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Cell shape and the microenvironment regulate nuclear translocation of NF-κB in breast epithelial and tumor cells.

Sero JE, Sailem HZ, Ardy RC, Almuttaqi H, Zhang T, Bakal C - Mol. Syst. Biol. (2015)

Bottom Line: Cell-cell contact, cell and nuclear area, and protrusiveness all contributed to variability in NF-κB localization in the absence and presence of TNFα.Higher levels of nuclear NF-κB were associated with mesenchymal-like versus epithelial-like morphologies, and RhoA-ROCK-myosin II signaling was critical for mediating shape-based differences in NF-κB localization and oscillations.Thus, mechanical factors such as cell shape and the microenvironment can influence NF-κB signaling and may in part explain how different phenotypic outcomes can arise from the same chemical cues.

View Article: PubMed Central - PubMed

Affiliation: Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, UK juliasero@post.harvard.edu chris.bakal@icr.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Bayesian network dependency models infer connections between shape and transcription factor localization using intrinsic heterogeneity of single cellsLeft: Distribution of nuclear/cytoplasmic NF-κB ratios in single cells ± TNFα (blue = unstimulated, red = TNFα 1 h, green = TNFα 5 h) showing distribution of values for each cell line. Right: Distribution of first principal component (PC) of morphology features showing multi-modal distribution of cell shapes.Example Bayesian network (MCF7). Edge (line/arrow) color denotes treatment conditions. Arrows indicate direction of dependency, and lines indicate interactions where direction cannot be determined. Numbers denote confidence (see Materials and Methods).Frequency of NF-κB ratio dependencies by cell line for the most commonly connected features ± TNFα (1 h).NF-κB dependencies on morphological factors detected in each condition. Red = dependency detected in unstimulated condition. Asterisk = dependency detected in TNFα-stimulated cell (1 h).
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fig02: Bayesian network dependency models infer connections between shape and transcription factor localization using intrinsic heterogeneity of single cellsLeft: Distribution of nuclear/cytoplasmic NF-κB ratios in single cells ± TNFα (blue = unstimulated, red = TNFα 1 h, green = TNFα 5 h) showing distribution of values for each cell line. Right: Distribution of first principal component (PC) of morphology features showing multi-modal distribution of cell shapes.Example Bayesian network (MCF7). Edge (line/arrow) color denotes treatment conditions. Arrows indicate direction of dependency, and lines indicate interactions where direction cannot be determined. Numbers denote confidence (see Materials and Methods).Frequency of NF-κB ratio dependencies by cell line for the most commonly connected features ± TNFα (1 h).NF-κB dependencies on morphological factors detected in each condition. Red = dependency detected in unstimulated condition. Asterisk = dependency detected in TNFα-stimulated cell (1 h).

Mentions: Given the differences in NF-κB activation between differently shaped breast cell lines, we next set out to determine whether NF-κB activation was related to morphology on the level of single cells. The distribution of NF-κB ratios varied between cell lines and conditions (Fig2A (left) and Supplementary Fig S2A), and in some cases, the cell-to-cell differences ranged over orders of magnitude (log ratio −1 to > 1). Moreover, all cell lines were morphologically heterogeneous. Figure2A (right) and Supplementary Fig S2B show the distribution of first principal component (PC1) scores of single cells based on 77 geometric shape and context features. Cell shape distributions were multi-modal, suggesting the existence of a finite number of morphological states (Yin et al, 2013; Sailem et al, 2014), and each cell line showed a different degree of heterogeneity.


Cell shape and the microenvironment regulate nuclear translocation of NF-κB in breast epithelial and tumor cells.

Sero JE, Sailem HZ, Ardy RC, Almuttaqi H, Zhang T, Bakal C - Mol. Syst. Biol. (2015)

Bayesian network dependency models infer connections between shape and transcription factor localization using intrinsic heterogeneity of single cellsLeft: Distribution of nuclear/cytoplasmic NF-κB ratios in single cells ± TNFα (blue = unstimulated, red = TNFα 1 h, green = TNFα 5 h) showing distribution of values for each cell line. Right: Distribution of first principal component (PC) of morphology features showing multi-modal distribution of cell shapes.Example Bayesian network (MCF7). Edge (line/arrow) color denotes treatment conditions. Arrows indicate direction of dependency, and lines indicate interactions where direction cannot be determined. Numbers denote confidence (see Materials and Methods).Frequency of NF-κB ratio dependencies by cell line for the most commonly connected features ± TNFα (1 h).NF-κB dependencies on morphological factors detected in each condition. Red = dependency detected in unstimulated condition. Asterisk = dependency detected in TNFα-stimulated cell (1 h).
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4380925&req=5

fig02: Bayesian network dependency models infer connections between shape and transcription factor localization using intrinsic heterogeneity of single cellsLeft: Distribution of nuclear/cytoplasmic NF-κB ratios in single cells ± TNFα (blue = unstimulated, red = TNFα 1 h, green = TNFα 5 h) showing distribution of values for each cell line. Right: Distribution of first principal component (PC) of morphology features showing multi-modal distribution of cell shapes.Example Bayesian network (MCF7). Edge (line/arrow) color denotes treatment conditions. Arrows indicate direction of dependency, and lines indicate interactions where direction cannot be determined. Numbers denote confidence (see Materials and Methods).Frequency of NF-κB ratio dependencies by cell line for the most commonly connected features ± TNFα (1 h).NF-κB dependencies on morphological factors detected in each condition. Red = dependency detected in unstimulated condition. Asterisk = dependency detected in TNFα-stimulated cell (1 h).
Mentions: Given the differences in NF-κB activation between differently shaped breast cell lines, we next set out to determine whether NF-κB activation was related to morphology on the level of single cells. The distribution of NF-κB ratios varied between cell lines and conditions (Fig2A (left) and Supplementary Fig S2A), and in some cases, the cell-to-cell differences ranged over orders of magnitude (log ratio −1 to > 1). Moreover, all cell lines were morphologically heterogeneous. Figure2A (right) and Supplementary Fig S2B show the distribution of first principal component (PC1) scores of single cells based on 77 geometric shape and context features. Cell shape distributions were multi-modal, suggesting the existence of a finite number of morphological states (Yin et al, 2013; Sailem et al, 2014), and each cell line showed a different degree of heterogeneity.

Bottom Line: Cell-cell contact, cell and nuclear area, and protrusiveness all contributed to variability in NF-κB localization in the absence and presence of TNFα.Higher levels of nuclear NF-κB were associated with mesenchymal-like versus epithelial-like morphologies, and RhoA-ROCK-myosin II signaling was critical for mediating shape-based differences in NF-κB localization and oscillations.Thus, mechanical factors such as cell shape and the microenvironment can influence NF-κB signaling and may in part explain how different phenotypic outcomes can arise from the same chemical cues.

View Article: PubMed Central - PubMed

Affiliation: Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, UK juliasero@post.harvard.edu chris.bakal@icr.ac.uk.

No MeSH data available.


Related in: MedlinePlus