Decomposition of gene expression state space trajectories.
Decomposition of gene expression state space trajectories.
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DOI：
10.1371/journal.pcbi.1000626
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发表时间：
2009-12
影响因子：
4.3
通讯作者：
Quackenbush J
中科院分区：
文献类型：
--
作者：
Mar JC;Quackenbush J
表示和分析复杂的网络仍然是创建生物过程和途径的动态网络模型的障碍。对细胞命运转变的研究可以揭示这些表型变化背后的转录调控程序，并产生我们所观察到的表达变化的协调模式。应用基因表达状态空间轨迹来捕捉全基因组水平的细胞命运转变是目前文献中使用的一种方法。在本文中，我们分析了Huang等人（2005）的基因表达数据集，该数据集跟踪了早幼粒细胞在诱导剂二甲亚砜和全反式维甲酸存在下向中性粒细胞样细胞分化的过程。Huang等人（2005）以Kauffman（2004）的工作为基础，提出了吸引子假说，指出细胞存在于一个表达景观中，它们的表达轨迹向这个景观中的吸引点汇聚。我们提出了另一种解释，通过认识到参与这些细胞命运转变的两种类型的过程来解释这种趋同行为——核心过程包括早幼粒细胞向中性粒细胞的特定分化途径，以及捕获这些途径和对诱导剂特异性反应的短暂过程。通过功能富集分析、特定的生物实例以及对轨迹及其核心和瞬态成分的分析，我们使用Huang等人（2005）的数据集验证了我们的假设。了解细胞如何从一种状态分化到另一种状态是生物学中的一个基本问题，对更好地理解进化、从单个受精卵发育成复杂生物体以及人类疾病的病因具有重要意义。观察这些过程的一种方法是将细胞视为“复杂的适应系统”，其中细胞中所有基因（超过20,000个基因）的状态决定了细胞在给定时间点的“状态”。在这个观点中，分化细胞沿着“状态空间”的路径从一个稳定的“吸引子”移动到另一个。在2005年的一篇论文中，黄穗和他的同事提出了一个实验模型，在这个模型中，他们声称有证据表明这种吸引子和它们之间的转换存在。这种方法的问题在于，尽管直观上很吸引人，但它缺乏预测能力。重新分析Huang的数据，我们证明了存在另一种解释，它仍然允许状态空间描述，但具有更大的可测试预测能力。具体来说，我们表明这些抽象的状态空间轨迹可以映射到更知名的途径上，并表示为“核心”分化途径和“瞬时”过程，这些过程捕获了引发分化的处理的效果。
Representing and analyzing complex networks remains a roadblock to creating dynamic network models of biological processes and pathways. The study of cell fate transitions can reveal much about the transcriptional regulatory programs that underlie these phenotypic changes and give rise to the coordinated patterns in expression changes that we observe. The application of gene expression state space trajectories to capture cell fate transitions at the genome-wide level is one approach currently used in the literature. In this paper, we analyze the gene expression dataset of Huang et al. (2005) which follows the differentiation of promyelocytes into neutrophil-like cells in the presence of inducers dimethyl sulfoxide and all-trans retinoic acid. Huang et al. (2005) build on the work of Kauffman (2004) who raised the attractor hypothesis, stating that cells exist in an expression landscape and their expression trajectories converge towards attractive sites in this landscape. We propose an alternative interpretation that explains this convergent behavior by recognizing that there are two types of processes participating in these cell fate transitions—core processes that include the specific differentiation pathways of promyelocytes to neutrophils, and transient processes that capture those pathways and responses specific to the inducer. Using functional enrichment analyses, specific biological examples and an analysis of the trajectories and their core and transient components we provide a validation of our hypothesis using the Huang et al. (2005) dataset. Understanding how cells differentiate from one state to another is a fundamental problem in biology with implications for better understanding evolution, the development of complex organisms from a single fertilized egg, and the etiology of human disease. One way to view these processes is to examine cells as “complex adaptive systems” where the state of all genes in a cell (more than 20,000 genes) determines that cell's “state” at a given point in time. In this view, differentiating cells move along a path in “state space” from one stable “attractor” to another. In a 2005 paper, Sui Huang and colleagues presented an experimental model in which they claimed to have evidence for such attractors and for the transitions between them. The problem with this approach is that although it is intuitively appealing, it lacks predictive power. Reanalyzing Huang's data, we demonstrate that there is an alternative interpretation that still allows for a state space description but which has greater ability to make testable predictions. Specifically, we show that these abstract state space trajectories can be mapped onto more well-known pathways and represented as a “core” differentiation pathway and “transient” processes that capture the effects of the treatments that initiate differentiation.
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影响因子：
2.7
作者：
Lee, YR;Shim, HJ;Kim, JS
通讯作者：
Kim, JS
影响因子：
5.8
作者：
Eichler, GS;Huang, S;Ingber, DE
通讯作者：
Ingber, DE
DOI：
10.1073/pnas.95.16.9172
发表时间：
1998-08-04
影响因子：
11.1
作者：
Guo, QB;Xie, JW;Bishop, JM
通讯作者：
Bishop, JM
DOI：
10.1073/pnas.77.5.2936
发表时间：
1980-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
BREITMAN, TR;SELONICK, SE;COLLINS, SJ
通讯作者：
COLLINS, SJ
DOI：
10.1073/pnas.75.5.2458
发表时间：
1978-01-01
影响因子：
11.1
作者：
COLLINS, SJ;RUSCETTI, FW;GALLO, RC
通讯作者：
GALLO, RC