美國(guó)科學(xué)家研究出一種簡(jiǎn)單的測(cè)試方法,，通過(guò)檢測(cè)血液中某些蛋白質(zhì)的含量，就能發(fā)現(xiàn)患者身體對(duì)移植器官的排異反應(yīng),。
這項(xiàng)新成果可幫助醫(yī)生在移植器官受到實(shí)質(zhì)損害之前發(fā)現(xiàn)排異反應(yīng),、及時(shí)采取應(yīng)對(duì)措施。還可用于調(diào)節(jié)免疫抑制劑的用量,，盡量減少副作用,。
患者接受器官移植后，自身免疫系統(tǒng)可能把移植器官當(dāng)作異物發(fā)起攻擊,，引起排異反應(yīng),。通常，在移植器官功能出現(xiàn)異常時(shí),，醫(yī)生會(huì)從器官上取下一小塊組織,，檢測(cè)是否有排異反應(yīng)發(fā)生。這種方法的缺點(diǎn)在于,，當(dāng)發(fā)現(xiàn)問(wèn)題時(shí),，器官可能已經(jīng)受損。
據(jù)英國(guó)《新科學(xué)家》雜志網(wǎng)站報(bào)道,，美國(guó)斯坦福大學(xué)的科學(xué)家利用現(xiàn)有資料,，分析排異反應(yīng)發(fā)生時(shí)血液中哪些蛋白質(zhì)的水平會(huì)發(fā)生變化，并從接受腎移植和心臟移植的患者身上取得血液樣本進(jìn)行研究,。
最終,，研究人員發(fā)現(xiàn)，有3種蛋白質(zhì)可以作用排異反應(yīng)檢測(cè)的“標(biāo)識(shí)物”,。在發(fā)生嚴(yán)重排異反應(yīng)時(shí),，血液中這些蛋白質(zhì)的水平顯著升高，而且它們都可以利用現(xiàn)有臨床手段檢測(cè)到,。
為減輕排異反應(yīng),，需要用藥物抑制患者免疫系統(tǒng)的功能，但這會(huì)導(dǎo)致患者免疫力低下,，容易受病菌和病毒侵襲,。參照血液檢測(cè)結(jié)果，就可以只在排異反應(yīng)出現(xiàn)的時(shí)候加大免疫抑制劑用量,，避免在平時(shí)不必要地抑制患者的免疫力,。
有關(guān)論文發(fā)表在《公共科學(xué)圖書(shū)館·計(jì)算生物學(xué)》雜志上。研究人員正在計(jì)劃進(jìn)行臨床試驗(yàn),，希望新的檢測(cè)方法3年到5年內(nèi)能付諸實(shí)用,。（生物谷Bioon.com）
生物谷推薦英文摘要：
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.
