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数据驱动模型揭示了对粘细菌聚集至关重要的突变细胞行为。

Data-Driven Models Reveal Mutant Cell Behaviors Important for Myxobacterial Aggregation.

作者信息

Zhang Zhaoyang, Cotter Christopher R, Lyu Zhe, Shimkets Lawrence J, Igoshin Oleg A

机构信息

Department of Bioengineering and Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA.

Department of Microbiology, University of Georgia, Athens, Georgia, USA.

出版信息

mSystems. 2020 Jul 14;5(4):e00518-20. doi: 10.1128/mSystems.00518-20.

Abstract

Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, and , that cannot aggregate unless mixed with wild-type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT, demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust. Self-organization into spatial patterns is evident in many multicellular phenomena. Even for the best-studied systems, our ability to dissect the mechanisms driving coordinated cell movement is limited. While genetic approaches can identify mutations perturbing multicellular patterns, the diverse nature of the signaling cues coupled to significant heterogeneity of individual cell behavior impedes our ability to mechanistically connect genes with phenotype. Small differences in the behaviors of mutant strains could be irrelevant or could sometimes lead to large differences in the emergent patterns. Here, we investigate rescue of multicellular aggregation in two mutant strains of mixed with wild-type cells. The results demonstrate how careful quantification of cell behavior coupled to data-driven modeling can identify specific motility features responsible for cell aggregation and thereby reveal important synergies and compensatory mechanisms. Notably, mutant cells do not need to precisely recreate wild-type behaviors to achieve complete aggregation.

摘要

单个突变常常会改变细胞行为的多个方面,但很少能揭示特定的具有统计学意义的变化是否具有生物学意义。为了确定哪些行为变化对多细胞自组织最为重要,我们设计了一种新方法,以[具体模型系统名称未给出]作为模型系统。在发育过程中,黏细菌协调它们的运动以聚集形成充满孢子的子实体。我们研究了在两种突变体[具体突变体名称未给出]和[具体突变体名称未给出]中聚集是如何恢复的,这两种突变体除非与野生型(WT)细胞混合否则无法聚集。为此,我们结合基于数据驱动的智能体建模,使用细胞追踪来跟踪荧光标记细胞的运动。结果表明,与WT细胞一样,这两种突变体都将其运动偏向聚集物,并降低聚集物内部的运动性。然而,WT并未纠正突变体行为的几个方面,这表明完美重现WT行为并非必要。事实上,错误行为之间的协同作用可以使聚集变得稳健。在许多多细胞现象中,自组织成空间模式是很明显的。即使对于研究得最透彻的系统,我们剖析驱动细胞协调运动机制的能力也是有限的。虽然遗传方法可以识别扰乱多细胞模式的突变,但与个体细胞行为的显著异质性相关的信号线索的多样性阻碍了我们将基因与表型进行机械连接的能力。突变菌株行为的微小差异可能无关紧要,有时也可能导致涌现模式的巨大差异。在这里,我们研究了与野生型细胞混合的两种[具体物种名称未给出]突变菌株中多细胞聚集的恢复情况。结果表明,结合数据驱动建模对细胞行为进行仔细量化如何能够识别负责细胞聚集的特定运动特征,从而揭示重要的协同作用和补偿机制。值得注意的是,突变细胞不需要精确重现野生型行为就能实现完全聚集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4b6/7363006/09cc8647ea8a/mSystems.00518-20-f0001.jpg

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