随机建模揭示了代谢异质性的机制
Stochastic modelling reveals mechanisms of metabolic heterogeneity
摘要：
表型变异是细胞生理学的标志。代谢的异质性，特别是单细胞现象，如微生物的耐药性和生长的变异性。许多研究都集中在转录组和蛋白质组的异质性，但目前还不清楚，如果这种变化渗透到细胞的代谢状态。在这里，我们提出了一个随机模型，以表明复杂形式的代谢异质性出现在酶的表达和催化的波动。分析预测克隆种群分裂成两个或两个以上的代谢不同的亚群。我们揭示了在确定性模型中看不到的机制，其中具有单峰表达分布的酶导致在整个人群中具有双峰或多峰分布的代谢物。基于已发表的数据，结果表明代谢物异质性可能比以前认为的更普遍。我们的工作揭示了基因表达和代谢之间的联系，并为探索代谢物异质性的来源提供了理论依据。
英文摘要：
Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.
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