细菌在分解复杂的生物聚合物时需要权衡利弊。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-08-08 eCollection Date: 2024-08-01 DOI:10.1371/journal.pcbi.1012320
Ksenia Guseva, Moritz Mohrlok, Lauren Alteio, Hannes Schmidt, Shaul Pollak, Christina Kaiser
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引用次数: 0

摘要

虽然复杂碳水化合物的解聚是限制微生物分解者生长的瓶颈,但我们仍然不了解不同类型胞外酶的产生如何影响单个微生物,进而影响整个分解者群落的表现。在这项工作中,我们利用一个理论模型来评估微生物在生物聚合物分解过程中可能面临的权衡,这些权衡是由于不同解聚酶类的生物化学特性不同而产生的。我们特别考虑了广泛存在于微生物类群中的两大类细胞外解聚酶:从聚合物链末端裂解小单元的外解聚酶和在随机位置产生不同大小降解产物的内解聚酶。我们的研究结果表明,在生产这些酶的过程中,存在着一种根本性的权衡,这种权衡与系统的复杂性无关,而完全来自于本质上不同的时间性解聚动态。因此,产生外源酶或只产生内源酶的专性生物分别将其生长限制在高或低底物条件下。相反,以最佳比例生产两种酶的通才扩大了它们的生态位,并从两种酶的协同作用中获益。最后,我们的研究结果表明,在空间明确的环境中,内专性和外专性组成的联合体只能在低营养条件下存在。总之,我们的分析表明,解聚途径的(进化或生态)选择会影响微生物在低基质或高基质条件下的适应性,并对微生物群落的生态动态产生影响。它提供了一种可能的解释,说明为什么自然界中的许多多糖降解器都具有产生这两种酶类的遗传潜力。
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Bacteria face trade-offs in the decomposition of complex biopolymers.

Although depolymerization of complex carbohydrates is a growth-limiting bottleneck for microbial decomposers, we still lack understanding about how the production of different types of extracellular enzymes affect individual microbes and in turn the performance of whole decomposer communities. In this work we use a theoretical model to evaluate the potential trade-offs faced by microorganisms in biopolymer decomposition which arise due to the varied biochemistry of different depolymerizing enzyme classes. We specifically consider two broad classes of depolymerizing extracellular enzymes, which are widespread across microbial taxa: exo-enzymes that cleave small units from the ends of polymer chains and endo-enzymes that act at random positions generating degradation products of varied sizes. Our results demonstrate a fundamental trade-off in the production of these enzymes, which is independent of system's complexity and which appears solely from the intrinsically different temporal depolymerization dynamics. As a consequence, specialists that produce either exo- or only endo-enzymes limit their growth to high or low substrate conditions, respectively. Conversely, generalists that produce both enzymes in an optimal ratio expand their niche and benefit from the synergy between the two enzymes. Finally, our results show that, in spatially-explicit environments, consortia composed of endo- and exo-specialists can only exist under oligotrophic conditions. In summary, our analysis demonstrates that the (evolutionary or ecological) selection of a depolymerization pathway will affect microbial fitness under low or high substrate conditions, with impacts on the ecological dynamics of microbial communities. It provides a possible explanation why many polysaccharide degraders in nature show the genetic potential to produce both of these enzyme classes.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
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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