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.

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多组学数据和社区代谢模型的合成揭示了硫化氢在结肠癌中的作用

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我们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限度地发挥数据潜力。我们的研究展示了一种通过结合多组学数据和社区代谢模型来检验假设的方法。具体来说，我们根据在同一个体肿瘤部位内外收集的粪便、粘膜和组织样本评估结直肠癌中硫化氢的产生。16S rRNA 微生物群落和丰度数据用于选择和告知代谢模型。然后我们使用了开源平台 MICOM，通过定义的微生物群落跟踪硫化氢的代谢通量，这些微生物群落代表肿瘤内或肿瘤外样本群落。我们还进行了靶向和非靶向代谢组学，并使用前者来定量评估我们的模型预测。对模型的深入研究确定了硫化氢生产中涉及的几个意外但可行的反应、微生物和微生物相互作用，我们的 16S 和代谢组学数据无法解释这些反应。这些结果将指导未来的体外、体内和计算机试验，以确定肿瘤组织中硫化氢产量增加的原因。对模型的深入研究确定了硫化氢生产中涉及的几个意外但可行的反应、微生物和微生物相互作用，我们的 16S 和代谢组学数据无法解释这些反应。这些结果将指导未来的体外、体内和计算机测试，以确定为什么肿瘤组织中硫化氢的产生增加。对模型的深入研究确定了硫化氢生产中涉及的几个意外但可行的反应、微生物和微生物相互作用，我们的 16S 和代谢组学数据无法解释这些反应。这些结果将指导未来的体外、体内和计算机试验，以确定肿瘤组织中硫化氢产量增加的原因。