The impact of interactions on invasion and colonization resistance in microbial communities.
The impact of interactions on invasion and colonization resistance in microbial communities.
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相互作用对微生物群落侵袭和抗性抗性的影响。
DOI：
10.1371/journal.pcbi.1008643
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发表时间：
2021-01
影响因子：
4.3
通讯作者：
Momeni B
中科院分区：
文献类型：
--
作者：
Kurkjian HM;Akbari MJ;Momeni B
在人类微生物群中，预防或促进入侵对人类健康至关重要。入侵的结果，反过来，居民社区的组成和居民成员与入侵者的相互作用的影响。在这里，我们研究了相互作用如何影响微生物群落中的入侵结果，当相互作用主要由释放到环境中或从环境中消耗的化学物质介导时。我们使用以前开发的动态模型，其中明确包括物种丰度和化学品的浓度，介导物种的相互作用。使用这个模型，我们评估了物种之间的相互作用如何影响入侵模拟一个新的物种被引入到现有的居民社区。我们将入侵的结果分为抵抗、增强、流离失所或破坏，这取决于居民社区的丰富程度是维持还是减少，以及入侵者是维持在社区中还是灭绝。我们发现，随着引入居民社区的入侵者数量的增加，破坏而不是增强变得更加普遍。随着居民社区为入侵者提供更多便利，抵抗的结果被流离失所和扩大所取代。相比之下，随着居民提供更多便利，流离失所的结果转变为抵抗。当入侵者对居民社区的促进作用被消除时，大多数增强结果变成了流离失所，而当入侵者对居民的抑制作用被消除时，入侵结果基本上不受影响。我们的研究结果表明，更好地了解居民社区内以及居民与入侵者之间的相互作用对于预测入侵微生物群落的成功至关重要。我们的常驻微生物群可以通过使病原体更难生长和建立来预防疾病，这种现象称为“殖民抵抗”。定植抗性是人类相关微生物群提供的主要益处之一，也是使用抗生素预防或治疗感染的可行替代方案。在这里，我们使用一个模型的微生物相互作用，通过生产和消费的代谢化合物来测定入侵和殖民抵抗力。我们在模拟中系统地研究了居民成员之间的相互作用以及居民与入侵者之间的相互作用如何影响殖民抵抗和入侵结果。在我们的模拟中，增加益生菌剂量的常见策略通常无法成功地将新物种增加到常驻微生物群中。相反，我们发现，居民成员和入侵者之间的净促进或抑制解释了社区是否保持完整以及入侵者是否能够建立。我们的研究结果表明，更好地了解微生物相互作用可以为成功的微生物群干预提供信息。
In human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions of resident members with the invader. Here we study how interactions influence invasion outcomes in microbial communities, when interactions are primarily mediated by chemicals that are released into or consumed from the environment. We use a previously developed dynamic model which explicitly includes species abundances and the concentrations of chemicals that mediate species interaction. Using this model, we assessed how species interactions impact invasion by simulating a new species being introduced into an existing resident community. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or decreased and whether the invader was maintained in the community or went extinct. We found that as the number of invaders introduced into the resident community increased, disruption rather than augmentation became more prevalent. With more facilitation of the invader by the resident community, resistance outcomes were replaced by displacement and augmentation. By contrast, with more facilitation among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, the majority of augmentation outcomes turned into displacement, while when inhibition of residents by invaders was eliminated, invasion outcomes were largely unaffected. Our results suggest that a better understanding of interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities. Our resident microbiota can prevent diseases by making it harder for pathogens to grow and establish, a phenomenon called “colonization resistance.” Colonization resistance is one of the major benefits provided by human-associated microbiota and a viable alternative to the use of antibiotics for preventing or treating infections. Here we use a model of microbial interactions through production and consumption of metabolic compounds to assay invasion and colonization resistance. We systematically examine in simulations how interactions among resident members and those between residents and an invader impact colonization resistance and invasion outcomes. In our simulations, the common strategy of increasing the dosage of probiotics is often unsuccessful for augmenting a new species into a resident microbiota. Instead, we find that the net facilitation or inhibition between the resident members and the invader explains whether the community remains intact and whether the invader can establish. Our results suggest that a better understanding of microbial interactions can inform successful microbiota interventions.
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DOI：
10.1038/nri3535
发表时间：
2013-11
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
影响因子：
2.6
作者：
Gritz EC;Bhandari V
通讯作者：
Bhandari V
影响因子：
2.8
作者：
Gordon, DM;Riley, MA
通讯作者：
Riley, MA
DOI：
10.1073/pnas.1505204112
发表时间：
2015-09-15
影响因子：
11.1
作者：
Acosta, Francisco;Zamor, Richard M.;Hambright, K. David
通讯作者：
Hambright, K. David
DOI：
10.1038/nrgastro.2010.117
发表时间：
2010-09
期刊：
Nature reviews. Gastroenterology & hepatology
影响因子：
--
作者：
Gareau MG;Sherman PM;Walker WA
通讯作者：
Walker WA