目前，科学家已经破译了一个关键的分子通路，该通路能够使机体从细菌和其他的微生物中辨别出病毒。这项研究为哺乳动物中免疫细胞抵御不同病原体的机制提供了深入的理解。
这项研究结果发布在9月3日Science的网络版本上。
他们的这项发现阐述了人类生物学一个重要的问题，即免疫细胞如何识别不同的病原体，并产生不同的免疫应答。该研究负责人Nir Hacohen介绍说，他们现在已经深入探究了该通路控制的重要生物学过程，这为疾病治疗和疫苗设计提供了新的思路。
值得一提的还有研究过程中使用的方法。这种方法不仅全面而且适用于大部分的生物学系统，也可用于实验室。
和计算机类似，细胞会接收和处理信息，然后读取信息流并通过一系列复杂的通路处理信息，并产生适当的应答。但与计算机不同的是哺乳动物细胞内的通路是由大量的基因网络以及相应的蛋白组成的。
该研究关注了人类的树突细胞，并发现了一个重要的分子通路。这项发现有助于人类自身免疫和其他相关疾病的研究，加深了对遗传易感性以及其他免疫失调的理解。(生物谷Bioon.com)
生物谷推荐原始出处：
Science 3 September 2009 DOI: 10.1126/science.1179050
Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses
Ido Amit, Manuel Garber, Nicolas Chevrier, Ana Paula Leite, Yoni Donner, Thomas Eisenhaure, Mitchell Guttman, Jennifer K. Grenier, Weibo Li, Or Zuk, Lisa A. Schubert, Brian Birditt, Tal Shay, Alon Goren, Xiaolan Zhang, Zachary Smith, Raquel Deering, Rebecca C. McDonald, Moran Cabili, Bradley E. Bernstein, John L. Rinn, Alex Meissner, David E. Root, Nir Hacohen, Aviv Regev
1 Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129 USA.; Harvard Medical School, Boston, MA 02115, USA.; MIT, Department of Biology, Cambridge, MA 02142, USA.
2 Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.
3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129 USA.; Harvard Medical School, Boston, MA 02115, USA.
4 Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.; Computational and Systems Biology, MIT Cambridge, MA 02139, USA.
5 Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.; MIT, Department of Biology, Cambridge, MA 02142, USA.
6 NanoString Technologies, 530 Fairview Ave. N, Suite 2000, Seattle, WA 98109, USA.
7 Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 149 13th St., Charlestown, MA 02129 USA.; Harvard Medical School, Boston, MA 02115, USA.
Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data, yet have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We apply this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells (DCs) to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins and constructed a network model consisting of two dozen core regulators and 76 fine-tuners that help explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly applicable, comprehensive, and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.
