Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments
Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments
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DOI：
10.1371/journal.pcbi.1007099
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发表时间：
2020-04-01
影响因子：
4.3
通讯作者：
Papin, Jason A.
中科院分区：
文献类型：
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作者：
Jenior, Matthew L.;Moutinho, Thomas J., Jr.;Papin, Jason A.
细菌对动态胞外条件的代谢反应不仅驱动单个物种的行为，也驱动整个微生物群落的行为。在过去的十年里，基因组规模的代谢网络重建帮助我们了解了细菌生理的重要代谢决定因素。这些网络模型一直是理解物种为了在环境中取得成功而可能利用的新陈代谢能力的强大力量。越来越多地，对特定背景代谢的了解对于阐明更大表型和疾病的代谢驱动因素至关重要。然而，以前将网络模型与组学数据结合使用以更好地表征实验系统的方法遇到了挑战，这是由于各种集成平台所必需的假设或由于大量输入数据的要求。考虑到这些挑战，我们开发了Riptie(简约和转录分布的反应包含)，它使用转录丰度和总通量的简约来确定代谢的最具成本效益的使用，也最好地反映细胞对转录的投资。此外，在难以量化特定生长条件的生物样本中，开发需要较少用户干预的方法以生成准确的代谢预测变得至关重要。利用代谢网络对模式生物大肠杆菌菌株进行重构。K-12亚串。MG1655(IJO1366)中，我们发现Riptie正确地识别了上下文特定的代谢途径活动，而不需要监督或知道特定的介质条件。我们还评估了Riptie在大肠杆菌高丰度的体内转译数据中的应用，并发现我们的方法也有效地预测了宿主相关细菌的代谢行为。在人类健康的背景下，在生长底物可用性难以量化的环境中，了解细菌内部的代谢变化可能会对我们阐明整个微生物区系中与疾病相关的生物失调的分子驱动因素的能力产生很大的下游影响。我们的结果表明，激流可能有可能提供了解复杂社区内细菌的上下文特定新陈代谢。作者摘要细菌的转录分析已经成为我们理解他们对环境变化的反应的工具。虽然传统的分析是信息量很大的，但在基因组规模的代谢网络重建(类型)中利用这些数据集可以为途径利用的转变和代谢调节变化的下游/上游分支提供极大改善的背景。以前的许多类型转录整合技术都集中在利用输入数据集建立最大共识，但最近发现，这些方法产生的代谢预测不如转录无关通量最小化方法(PFBA)准确，后者确定了给定某些生长约束的最有效/最经济的代谢模式。尽管取得了这样的成功，但增长条件并不总是容易量化的，并突显了基于这些发现构建新平台的必要性。我们的新方法Riptie结合了这些概念，并利用转录分析加权的整体最小通量来确定实现生长的最节能途径，其中包括更高转录的酶，而不需要事先了解细胞外条件。利用来自大肠杆菌的一个研究得很好的类型，我们证明了这种新方法利用各种体外和体内转录本正确地预测了新陈代谢的模式。这个平台对于揭示符合适应性进化的支配原则的特定背景的细菌表型可能是重要的，这些原则驱动了疾病的表现或微生物之间的相互作用。
The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell's investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.Author summaryTranscriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.