摘要
目前,肾脏移植已成为所有终末期肾病(ESRD)患者首选的最理想的肾脏替代疗法。而同种异体移植排斥反应仍是目前器官移植领域面临的最大难题。肾移植面临的排斥反应,包括超急性排斥反应(HAR)、加速性排斥反应、急性排斥反应(AR)和慢性排斥反应(CR)。其中,急性排斥反应是肾移植术后的主要并发症。另外,它也是导致慢性排斥反应或慢性移植肾病(CAN)、慢性移植物失功的重要的危险因素。急性和慢性排斥反应成为影响器官移植近期和远期效果的最主要原因。
除了急性和慢性排斥反应,肾移植还要面临超急性排斥反应和加速性排斥反应的危险,这两种排斥反应都与免疫高致敏有关。免疫高致敏患者即患者体内预存有较高水平的群体反应性抗体(PRA)。 目前根据PRA值把患者分为轻度致敏(<10%)、中度致敏(10%-50%)和高度致敏(>50%)。高敏患者移植危险性较高,术中或术后超急性、加速性、急性、慢性排斥反应的发生率均较高,往往成为移植的禁忌。由于免疫高敏的分子发生机制尚不明确,从基因水平研究其病因同时就很重要。
另外,除了排斥反应,免疫抑制剂的长期应用导致移植受者发生感染、心血管并发症、恶性肿瘤、肝脏和肾脏药物毒性等诸多问题的机会明显增加。重要的是要在免疫抑制药物取得疗效和发生风险之间取得平衡,以避免对受者的免疫抑制不足或免疫抑制过度。如果能从基因水平揭示免疫抑制剂的作用靶基因,将为临床更有效的用药提供新的理论和实验依据。
移植排斥是一个复杂的过程,目前尚未完全认识。为了更好地研究肾移植排斥反应,全面了解同种异体免疫应答的基本过程以及移植后临床上无排斥反应表现时的免疫应答过程就很必要,也很重要。移植后的免疫应答是一个多基因参与的、非常复杂的过程,T细胞和抗原呈递细胞(APC)的活化在其中起重要作用。随着分子免疫学研究的飞速发展,人们逐渐认识到机体对移植肾的免疫应答及其调控严格受到不同基因的控制。研究认为在T细胞活化过程中,参与激活和表达的基因约有70余种,这些基因按照功能分为三类:细胞原癌基因、细胞因子/细胞因子受体基因以及其他表面分子基因,根据激活所需时间或表达顺序分为三类:即时基因(细胞接受刺激后15-30分钟表达)、早基因(0.5-24小时表达)和晚基因(数天内表达)。T细胞在排
肾脏移植淋巴细胞免疫相关基因表达谱研究
第二军医大学博士论文
斥反应过程中的作用机制受多种基因和信号传导通路调控,其中涉及淋巴细胞多种基
因的表达变化与调控。
应用传统的分子生物学研究方法,无法阐明移植免疫过程中多个基因的复杂作用
机制及其相互作用和调控关系。近年来发展起来的高通量的基因表达分析平台一一基
囚,芯片于支术则可以满足此要求,能够同时句{究卜千万个从因的表达情况,为全而了解
基因表达谱变化提供了技术保证。IiI前,基因芯片技术已成为生物医学研究中的一个
强有力的_上具。与传统的.{究方法相比,基囚,芯片具有;筒通量、高度平行性、高灵敏
度、高效快速等优点。
为此,本实验应用基因芯片技术这一目前先进的基因表达分析方法,设计进行了
肾脏移植淋巴细胞免疫相关基因表达谱研究,包括3部分内容。山于移植术后24小
{付早去L因表达,而到术后7天,f细胞活化过程中的大部分基因(包括晚基因)已被
激活,因此实验的第一、第二两部分分别动态检测肾移植术后24小时和7天受者外
周血淋巴细胞基因表达谱变化情况,将有助于认识移植免疫的基本过程,并有利于研
究同种移植排斥反应和免疫抑制剂的作用机制。同时研究免疫抑制剂例如甲基强的松
龙(MP)冲击治疗和三联免疫抑制治疗对受者淋巴细胞免疫相关基因的影响,了解
其发挥药理作用的靶基因,以期对免疫抑制剂的研发和临床用药提供一些新的参考依
据。实验的第三部分对免疫高度致敏患者外周血淋巴细胞基因表达谱研究,筛选并分
析差异表达的基因,以期了解免疫高敏相关基因及其调控网络,从基因水平揭示淋巴
细胞在免疫高敏发生中的分子机制。
总之,木研究应用非侵袭性的方法,通过高通量、高灵敏度的基因芯片技术,在
体内研究肾脏移植淋巴细胞免疫相关基因表达谱。目前国内外尚未见有关报告。希望
木研究能够有助于进一步认识移植免疫的基本过程、免疫高致敏的发生机制,并从而
有助于进一步研究排斥反应的发生机制和免疫抑制方案。
第一部分
肾移植术后受者外周血淋巴细胞早反应基因表达的基因芯片研究
目的:研究肾移植术后受者外周血淋巴细胞早反应基因表达谱变化,了解移植免疫的
基本过程,从基因水平了解淋巴细胞在肾移植免疫反应过程中的作用。同时研究甲基
强的松龙(MP)冲击治疗对移植后淋巴细胞基因表达谱的影响,了解其分子作用机
肾脏移植淋巴细胞免疫相关基因表达语研究
第二军医大学博士论文
制。方法:取16例肾移植受者移植术前作为对照组,移植术后24小时作为实验组。
每例患者在术前和术后24小时分别抽取IOml新鲜血液,分离患者外周血淋巴细胞,
同组标本混合。应用基因芯片技术,按一步法抽提实验组和对照组细胞总RNA并纯
化mRNA,分别逆转录合成以Cy3一dUTP和Cys一dUT,标记的cDNA探针?
Currently, the optimal treatment of end-stage renal disease (ESRD) is renal transplantation. Allograft rejection is still an important obstacle. Among kidney transplant rejection including hyperacule rejection, accelerated rejection, acute rejection and chronic rejection, acute rejection is the major complication following renal transplantation. Acute rejection remains a significant problem in clinical transplantation. In contrast, acute rejection is an important risk factor contributing to chronic rejection or chronic allograft dysfunction or chronic allograft nephropathy or late graft loss. Acute rejection or chronic rejection is a key problem which affect long-term allograft survival.
Hyperacute rejection and accelerated rejection are associated with high panel reactive antibody (PRA >or = 50%) in highly sensitized patients awaiting transplantation who are sensitized to HLA antigens. These patients often have poor outcome after transplantation because of higher morbidity of hyperacute rejection, accelerated rejection, acute rejection or chronic rejection. To deal with sensitization is a worldwide problem. To explore molecular mechanisms of highly sensitization is necessary.
Besides rejection, complication due to long treatment with immunosuppressive agents is serious. How to make good use of immunosuppressive agents needs insight of their molecular mechanisms and target genes.
The rejection of an allograft is a complex and incompletely understood process. To understand rejection, it is necessary and important to understand the allogeneic immune response. Activation of alloreactive T cells and antigen-presenting cell (APC) is crucial. With the development of immunobiology, it is known that various genes involves activation of lymphocytes. There are more than 70 genes including oncogene, cytokine / cytokine receptor and other membran surface molecular gene. According to the time needed to express, these genes are divided into 3 groups: immediately genes (expressed within 15-30 minutes after stimulation), early genes (expressed within 0.5-24 hours) and late genes (expressed within several days). Many genes that code for signal transduction,
inflammatory, adhesion, or effector molecules involve allogeneic immune response. There are many genes and signal transduction pathway in regulation of T cell activation.
How to analysis hundreds or more genes in transplant immunity needs a powerful method such as recently developed DNA microarray technology. High-density microarray technology is a novel method for screening the expression of thousands of genes in a small tissue sample. This technique is well suited for studying allogeneic immune response and acute allograft rejection because alloimmune responses are complex and involve many cell types, making it extremely difficult to predict which gene products are reliable makers for alloimmune response or allograft rejection. DNA microarray analysis of genes expressed in peripheral lymphocytes during human renal allograft immune response and in highly sensitized patients has not been reported previously.
In this study, we sequentially investigated early and late gene expression profile of peripheral lymphocytes in human renal allograft recipients in the early post transplantation period (24 hours late and 7 days late) by means high-density cDNA microarray containing 4096 human genes (Part I and Part II). In these two parts, we also analyse the regulation of target genes expression by immunosuppressive agents including methylprednisolone pulse treatment and triple immunosuppressive medications consisting of cyclosporine, mycophenolate mofetil, and prednisone. In Part III, we comparatively analyze the immunological genes associated with sensitization of peripheral lymphocytes in highly sensitized patients using cDNA microarray technology.
Parti
Gene expression profile analysis of peripheral lymphocytes in human renal allograft
recipients during early post-transplantation period using cDNA microarray
technology
Objective: To explore differentially exp
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