An integrated genomic and metabolomic framework for cell wall biology in rice.
An integrated genomic and metabolomic framework for cell wall biology in rice.
复制标题
水稻细胞壁生物学的整合基因组和代谢组学框架
DOI：
10.1186/1471-2164-15-596
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发表时间：
2014-07-15
期刊：
影响因子：
4.4
通讯作者：
Peng L
中科院分区：
文献类型：
--
作者：
Guo K;Zou W;Feng Y;Zhang M;Zhang J;Tu F;Xie G;Wang L;Wang Y;Klie S;Persson S;Peng L
背景植物细胞壁是复杂的结构，在植物生长和发育过程中履行许多不同的功能。因此，数以千计的基因产物参与细胞壁的合成和维护也就不足为奇了。然而，大多数这些基因产物的功能关联仍然不清楚。推断生物关联的一种有用方法是通过转录协调或基因的共表达。这种方法已被证明对多种生物过程有用。然而，将共表达与其他大规模测量相结合可能会改善生物学推论。结果在本研究中，我们使用共表达和细胞壁代谢组学的组合方法来获得对水稻细胞壁合成的新见解。我们首先根据公开的数据集创建了一个加权基因共表达网络，然后通过确定几乎覆盖水稻整个生命周期的 29 个组织的细胞壁组成来建立一个全面的细胞壁数据集。随后，我们通过将共表达的基因模块转换为特征向量来组合数据集，代表模块中基因的表达谱，并针对细胞壁内容物进行比较分析。在这里，我们有了三个重大发现。首先，我们通过分别寻找初生壁纤维素生物合成模块和次生壁纤维素生物合成模块来确认我们的方法。其次，我们发现共表达模块与次生细胞壁的重组以及半纤维素结构的修饰和降解密切相关。第三，我们推断至少一个模块可能在富含G的木质化的产生中发挥调节作用。结论在这里，我们整合了转录组关联和细胞壁代谢，发现某些共表达的基因模块与不同的细胞壁特征呈正相关。我们建议，结合多种数据类型，例如协调转录和细胞壁分析，可能是收集生物过程新见解的有用方法。如此处所示，多个数据集的组合可以进一步改进通常通过单一类型的数据集生成的函数推理。此外，我们的数据扩展了典型的共表达方法，可以更深入地了解水稻的细胞壁生物学。
BackgroundPlant cell walls are complex structures that full-fill many diverse functions during plant growth and development. It is therefore not surprising that thousands of gene products are involved in cell wall synthesis and maintenance. However, functional association for the majority of these gene products remains obscure. One useful approach to infer biological associations is via transcriptional coordination, or co-expression of genes. This approach has proved useful for several biological processes. Nevertheless, combining co-expression with other large-scale measurements may improve the biological inferences.ResultsIn this study, we used a combined approach of co-expression and cell wall metabolomics to obtain new insight into cell wall synthesis in rice. We initially created a weighted gene co-expression network from publicly available datasets, and then established a comprehensive cell wall dataset by determining cell wall compositions from 29 tissues that almost cover the whole life cycle of rice. We subsequently combined the datasets through the conversion of co-expressed gene modules into eigen-vectors, representing expression profiles for the genes in the modules, and performed comparative analyses against the cell wall contents. Here, we made three major discoveries. First, we confirmed our approach by finding primary and secondary wall cellulose biosynthesis modules, respectively. Second, we found co-expressed modules that strongly correlated with re-organization of the secondary cell walls and with modifications and degradation of hemicellulosic structures. Third, we inferred that at least one module is likely to play a regulatory role in the production of G-rich lignification.ConclusionsHere, we integrated transcriptomic associations and cell wall metabolism and found that certain co-expressed gene modules are positively correlated with distinct cell wall characteristics. We propose that combining multiple data-types, such as coordinated transcription and cell wall analyses, may be a useful approach to glean new insight into biological processes. The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets. In addition, our data extend the typical co-expression approach to allow deeper insight into cell wall biology in rice.
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