metaModules identifies key functional subnetworks in microbiome-related disease
metaModules identifies key functional subnetworks in microbiome-related disease
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DOI：
10.1093/bioinformatics/btv526
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发表时间：
2016-06-01
期刊：
影响因子：
5.8
通讯作者：
Abeln, Sanne
中科院分区：
文献类型：
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作者：
May, Ali;Brandt, Bernd W.;Abeln, Sanne
动机：人体微生物组在健康和疾病中起着关键作用。多亏了比较超转录组学，现在可以通过计算来探索疾病中被微生物群解除调控的细胞功能。与以基因为中心的方法不同，基于途径的方法提供了这些功能的系统视图；然而，他们通常是孤立地、整体地考虑每个途径。因此，他们可以忽略(i)跨越多个通路，（ii）包含双向不受管制的成分，（iii）局限于一个通路区域的关键差异。为了捕获这些属性，需要超出预定义路径范围的计算方法。结果：通过将现有的模块发现算法集成到比较元转录组学分析中，我们开发了元模块，这是一种新的计算框架，用于自动识别健康和疾病相关社区之间的关键功能差异。利用这一框架，我们恢复了显著解除管制的子网络，这些子网络确实被认为参与了两种经过充分研究的微生物组介导的口腔疾病，如牙周病中的丁酸盐产生和龋齿中糖醇的代谢。更重要的是，我们的结果表明，我们的方法可以用于基于自动发现新的疾病相关功能子网的假设生成，否则将需要大量和费力的人工评估。
Motivation: The human microbiome plays a key role in health and disease. Thanks to comparative metatranscriptomics, the cellular functions that are deregulated by the microbiome in disease can now be computationally explored. Unlike gene-centric approaches, pathway-based methods provide a systemic view of such functions; however, they typically consider each pathway in isolation and in its entirety. They can therefore overlook the key differences that (i) span multiple pathways, (ii) contain bidirectionally deregulated components, (iii) are confined to a pathway region. To capture these properties, computational methods that reach beyond the scope of predefined pathways are needed.Results: By integrating an existing module discovery algorithm into comparative metatranscriptomic analysis, we developed metaModules, a novel computational framework for automated identification of the key functional differences between health-and disease-associated communities. Using this framework, we recovered significantly deregulated subnetworks that were indeed recognized to be involved in two well-studied, microbiome-mediated oral diseases, such as butanoate production in periodontal disease and metabolism of sugar alcohols in dental caries. More importantly, our results indicate that our method can be used for hypothesis generation based on automated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment.