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Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer
Methods ( IF 4.2 ) Pub Date : 2018-04-26
Vanessa L. Hale, Patricio Jeraldo, Michael Mundy, Janet Yao, Gary Keeney, Nancy Scott, E. Heidi Cheek, Jennifer Davidson, Megan Green, Christine Martinez, John Lehman, Chandra Pettry, Erica Reed, Kelly Lyke, Bryan A. White, Christian Diener, Osbaldo Resendis-Antonio, Jaime Gransee, Tumpa Dutta, Xuan-Mai Petterson, Lisa Boardman, David Larson, Heidi Nelson, Nicholas Chia

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.



中文翻译:

多组学数据和社区代谢模型的综合揭示了硫化氢在结肠癌中的作用的见解

多组学数据和基因组规模的微生物代谢模型使我们能够以历史上无法获得的方式检查微生物群落,群落功能和相互作用。现在,我们面临的最大挑战之一是确定如何集成数据并最大程度地发挥数据潜力。我们的研究表明了一种通过结合多组数据和社区代谢模型来检验假设的方法。具体来说,我们根据粪便,粘膜和在同一个体内和外的肿瘤部位收集的组织样本评估大肠癌中硫化氢的产生。使用16S rRNA微生物群落和丰度数据来选择和告知代谢模型。然后,我们使用了MICOM(一个开源平台),通过代表肿瘤内或肿瘤外样本群落的特定微生物群落追踪硫化氢的代谢通量。我们还进行了有针对性的和无针对性的代谢组学研究,并使用前者来定量评估我们的模型预测。对模型进行更深入的研究,发现了硫化氢生产中涉及的一些出乎意料但可行的反应,微生物和微生物相互作用,而我们的16S和代谢组学数据无法解释这些问题。这些结果将指导未来 和硫化氢生产中涉及的微生物相互作用,而我们的16S和代谢组学数据无法解释这些相互作用。这些结果将指导未来 和硫化氢生产中涉及的微生物相互作用,而我们的16S和代谢组学数据无法解释这些相互作用。这些结果将指导未来体外体内计算机模拟测试确定了为什么肿瘤组织中硫化氢的产生增加了。

更新日期:2018-04-26
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