Environmental and ecological controls of the spatial distribution of microbial populations in aggregates.
Environmental and ecological controls of the spatial distribution of microbial populations in aggregates.
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DOI：
10.1371/journal.pcbi.1010807
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发表时间：
2022-12
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
在微生物群落中，不同种群物种之间的生态相互作用是导致聚集体（颗粒、生物膜或絮体）空间分布的原因。为了探索控制这些过程的潜在机制，我们开发了一个数学建模框架，能够描述，标记和量化定义的空间结构，这些结构来自社区中的微生物和环境相互作用。在不同的环境条件下，使用基于个体的建模来模拟一个由三个群体在一个聚集体中合作或竞争的人工系统。在这项研究中，中立主义，竞争，竞争主义和竞争主义和竞争的并存。我们能够确定种间分离的社区，出现在竞争环境中（列分层），和分层分布的人口出现在crossal（分层）。当不同的生态相互作用被认为是在同一个聚合物，由此产生的空间分布被确定为一个控制的最有限的基板。定义了一个理论模量，我们能够量化的环境条件和生态相互作用的影响，以预测最可能的空间分布。在我们的研究结果中观察到的特定微生物模式使我们能够确定细菌在建立微生物群落时茁壮成长的最佳空间组织，以及这如何允许不同生长速率的种群共存。我们的模型表明，虽然不同物种之间的生态关系决定了细菌的分布，但环境控制着群落的最终空间分布。微生物群落是微生物与环境相互作用的产物。为了充分理解和控制微生物聚集体的形成，我们需要解开细胞-细胞、细胞-环境和细胞-空间相互作用的原理。到目前为止，大多数研究主要集中在两种微生物之间的单一相互作用上。然而，微生物生态学比这更复杂，多种生态相互作用有助于微生物群落组装。确定不同的空间分布的细菌是理解的基本生物学机制，管理聚集体形成的第一步。在这里，我们表明，它是可以通过数学建模来评估多种生态相互作用和环境对微生物群落组装的影响。我们已经能够区分竞争中群落的种间分离和共栖中的分层分布。当我们考虑一个以上的生态种群之间的相互作用，由此产生的空间分布被确定为一个最有限的基板控制。此外，我们定义了一个理论模量，使我们能够预测在特定环境条件下的最可能的空间分布。
In microbial communities, the ecological interactions between species of different populations are responsible for the spatial distributions observed in aggregates (granules, biofilms or flocs). To explore the underlying mechanisms that control these processes, we have developed a mathematical modelling framework able to describe, label and quantify defined spatial structures that arise from microbial and environmental interactions in communities. An artificial system of three populations collaborating or competing in an aggregate is simulated using individual-based modelling under different environmental conditions. In this study, neutralism, competition, commensalism and concurrence of commensalism and competition have been considered. We were able to identify interspecific segregation of communities that appears in competitive environments (columned stratification), and a layered distribution of populations that emerges in commensal (layered stratification). When different ecological interactions were considered in the same aggregate, the resultant spatial distribution was identified as the one controlled by the most limiting substrate. A theoretical modulus was defined, with which we were able to quantify the effect of environmental conditions and ecological interactions to predict the most probable spatial distribution. The specific microbial patterns observed in our results allowed us to identify the optimal spatial organizations for bacteria to thrive when building a microbial community and how this permitted co-existence of populations at different growth rates. Our model reveals that although ecological relationships between different species dictate the distribution of bacteria, the environment controls the final spatial distribution of the community. Microbial communities are assembled by the interactions between microorganisms and the local environment. To fully understand and control the formation of microbial aggregates, we need to unravel the principles of both cell-cell, cell-environment and cell-space interactions. Until now, most studies have focused predominantly on single interactions between two microbes. However, microbial ecology is more complex than that, and multiple ecological interactions contribute to microbial community assembly. The identification of distinct spatial distributions of bacteria is a first step towards the understanding the underlying biological mechanisms that govern aggregate formation. Here, we show that it is possible to evaluate the influence of multiple ecological interactions and the environment on microbial community assembly through mathematical modelling. We have been able to distinguish interspecific segregation of communities in competition, and layered distribution in commensalism. When we considered more than one ecological interaction between populations, the resultant spatial distribution was identified as the one controlled by the most limiting substrate. Additionally, we defined a theoretical modulus that able us to predict the most probable spatial distribution under specific environmental conditions.
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影响因子：
11
作者：
Ciccarese, Davide;Micali, Gabriele;Borer, Benedict;Ruan, Chujin;Or, Dani;Johnson, David R.
通讯作者：
Johnson, David R.
影响因子：
16.8
作者：
Dal Co, Alma;van Vliet, Simon;Ackermann, Martin
通讯作者：
Ackermann, Martin
影响因子：
3
作者：
COSTERTON, JW;GEESEY, GG;CHENG, KJ
通讯作者：
CHENG, KJ
影响因子：
5
作者：
ALPHENAAR, PA;PEREZ, MC;LETTINGA, G
通讯作者：
LETTINGA, G
DOI：
10.1073/pnas.0710150104
发表时间：
2007-12-11
影响因子：
11.1
作者：
Hallatschek, Oskar;Hersen, Pascal;Nelson, David R.
通讯作者：
Nelson, David R.