科學(xué)家長(zhǎng)期以來(lái)一直認(rèn)為在學(xué)習(xí)和記憶過(guò)程中,，特定的一些基因必定被激發(fā)，以改變大腦內(nèi)部神經(jīng)的活動(dòng),。這些基因表達(dá)的中斷會(huì)導(dǎo)致癲癇等異常癥狀,。但是確認(rèn)所有這些基因是一項(xiàng)很困難的工作。在《BMC Neuroscience》上,，來(lái)自Carnegie Mellon大學(xué)的科學(xué)家顯示了用一種全新的計(jì)算方法能快速確定這些基因,。
來(lái)自神經(jīng)基礎(chǔ)認(rèn)知中心的生物科學(xué)助理教授Alison Barth說(shuō)：“這項(xiàng)成果將最終得到治療神經(jīng)疾病的新藥物。同時(shí)我們也希望我們的工作能為研究者在分子水平上了解學(xué)習(xí)和記憶形成過(guò)程中神經(jīng)的改變帶來(lái)幫助,。”
在動(dòng)物學(xué)習(xí)和記憶過(guò)程中,，大腦中特殊的神經(jīng)元會(huì)被激活。這些分子變化導(dǎo)致神經(jīng)元功能改變,。神經(jīng)科學(xué)家一直知道有兩種因子——CREB和zif268起到激活基因的作用,。CREB和zif268是轉(zhuǎn)錄因子,。
Carnegie Mellon的小組創(chuàng)造了一種一步步尋找被CREB和zif268激活的基因的計(jì)算機(jī)方法。他們找到了大約20000個(gè)相關(guān)基因,。計(jì)算機(jī)程序在人類和老鼠基因組中發(fā)現(xiàn)了數(shù)百個(gè)和CREB或者zif268結(jié)合的基因。而其中絕大部分之前從未被確認(rèn)為CREB和zif268的目標(biāo)過(guò),。
Schwartz認(rèn)為：“這意味著這些基因和學(xué)習(xí)及記憶相關(guān),。在不同物種中起到類似作用的基因被認(rèn)為有著很重要的作用，因?yàn)樗麄冊(cè)谶M(jìn)化過(guò)程中得到保留,。”小組將他們的結(jié)果在網(wǎng)上公布,，現(xiàn)在他們的在線數(shù)據(jù)庫(kù)中包含了基因名稱、符號(hào)和編號(hào)等信息——這些信息通常是不會(huì)被全部收集的,。
原文鏈接：http://www.physorg.com/news96118776.html
譯自：physorg.com
原始出處:
A comparative genomics approach to identifying the plasticity transcriptome
Andreas R Pfenning1 , Russell Schwartz1 ,2 and Alison L Barth2 , 3
1Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
BMC Neuroscience 2007, 8:20 doi:10.1186/1471-2202-8-20
Abstract
Background
Neuronal activity regulates gene expression to control learning and memory, homeostasis of neuronal function, and pathological disease states such as epilepsy. A great deal of experimental evidence supports the involvement of two particular transcription factors in shaping the genomic response to neuronal activity and mediating plasticity: CREB and zif268 (egr-1, krox24, NGFI-A). The gene targets of these two transcription factors are of considerable interest, since they may help develop hypotheses about how neural activity is coupled to changes in neural function.
Results
We have developed a computational approach for identifying binding sites for these transcription factors within the promoter regions of annotated genes in the mouse, rat, and human genomes. By combining a robust search algorithm to identify discrete binding sites, a comparison of targets across species, and an analysis of binding site locations within promoter regions, we have defined a group of candidate genes that are strong CREB- or zif268 targets and are thus regulated by neural activity. Our analysis revealed that CREB and zif268 share a disproportionate number of targets in common and that these common targets are dominated by transcription factors.
Conclusion
These observations may enable a more detailed understanding of the regulatory networks that are induced by neural activity and contribute to the plasticity transcriptome. The target genes identified in this study will be a valuable resource for investigators who hope to define the functions of specific genes that underlie activity-dependent changes in neuronal properties.
