Benoist Christophe, Germain Ronald N, Mathis Diane
Section on Immunology and Immunogenetics, Joslin Diabetes Center, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.
Immunol Rev. 2006 Apr;210:229-34. doi: 10.1111/j.0105-2896.2006.00374.x.
A complete understanding of the immune system will ultimately require an integrated perspective on how genetic and epigenetic entities work together to produce the range of physiologic and pathologic behaviors characteristic of immune function. The immune network encompasses all of the connections and regulatory associations between individual cells and the sum of interactions between gene products within a cell. With 30,000+ protein-coding genes in a mammalian genome, further compounded by microRNAs and yet unrecognized layers of genetic controls, connecting the dots of this network is a monumental task. Over the past few years, high-throughput techniques have allowed a genome-scale view on cell states and cell- or system-level responses to perturbations. Here, we observe that after an early burst of enthusiasm, there has developed a distinct resistance to placing a high value on global genomic or proteomic analyses. Such reluctance has affected both the practice and the publication of immunological science, resulting in a substantial impediment to the advances in our understanding that such large-scale studies could potentially provide. We propose that distinct standards are needed for validation, evaluation, and visualization of global analyses, such that in-depth descriptions of cellular responses may complement the gene/factor-centric approaches currently in favor.
要全面理解免疫系统,最终需要从基因和表观遗传实体如何协同作用以产生免疫功能所特有的一系列生理和病理行为的综合角度来考量。免疫网络涵盖了单个细胞之间的所有连接和调节关联,以及细胞内基因产物之间相互作用的总和。哺乳动物基因组中有30000多个蛋白质编码基因,再加上微小RNA以及尚未被认识的遗传控制层面,梳理这个网络的头绪是一项艰巨的任务。在过去几年里,高通量技术使人们能够从基因组规模上了解细胞状态以及细胞或系统层面对于扰动的反应。在此,我们注意到,在早期一阵热情之后,人们对高度重视全球基因组或蛋白质组分析产生了明显的抵触情绪。这种不情愿既影响了免疫学研究的实践,也影响了其成果的发表,严重阻碍了我们通过此类大规模研究可能获得的对免疫学理解的进展。我们建议,对于全球分析的验证、评估和可视化需要有不同的标准,以便对细胞反应的深入描述能够补充目前流行的以基因/因子为中心的研究方法。