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PLoS Com.Biolo.:一种简单器官移植排斥反应血液检测法

  1. 器官移植
  2. 血液检测

来源:新华网 2010-09-29 14:44

美国科学家研究出一种简单的测试方法,通过检测血液中某些蛋白质的含量,就能发现患者身体对移植器官的排异反应。 这项新成果可帮助医生在移植器官受到实质损害之前发现排异反应、及时采取应对措施。还可用于调节免疫抑制剂的用量,尽量减少副作用。 患者接受器官移植后,自身免疫系统可能把移植器官当作异物发起攻击,引起排异反应。

美国科学家研究出一种简单的测试方法,通过检测血液中某些蛋白质的含量,就能发现患者身体对移植器官的排异反应。

这项新成果可帮助医生在移植器官受到实质损害之前发现排异反应、及时采取应对措施。还可用于调节免疫抑制剂的用量,尽量减少副作用。

患者接受器官移植后,自身免疫系统可能把移植器官当作异物发起攻击,引起排异反应。通常,在移植器官功能出现异常时,医生会从器官上取下一小块组织,检测是否有排异反应发生。这种方法的缺点在于,当发现问题时,器官可能已经受损。

据英国《新科学家》杂志网站报道,美国斯坦福大学的科学家利用现有资料,分析排异反应发生时血液中哪些蛋白质的水平会发生变化,并从接受肾移植和心脏移植的患者身上取得血液样本进行研究。

最终,研究人员发现,有3种蛋白质可以作用排异反应检测的“标识物”。在发生严重排异反应时,血液中这些蛋白质的水平显着升高,而且它们都可以利用现有临床手段检测到。

为减轻排异反应,需要用药物抑制患者免疫系统的功能,但这会导致患者免疫力低下,容易受病菌和病毒侵袭。参照血液检测结果,就可以只在排异反应出现的时候加大免疫抑制剂用量,避免在平时不必要地抑制患者的免疫力。

有关论文发表在《公共科学图书馆·计算生物学》杂志上。研究人员正在计划进行临床试验,希望新的检测方法3年到5年内能付诸实用。(生物谷Bioon.com)

生物谷推荐英文摘要:

PLoS Comput Biol 6(2): e1000662. doi:10.1371/journal.pcbi.1000662

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets
Silpa Suthram1,2,3, Joel T. Dudley1,2,3, Annie P. Chiang1,2,3, Rong Chen1,2,3, Trevor J. Hastie4, Atul J. Butte1,2,3*

1 Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States of America, 2 Department of Pediatrics, Stanford University, Stanford, California, United States of America, 3 Lucile Packard Children's Hospital, Palo Alto, California, United States of America, 4 Department of Statistics, Stanford University, Stanford, California, United States of America

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.

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