Cell：成功建立环境影响细菌转录过程模型

生物谷

生物谷报道：环境能在很大程度上影响一个生物基因组的动态表达，并在基因编码的各种组分结合成完整而具有生物功能的网络的过程中起到关键作用。12月28日出版的《细胞》（Cell）杂志的封面文章，讲述的是美国的一个科学家小组成功建立了一个分析细菌Halobacterium  Salinarum  NRC-1以上过程的模型，Halobacterium  Salinarum是一种嗜盐的古生菌，一般只生存在盐水池塘或是盐湖中。

科学家通过分析数据，确定了在高盐度环境下，细菌80%的基因和关键的非生物因素之间调节和功能上的相互关系，并由此建立了模型。通过147个实验中72个转录因子和9个环境因子（EFs）的相对变化，该模型精确的预言了这些基因的动态转录反应。

研究人员利用了南旧金山湾获得的嗜盐细菌，这种生物为了适应高盐分环境而产生了一系列令人惊讶的适应性变化，其中包括在细胞膜上产生色素，以调节由光产生能量的过程。此外，这些细菌还产生了悬浮用的气泡结构，以帮助它们在水体中垂直移动来寻找氧载体。

尽管以上这些适应性变化每一个都很重要，然而环境因素造成的对细菌所有核心生理学过程的整体调节，对其在动态变化的环境中生存更加关键。因此，研究人员提出的转录控制模型非常有意义，这一模型精确预测了Halobacterium面对新环境或基因扰动时的转录变化。

此外，更重要的是，新研究支持了一种观点：生物体系的特征和它们所处的环境能帮助科学家建立高度精确、可预测性的基因调控模型，这些模型不仅针对传统生物，也能用于更多我们尚未了解的物种。（科学网　何宏辉/编译）

生物谷推荐英文原文：

Cell, Vol 131, 1354-1365, 28 December 2007

Resource

A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

Richard Bonneau,2,5,7 Marc T. Facciotti,1 David J. Reiss,1 Amy K. Schmid,1 Min Pan,1 Amardeep Kaur,1 Vesteinn Thorsson,1 Paul Shannon,1 Michael H. Johnson,1 J. Christopher Bare,1 William Longabaugh,1 Madhavi Vuthoori,1 Kenia Whitehead,1 Aviv Madar,2 Lena Suzuki,4 Tetsuya Mori,4 Dong-Eun Chang,4 Jocelyne DiRuggiero,3 Carl H. Johnson,4 Leroy Hood,1 and Nitin S. Baliga1,6,7,

1 Institute for Systems Biology, Seattle, WA 98103, USA
2 Center for Genomics & Systems Biology, New York University, New York, NY 10003, USA
3 University of Maryland, College Park, MD 20742, USA
4 Vanderbilt University, Nashville, TN 37240, USA
5 Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY 10003, USA
6 Departments of Microbiology and Molecular and Cellular Biology, University of Washington, Seattle, WA 98195, USA

Corresponding author
Nitin S. Baliga
nbaliga@systemsbiology.org

The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among ∼80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments—suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.