Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, Iowa, United States of America.
PLoS One. 2010 Sep 2;5(9):e12525. doi: 10.1371/journal.pone.0012525.
Large-scale gene expression studies have not yielded the expected insight into genetic networks that control complex processes. These anticipated discoveries have been limited not by technology, but by a lack of effective strategies to investigate the data in a manageable and meaningful way. Previous work suggests that using a pre-determined seed-network of gene relationships to query large-scale expression datasets is an effective way to generate candidate genes for further study and network expansion or enrichment. Based on the evolutionary conservation of gene relationships, we test the hypothesis that a seed network derived from studies of retinal cell determination in the fly, Drosophila melanogaster, will be an effective way to identify novel candidate genes for their role in mouse retinal development.
METHODOLOGY/PRINCIPAL FINDINGS: Our results demonstrate that a number of gene relationships regulating retinal cell differentiation in the fly are identifiable as pairwise correlations between genes from developing mouse retina. In addition, we demonstrate that our extracted seed-network of correlated mouse genes is an effective tool for querying datasets and provides a context to generate hypotheses. Our query identified 46 genes correlated with our extracted seed-network members. Approximately 54% of these candidates had been previously linked to the developing brain and 33% had been previously linked to the developing retina. Five of six candidate genes investigated further were validated by experiments examining spatial and temporal protein expression in the developing retina.
CONCLUSIONS/SIGNIFICANCE: We present an effective strategy for pursuing a systems biology approach that utilizes an evolutionary comparative framework between two model organisms, fly and mouse. Future implementation of this strategy will be useful to determine the extent of network conservation, not just gene conservation, between species and will facilitate the use of prior biological knowledge to develop rational systems-based hypotheses.
大规模基因表达研究并未如预期那样深入了解控制复杂过程的遗传网络。这些预期的发现不是受到技术的限制，而是由于缺乏有效的策略来以可管理和有意义的方式研究数据。以前的工作表明，使用预先确定的基因关系种子网络来查询大规模表达数据集是生成候选基因进行进一步研究和网络扩展或富集的有效方法。基于基因关系的进化保守性，我们检验了这样一个假设，即源自果蝇视网膜细胞决定的种子网络将是一种有效的方法，可以确定在小鼠视网膜发育中起作用的新候选基因。
方法/主要发现：我们的结果表明，调节果蝇视网膜细胞分化的许多基因关系可以识别为发育中的小鼠视网膜中基因之间的成对相关性。此外，我们证明，我们提取的相关鼠标基因种子网络是查询数据集和生成假设的有效工具。我们的查询确定了 46 个与我们提取的种子网络成员相关的基因。这些候选基因中有大约 54%以前与发育中的大脑有关，33%以前与发育中的视网膜有关。进一步研究的六个候选基因中的五个通过检查发育中的视网膜中时空蛋白表达的实验得到了验证。
结论/意义：我们提出了一种有效的策略，用于采用系统生物学方法，利用两种模式生物，即果蝇和小鼠之间的进化比较框架。未来实施这一策略将有助于确定网络保守性的程度，而不仅仅是物种之间的基因保守性，并促进利用先前的生物学知识来制定合理的基于系统的假设。
