受调控的细菌相互作用网络:一个数学框架,用于描述在包含代谢物交叉喂养的情况下的竞争性生长。

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2023-08-21 eCollection Date: 2023-08-01 DOI:10.1371/journal.pcbi.1011402
Isaline Guex, Christian Mazza, Manupriyam Dubey, Maxime Batsch, Renyi Li, Jan Roelof van der Meer
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引用次数: 0

摘要

当具有相同资源偏好的细菌物种共享相同的生长环境时,通常认为会出现直接竞争。已经制定了各种各样的竞争和更通用的“相互作用”模型,但目前缺乏的是将单一栽培生长动力学和群落生长联系起来的模型,包括新出现的生物相互作用,如代谢物交叉喂养。为了理解和数学描述潜在交叉喂养相互作用的性质,我们设计了两种细菌在液体培养基中生长的实验,在资源有限的环境中,两种细菌分别以单培养或共培养的方式生长。我们测量了单底物竞争或双物种特异性底物(底物“无差异”)下的种群增长,并从两个物种不同的细胞比例开始。使用实验数据作为输入,我们首先考虑了基于资源的竞争的平均场模型,该模型很好地捕捉到了经验上观察到的单一栽培的增长率,但未能正确预测共同栽培混合物中的生长率,特别是对于偏斜的起始物种比率。基于此,我们通过交叉喂养相互作用扩展了模型,其中一个消费者对底物的消耗产生代谢物,而代谢物又是另一个消费者的资源,从而在物种系统中产生正反馈。考虑了两种不同的交叉喂养选择,这两种选择要么导致持续的代谢物交叉喂养,要么导致一种受调节的形式,其中代谢物的利用率根据阈值或Hill函数激活,取决于代谢物浓度。数学证明和实验数据都表明,调节交叉喂养是恒定代谢产物利用的首选模型,在Hill系数高、接近二元(开/关)激活状态的情况下,具有最佳的共培养生长预测。这表明,物种只有在达到足够高的浓度时才会使用出现的代谢物浓度;可能是由于它们的能量含量低于主要基质。代谢产物共享在起始细胞比例不平衡的情况下尤其重要,由于多数伴侣释放代谢产物,导致少数伴侣从竞争底物中增殖得比预期的更多。因此,这种效应可能会平息直接的基质竞争,并且在具有典型的非常偏斜的相对类群丰度和生长较慢的类群的自然群落中可能很重要。总之,受调控的细菌相互作用网络正确地描述了混合物中物种-底物的生长反应,而从单一栽培生长实验中可以获得的动力学参数很少。
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Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding.

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments.

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PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
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期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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