通过自动工具和共识方法重建的微生物群落代谢模型的比较分析。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yunli Eric Hsieh, Kshitij Tandon, Heroen Verbruggen, Zoran Nikoloski
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引用次数: 0

摘要

微生物群落的基因组尺度代谢模型(GEM)为了解其成员的功能能力提供了宝贵的信息,并有助于探索微生物之间的相互作用。这些模型是利用不同的自动重建工具生成的,每种工具都依赖于不同的生化数据库,这些数据库可能会影响从硅学分析中得出的结论。解决这一问题的方法之一是采用一种共识重建方法,将不同重建工具的结果结合起来。在这里,我们利用两个海洋细菌群落的元基因组学数据,对三种自动工具(即 CarveMe、gapseq 和 KBase)和一种共识方法重建的群落模型进行了比较分析。我们的分析表明,这些重建方法虽然基于相同的基因组,但由于所使用的数据库不同,产生的 GEM 的基因和反应数量以及代谢功能也各不相同。此外,我们的研究结果表明,所交换的代谢物集更多地受到重建方法的影响,而不是所调查的特定细菌群落。这一观察结果表明,使用群落 GEM 预测代谢物相互作用可能存在偏差。我们的研究还表明,共识模型涵盖了更多的反应和代谢物,同时也减少了死胡同代谢物的存在。因此,在评估微生物群落的功能潜力时,使用共识模型可以充分、无偏见地利用来自不同重建的聚集基因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches.

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches.

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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