will be used when the patient has had multiple transfusions or TR before. Moreover, multiple transfusions may produce antibodies other than anti-ABO antibodies and anti-HLA antibodies. Our results showed that most of the TR were FNHTR. The main causes of FNHTR are HLA antigen and cytokines released by leukocytes [37]. The number of leukocytes <5 × 106/unit could effectively prevent the generation of alloimmunity [38]. However, some cases have FNHTR even after reducing the number of leukocytes and cytokines by using pre-storage leukoreduced components [39]. That might be explained by the leukoreduced blood products still containing a small number of leukocytes and cytokines [40]. The number of leukocytes in LPR was about 1 × 105/unit after filtering by a Cellselect filter and the number of leukocytes in LPP was about 0.4 × 105/unit after filtering by a cotton-wool (Imugard) filter [40]. Therefore, it was indicated that TRs after transfusing leukoreduced components were related to the sensitivity of immune system, and a small number of immune-stimulations were enough to trigger an immune response in the patient [41].
Combining with the results of a previous study [9], p value was 0.007 (OR: 4.571, 95% CI: 1.417–14.748) when the haplotype contained only CTLA-4 SNPs (Ars4553808Grs62182595Crs16840252Crs5742909). While adding CD28 SNP into this haplotype, the p value was significantly statistical different when allele of rs3181097 was G (p = 0.015), but A was not (p = 0.718). Therefore, it was represented that the rs3181097 of CD28 gene also was a crucial SNP in TR.
The SNP of rs4553808 (-1661) in the CTLA-4 promoter region would affect the transcriptional binding activity for CCAAT/enhancer-binding protein beta (c/EBPβ) and nuclear factor I and influence the transcription level of the CTLA-4 gene [42], and the SNP of rs5742909(-318) was associated with the expression of CTLA-4 [43] too. However, rs16840252 (-1146) could be affected by rs4553808 or rs5742909 of CTLA-4 gene, because there was a strong linkage disequilibrium between them [44]. The SNP of rs3181097 (-1059) in the promoter region of CD28 was related to the clinical pathology of childhood IgA nephropathy in Korean ethnic groups [44]. In the position of rs3181097, specificity protein 1 (SP1) and c/EBPα transcription factors could bind to the A-containing sequence, but only SP1 could bind to the G-containing sequence [45]. Consequently, the SNP of rs3181097 would affect the transcription factor binding sites and directly or indirectly changing the expression of CD28 and regulated T cell-mediated immune response [46] then resulting in TR.
These SNPs (rs4553808, rs62182595, rs16840252, rs5742909, and rs3181097) are all in the promoter region of the gene, and gene variation in the promoter region would affect the expression level [47]. Hence, the mechanism of TR caused by these SNPs might result from the expression level of CTLA-4 and CD28. However, the function of rs62182595 and rs3181097 SNP has not been investigated yet. Consequently, the mechanism of TR caused by rs3181097 was not affirmed. Moreover, the blood product contained more than one factor that could result in TR and the patient’s physical condition was different, so the TR was regulated by multiple factors.

5. Conclusions

The gene polymorphisms of CD28 and CTLA-4 were both associated with TR, and it might be due to the regulation of T cell activation and immune response. In the future, the effect of the SNP on CD28 and CTLA-4 expression should be further investigated by cell model study, and the biochemical pathways of TR caused by the expression of CD28 and CTLA-4 should also be investigated. These efforts could effectively prevent the occurrence of TR and improve the safety of blood transfusion. However, the limitation of this study was that the patients with cancer, autoimmune disease, or drug used were not exclude.

Author Contributions

D.-P.C. conceived and designed the experiments and wrote the draft of the manuscript. W.-T.L. performed the experiments. W.-T.W. reviewed and approved the final draft. T.-S.C. analyzed the data and contributed reagents/materials/analysis tools. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Chang Gung Memorial Hospital grants CMRPG3F0442, CMRPG3I0261, and BMRPA81 to D.-P.C.

Acknowledgments

The excellent consulting assistance and sample resources from the blood bank of Chang Gung Memorial Hospital are gratefully acknowledged.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Suddock, J.T.; Crookston, K.P. Transfusion Reactions. In StatPearls; StatPearls Publishing LLC: Treasure Island, FL, USA, 2019; Available online: https://www.ncbi.nlm.nih.goTv/books/NBK482202/ (accessed on 2 March 2020).
  2. Sahu, S.; Hemlata Verma, A. Adverse events related to blood transfusion. Indian J. Anaesth. 2014, 58, 543–551. [Google Scholar] [CrossRef] [PubMed]
  3. Chang, C.C.; Lee, T.C.; Su, M.J.; Lin, H.C.; Cheng, F.Y.; Chen, Y.T.; Yen, T.H.; Chu, F.Y. Transfusion-associated adverse reactions (TAARs) and cytokine accumulations in the stored blood components: The impact of prestorage versus poststorage leukoreduction. Oncotarget 2017, 9, 4385–4394. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Mempel, W.; Böck, M. Substitution of thrombocyte concentrates in polytransfused patients. BeitrInfusionsther 1993, 31, 81–85. [Google Scholar] [PubMed]
  5. Cerci Gurbuz, B.; Soyoz, M.; Ozkale Okyay, D.; Kilicaslan Ayna, T.; Pirim, I. Comparison of anti-HLA antibody production according to gestational periods in pregnant women. Transplant. Proc. 2017, 49, 464–466. [Google Scholar] [CrossRef]
  6. Ramzi, M.; Iravani Saadi, M.; Yaghobi, R.; Arandi, N. Dysregulated Expression of CD28 and CTLA-4 Molecules in Patients with Acute Myeloid Leukemia and Possible Association with Development of Graft versus Host Disease after Hematopoietic Stem Cell Transplantation. Int. J. Organ. Transplant. Med. 2019, 10, 84–90. [Google Scholar] [PubMed]
  7. Laurenti, E.; Göttgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 2018, 553, 418–426. [Google Scholar] [CrossRef]
  8. Brunner-Weinzierl, M.C.; Hoff, H.; Burmester, G.R. Multiple functions for CD28 and cytotoxic T lymphocyte antigen-4 during different phases of T cell responses: Implications for arthritis and autoimmune diseases. Arthritis Res. Ther. 2004, 6, 45–54. [Google Scholar] [CrossRef] [Green Version]
  9. Wen, Y.H.; Lin, W.T.; Wang, W.T.; Chiueh, T.S.; Chen, D.P. Association of CTLA4 Gene Polymorphism with Transfusion Reaction after Infusion of Leukoreduced Blood Component. J. Clin. Med. 2019, 8, 1961. [Google Scholar] [CrossRef] [Green Version]
  10. Sansom, D.M. CD28, CTLA-4 and their ligands: Who does what and to whom? Immunology 2000, 101, 169–177. [Google Scholar] [CrossRef]
  11. Holt, M.P.; Punkosdy, G.A.; Glass, D.D.; Shevach, E.M. TCR Signaling and CD28/CTLA-4 Signaling Cooperatively Modulate T Regulatory Cell Homeostasis. J. Immunol. 2017, 198, 1503–1511. [Google Scholar] [CrossRef]
  12. Zouidi, F.; Stayoussef, M.; Bouzid, D.; Fourati, H.; Abida, O.; Ayed, M.B.; Kammoun, T.; Hachicha, M.; Penha-Gonçalves, C.; Masmoudi, H. Contribution of PTPN22, CD28, CTLA-4 and ZAP-70 variants to the risk of type 1 diabetes in Tunisians. Gene 2014, 533, 420–426. [Google Scholar] [CrossRef]
  13. Chen, Y.; Chen, S.; Gu, Y.; Feng, T.J.; Shi, Y.; Fu, Q.; Wang, Z.X.; Cai, Y.; Dai, H.; Zheng, S.; et al. CTLA-4 +49 G/A, a functional T1D risk SNP, affects CTLA-4 level in Treg subsets and IA-2A positivity, but not beta-cell function. Sci. Rep. 2018, 8, 1–8. [Google Scholar] [CrossRef]
  14. Kim, Y.O.; Kim, H.J.; Kim, S.K.; Chung, J.H.; Hong, S.J. Association of the CD28/CTLA4/ICOS polymorphisms with susceptibility to rheumatoid arthritis. Clin. Chem. Lab. Med. 2010, 48, 345–353. [Google Scholar] [CrossRef]
  15. Liu, C.P.; Jiang, J.A.; Wang, T.; Liu, X.M.; Gao, L.; Zhu, R.R.; Shen, Y.; Wu, M.; Xu, T.; Zhang, X.G. CTLA-4 and CD86 genetic variants and haplotypes in patients with rheumatoid arthritis in southeastern China. Genet. Mol. Res. 2013, 12, 1373–1382. [Google Scholar] [CrossRef]
  16. Zeng, Y.; Lai, N. Association between the CD28 c.17 +3 T>C Polymorphism (rs3116496) and Cancer Risk: An Updated Meta-Analysis. Med. Sci. Monit. 2019, 25, 1917–1927. [Google Scholar] [CrossRef]
  17. Yan, Q.; Chen, P.; Lu, A.; Zhao, P.; Gu, A. Association between CTLA-460G/A and 1661A/G polymorphisms and the risk of cancers: A meta-analysis. PLoS ONE 2013, 8, e83710. [Google Scholar] [CrossRef]
  18. Porciello, N.; Tuosto, L. CD28 costimulatory signals in T lymphocyte activation: Emerging functions beyond a qualitative and quantitative support to TCR signalling. Cytokine Growth Factor Rev. 2016, 28, 11–19. [Google Scholar] [CrossRef]
  19. Njau, M.N.; Jacob, J. The CD28/B7 pathway: A novel regulator of plasma cell function. Adv. Exp. Med. Biol. 2013, 785, 67–75. [Google Scholar] [CrossRef]
  20. Rozanski, C.H.; Arens, R.; Carlson, L.M.; Nair, J.; Boise, L.H.; Chanan-Khan, A.A.; Schoenberger, S.P.; Lee, K.P. Sustained antibody responses depend on CD28 function in bone marrow–resident plasma cells. J. Exp. Med. 2011, 208, 1435–1446. [Google Scholar] [CrossRef]
  21. Green, J.M.; Noel, P.J.; Sperling, A.I.; Walunas, T.L.; Gray, G.S.; Bluestone, J.A.; Thompson, C.B. Absence of B7-dependent responses in CD28-deficient mice. Immunity 1994, 1, 501–508. [Google Scholar] [CrossRef]
  22. Appleman, L.J.; Boussiotis, V.A. T cell anergy and costimulation. Immunol. Rev. 2003, 192, 161–180. [Google Scholar] [CrossRef]
  23. Finn, P.W.; He, H.; Wang, Y.; Wang, Z.; Guan, G.; Listman, J.; Perkins, D.L. Synergistic induction of CTLA-4 expression by costimulation with TCR plus CD28 signals mediated by increased transcription and messenger ribonucleic acid stability. J. Immunol. 1997, 158, 4074–4081. [Google Scholar] [PubMed]
  24. Walunas, T.L.; Lenschow, D.J.; Bakker, C.Y.; Linsley, P.S.; Freeman, G.J.; Green, J.M.; Thompson, C.B.; Bluestone, J.A. CTLA-4 can function as a negative regulator of T cell activation. Immunity 1994, 1, 405–413. [Google Scholar] [CrossRef]
  25. Tivol, E.A.; Borriello, F.; Schweitzer, A.N.; Lynch, W.P.; Bluestone, J.A.; Sharpe, A.H. Loss of CTLA-4 leads to massive lymphoproliferation and fatal multiorgan tissue destruction, revealing a critical negative regulatory role of CTLA-4. Immunity 1995, 3, 541–547. [Google Scholar] [CrossRef] [Green Version]
  26. Waterhouse, P.; Penninger, J.M.; Timms, E.; Wakeham, A.; Shahinian, A.; Lee, K.P.; Thompson, C.B.; Griesser, H.; Mak, T.W. Lymphoproliferative disorders with early lethality in mice deficient in Ctla-4. Science 1995, 270, 985–988. [Google Scholar] [CrossRef]
  27. Spina, V.L.; Aleandri, V.; Pacchiarotti, A.; Salvi, M. Immune tolerance in pregnancy. Maternal-fetal interactions. Minerva Ginecol. 1998, 50, 533–537. [Google Scholar] [PubMed]
  28. Zhao, X.; Jiang, Y.; Wang, L.; Li, Z.; Li, Q.; Feng, X. Advances in Understanding the Immune Imbalance between T-Lymphocyte Subsets and NK Cells in Recurrent Spontaneous Abortion. Geburtshilfe Frauenheilkd. 2018, 78, 677–683. [Google Scholar] [CrossRef] [Green Version]
  29. Vaiman, D. Genetic regulation of recurrent spontaneous abortion in humans. Biomed. J. 2015, 38, 11–24. [Google Scholar] [CrossRef]
  30. Li, L.; Liu, J.; Qin, S.; Li, R. Correlation between CTLA4 genetic polymorphisms, its serum protein level and the susceptibility to recurrent spontaneous abortion: A case-control study. Medicine 2018, 97, e12754. [Google Scholar] [CrossRef]
  31. Rasti, Z.; Nasiri, M. Association of the +49 A/G Polymorphism of CTLA4 Gene with Idiopathic Recurrent Spontaneous Abortion in Women in Southwest of Iran. J. Reprod. Infertil. 2016, 17, 151–156. [Google Scholar] [PubMed]
  32. Wang, G.; Sun, J. Interactive Effects of Snps Located Within CD28/B7Pathway and Environment on Susceptibility to Recurrent Spontaneous Abortion. Cell. Physiol. Biochem. 2017, 43, 2185–2199. [Google Scholar] [CrossRef] [Green Version]
  33. Misra, M.K.; Singh, B.; Mishra, A.; Agrawal, S. Co-stimulatory CD28 and transcription factor NFKB1 gene variants affect idiopathic recurrent miscarriages. J. Hum. Genet. 2016, 61, 1035–1041. [Google Scholar] [CrossRef]
  34. Zenclussen, A.C.; Gerlof, K.; Zenclussen, M.L.; Sollwedel, A.; Bertoja, A.Z.; Ritter, T.; Kotsch, K.; Leber, J.; Volk, H.D. Abnormal T-cell reactivity against paternal antigens in spontaneous abortion: Adoptive transfer of pregnancy-induced CD4+CD25+ T regulatory cells prevents fetal rejection in a murine abortion model. Am. J. Pathol. 2005, 166, 811–822. [Google Scholar] [CrossRef]
  35. NHSN Manual: Biovigilance Component Protocol April 2018. Available online: https://www.cdc.gov/nhsn/pdfs/biovigilance/bv-hv-protocol-current.pdf (accessed on 10 December 2019).
  36. Sharpe, A.H. Mechanisms of costimulation. Immunol. Rev. 2009, 229, 5–11. [Google Scholar] [CrossRef] [Green Version]
  37. Goodnough, L.T.; Riddell, J.; Lazarus, H.; Chafel, T.L.; Prince, G.; Hendrix, D.; Yomtovian, R. Prevalence of platelet transfusion reactions before and after implementation of leukocyte-depleted platelet concentrates by filtration. Vox Sang. 1993, 65, 103–107. [Google Scholar] [CrossRef]
  38. Tayler, V.V. (Ed.) AABB Technical Manual, 13th ed.; AABB: Bethesda, MD, USA, 1999; pp. 175–176. [Google Scholar]
  39. Rajesh, K.; Harsh, S.; Amarjit, K. Effects of Prestorage Leukoreduction on the Rate of Febrile Nonhemolytic Transfusion Reactions to Red Blood Cells in a Tertiary Care Hospital. Ann. Med. Health Sci. Res. 2015, 5, 185–188. [Google Scholar] [CrossRef] [Green Version]
  40. Vakkila, J.; Myllylä, G. Amount and type of leukocytes in leukocyte-free red cell and platelet concentrates. Vox Sang. 1987, 53, 76–82. [Google Scholar] [CrossRef]
  41. Brodin, P.; Davis, M.M. Human immune system variation. Nat. Rev. Immunol. 2017, 17, 21–29. [Google Scholar] [CrossRef]
  42. Wang, X.B.; Pirskanen, R.; Giscombe, R.; Lefvert, A.K. Two SNPs in the promoter region of the CTLA-4 gene affect binding of transcription factors and are associated with human myasthenia gravis. J. Intern. Med. 2008, 263, 61–69. [Google Scholar] [CrossRef]
  43. Ligers, A.; Teleshova, N.; Masterman, T.; Huang, W.X.; Hillert, J. CTLA-4 gene expression is influenced by promoter and exon 1 polymorphisms. Genes Immun. 2001, 2, 145–152. [Google Scholar] [CrossRef] [Green Version]
  44. Zou, C.; Qiu, H.; Tang, W.; Wang, Y.; Lan, B.; Chen, Y.C. TLA4 tagging polymorphisms and risk of colorectal cancer: A case–control study involving 2306 subjects. Onco Targets Ther. 2018, 11, 4609–4619. [Google Scholar] [CrossRef] [Green Version]
  45. Kim, H.J.; Chung, J.H.; Kang, S.; Kim, S.K.; Cho, B.S.; Kim, S.D.; Hahn, W.H. Association of CTLA4, CD28 and ICOS gene polymorphisms with clinicopathologic characteristics of childhood IgA nephropathy in Korean population. J. Genet. 2011, 90, 151–155. [Google Scholar] [CrossRef]
  46. Lenschow, D.J.; Walunas, T.L.; Bluestone, J.A. CD28/B7 system of T cell co-stimulation. Annu. Rev. Immunol. 1996, 14, 233–258. [Google Scholar] [CrossRef]
  47. Mikhaylichenko, O.; Bondarenko, V.; Harnett, D.; Schor, I.E.; Males, M.; Viales, R.R.; Furlong, E.E. The degree of enhancer or promoter activity is reflected by the levels and directionality of eRNA transcription. Genes Dev. 2018, 32, 42–57. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. The CD28 primers used in this study. F: forward primer; R: reverse primer.
Table 1. The CD28 primers used in this study. F: forward primer; R: reverse primer.
SequenceGC CountTmbp
Promoter
F: 5′- GGG TGG TAA GAA TGT GGA TGA ATC-3′24/11 46%63.6 °C1542
R: 5′-CAA GGC ATC CTG ACT GCA GCA-3′21/12 57%63.2 °C
Intron3
F: 5′- AAG GAT GCA GTT TAG GGT CTA GAT T -3′25/10 40%62.5 °C886
R: 5′-GAT CAA GCC AAC ATT GTC CAT TGG-3′24/11 46%63.6 °C
GC-content was defined as count (G + C)/ count (A + T); Tm was defined as the temperature at half the time the double helix structure of DNA degrades; bp: base pairs.
Table
Table 2. Characteristics of patients (n = 38) with transfusion reaction (TR) after transfusing leukoreduced blood components and healthy controls (n = 36).
Table 2. Characteristics of patients (n = 38) with transfusion reaction (TR) after transfusing leukoreduced blood components and healthy controls (n = 36).
Patients, No. (%)Controls, No. (%)
Average age of patients54.3 ± 3 22.5 ± 0.96
Gender (female)7162
Type of blood transfusion
Leukocyte-poor platelet 17
Leukocyte-poor red blood cell21
Type of transfusion reaction
Allergic reaction15 (39.5)
Febrile non-hemolytic transfusion reaction (FNHTR)25 (65.8)
Both allergic reaction and FNHTR2 (5.3)
Table
Table 3. Hardy–Weinberg equilibrium (HWE) in control group, allele frequencies and odds ratio for TR in patients and controls.
Table 3. Hardy–Weinberg equilibrium (HWE) in control group, allele frequencies and odds ratio for TR in patients and controls.
SNPPositionAlleleMinor Allele FrequencyHWE p ValueOdds Ratio 95% CIpa Value
PatientControl
rs1879877203705277G/T0.3940.3470.8891.226 (0.628–2.393)0.550
(−1198)
rs3181096203705369C/T0.2760.2080.9071.582 (0.732–3.417)0.241
(−1106)
rs3181097203705416G/A0.4000.5000.2300.667 (0.330–1.345)0.257
(−1059)
rs3181098203705655G/A0.2570.2080.9071.313 (0.607–2.840)0.489
(−820)
rs3116496203729789T/C0.1570.1430.6151.156 (0.457–2.923)0.759
(IVS3 +17)
SNP: single nucleotide polymorphism; HWE: Hardy–Weinberg equilibrium; 95 % CI: 95% confidence interval; pa values were counted from chi-square test or Fisher’s exact test.
Table
Table 4. Genotypes of CD28 SNPs and their correlations with risk of TR.
Table 4. Genotypes of CD28 SNPs and their correlations with risk of TR.
SNPGenotypeOR (95% CI)p Value
Promoter
rs1879877GG vs. GT vs. TT 0.714
GGref.ref.
GT1.715 (0.452–6.506)0.426
TT 1.600 (0.430–5.958)0.482
GT + TT1.653 (0.486–5.628)0.418
rs3181096CC vs. CT vs. TT 0.647
CCref.ref.
CT0.773 (0.285–2.094)0.612
TT0.457 (0.076–2.755)0.667
CT + TT0.698 (0.275–1.776)0.450
rs3181097GG vs. AG vs. AA 0.031
GGref.ref.
AG 0.210 (0.062–0.712)0.010
AA0.519 (0.134–2.018)0.341
AG + AA0.292 (0.095–0.901)0.028
rs3181098GG vs. AG vs. AA 0.651
GGref.ref.
AG0.957 (0.345–2.652)0.932
AA0.478 (0.079–2.879)0.668
AG + AA0.829 (0.322–2.133)0.697
Intron3
rs3116496TT vs. CT vs. CC 0.601
TTref.ref.
CT0.987 (0.356–2.708)0.972
CC NA0.491
CT + CC1.080 (0.398–2.927)0.880
Bold type in p value means that it had significant difference between patients and healthy controls, p < 0.05. OR: odds ratio; CI: confidence interval.
Table
Table 5. The haplotype analysis of CTLA-4 (rs4553808, rs62182595, rs16840252, rs5742909) and CD28 (rs3181097) gene SNPs.
Table 5. The haplotype analysis of CTLA-4 (rs4553808, rs62182595, rs16840252, rs5742909) and CD28 (rs3181097) gene SNPs.
HaplotypeOR (95% CI)p Value
Only CTLA-4
AGCC4.571 (1.417–14.748)0.007
Combine CTLA-4 and CD28
AGCCG2.591 (1.197–5.606)0.015
AGCCA0.866 (0.397–1.889)0.718
OR: odds ratio, CI.: confidence interval. Bold type: significant difference between patients and healthy controls, p < 0.05.

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MDPI and ACS Style

Chen, D.-P.; Lin, W.-T.; Wang, W.-T.; Chiueh, T.-S. The Influence of CD28 Gene Polymorphism in Transfusion Reaction after Transfusing Leukoreduced Blood Components. J. Clin. Med. 2020, 9, 877. https://doi.org/10.3390/jcm9030877

AMA Style

Chen D-P, Lin W-T, Wang W-T, Chiueh T-S. The Influence of CD28 Gene Polymorphism in Transfusion Reaction after Transfusing Leukoreduced Blood Components. Journal of Clinical Medicine. 2020; 9(3):877. https://doi.org/10.3390/jcm9030877

Chicago/Turabian Style

Chen, Ding-Ping, Wei-Tzu Lin, Wei-Ting Wang, and Tzong-Shi Chiueh. 2020. "The Influence of CD28 Gene Polymorphism in Transfusion Reaction after Transfusing Leukoreduced Blood Components" Journal of Clinical Medicine 9, no. 3: 877. https://doi.org/10.3390/jcm9030877

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