大肠杆菌黏合表面积与体积的异速变化:大小变异、成丝和分裂动力学的影响。

IF 2 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Tanvi Kale, Dhruv Khatri, Chaitanya A Athale
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引用次数: 0

摘要

细胞表面积(SA)随体积(V)的增加是由细胞的生长和大小形状的调节决定的。大多数杆状模型细菌大肠杆菌的研究集中在现象或控制这种缩放的分子机制上。在这里,我们继续通过显微镜,图像分析和统计模拟的组合来检查群体统计和细胞分裂动力学在这种缩放中的作用。我们发现,虽然从中对数培养中取样的细胞的SA随V按比例指数2/3缩放,即几何定律SA ~ V2/3,但丝状细胞具有更高的指数值。我们通过调节生长速率来改变丝状细胞的比例,发现SA-V的指数大于2/3,超出了几何标度定律的预测。然而,由于增加的增长率改变了种群细胞大小分布的平均值和扩散,我们使用统计建模来消除平均大小和变异性之间的影响。模拟(i)以恒定的标准偏差(sd)增加平均细胞长度,(ii)以恒定的平均长度增加sd,以及(iii)同时变化两者,结果导致缩放指数超过2/3几何定律,当包含种群变异时,sd具有更强的作用。为了克服未同步细胞群体的统计抽样可能产生的影响,我们通过使用图像分析管道识别的出生和分裂之间的帧来“虚拟同步”细胞的时间序列,并将它们分为四个等间隔的阶段-B, C1, C2和d。从这些时间序列中估计的阶段特异性缩放指数和细胞长度可变性都发现随着出生的连续阶段(B), C1,这些结果表明,在估计细菌细胞的SA-V缩放时,需要考虑群体统计和细胞生长和分裂的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Allometry ofEscherichia colisurface area with volume: effect of size variability, filamentation and division dynamics.

The cell surface area (SA) increase with volume (V) is determined by growth and regulation of size and shape. Most studies of the rod-shaped model bacteriumEscherichia colihave focussed on the phenomenology or molecular mechanisms governing such scaling. Here, we proceed to examine the role of population statistics and cell division dynamics in such scaling by a combination of microscopy, image analysis and statistical simulations. We find that while the SA of cells sampled from mid-log cultures scales with V by a scaling exponent 2/3, i.e. the geometric law SA ∼V2/3, filamentous cells have higher exponent values. We modulate the growth rate to change the proportion of filamentous cells, and find SA-V scales with an exponent>2/3, exceeding that predicted by the geometric scaling law. However, since increasing growth rates alter the mean and spread of population cell size distributions, we use statistical modeling to disambiguate between the effect of the mean size and variability. Simulating (i) increasing mean cell length with a constant standard deviation (s.d.), (ii) a constant mean length with increasing s.d. and (iii) varying both simultaneously, results in scaling exponents that exceed the 2/3 geometric law, when population variability is included, with the s.d. having a stronger effect. In order to overcome possible effects of statistical sampling of unsynchronized cell populations, we 'virtually synchronized' time-series of cells by using the frames between birth and division identified by the image-analysis pipeline and divided them into four equally spaced phases-B, C1, C2 and D. Phase-specific scaling exponents estimated from these time series and the cell length variability were both found to decrease with the successive stages of birth (B), C1, C2 and division (D). These results point to a need to consider population statistics and a role for cell growth and division when estimating SA-V scaling of bacterial cells.

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来源期刊
Physical biology
Physical biology 生物-生物物理
CiteScore
4.20
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.
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