Abstract P130: P-cadherin activates metabolic coupling in the tumor-mesothelial niche: From glycolysis through lactate to lipogenesis

2021 ◽  
Author(s):  
Kun Wang ◽  
Jing Ma ◽  
Carmen C. L. Wong ◽  
Chi Bun Chan ◽  
Alice S. T. Wong
Keyword(s):  
Metabolic Coupling

scholarly journals Dendritic Fibroblasts in Three-dimensional Collagen Matrices

2003 ◽  
Vol 14 (2) ◽  
pp. 384-395 ◽  
Author(s):  
Frederick Grinnell ◽  
Chin-Han Ho ◽  
Elisa Tamariz ◽  
David J. Lee ◽  
Gabriella Skuta
Keyword(s):  
Dimensional Space ◽  
Human Fibroblasts ◽  
Three Dimensional ◽  
Rho Kinase ◽  
Matrix Remodeling ◽  
Dependent Manner ◽  
Collagen Matrices ◽  
Form And Function ◽  
Metabolic Coupling ◽  
Dendritic Network

Cell motility determines form and function of multicellular organisms. Most studies on fibroblast motility have been carried out using cells on the surfaces of culture dishes. In situ, however, the environment for fibroblasts is the three-dimensional extracellular matrix. In the current research, we studied the morphology and motility of human fibroblasts embedded in floating collagen matrices at a cell density below that required for global matrix remodeling (i.e., contraction). Under these conditions, cells were observed to project and retract a dendritic network of extensions. These extensions contained microtubule cores with actin concentrated at the tips resembling growth cones. Platelet-derived growth factor promoted formation of the network; lysophosphatidic acid stimulated its retraction in a Rho and Rho kinase-dependent manner. The dendritic network also supported metabolic coupling between cells. We suggest that the dendritic network provides a mechanism by which fibroblasts explore and become interconnected to each other in three-dimensional space.

scholarly journals Neuron-Astrocyte Metabolic Coupling Protects against Activity-Induced Fatty Acid Toxicity

Cell ◽  
2019 ◽  
Vol 177 (6) ◽  
pp. 1522-1535.e14 ◽  
Author(s):  
Maria S. Ioannou ◽  
Jesse Jackson ◽  
Shu-Hsien Sheu ◽  
Chi-Lun Chang ◽  
Aubrey V. Weigel ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Metabolic Coupling ◽  
Acid Toxicity

scholarly journals Astrocyte sodium signaling and neuro-metabolic coupling in the brain

Neuroscience ◽  
2016 ◽  
Vol 323 ◽  
pp. 121-134 ◽  
Author(s):  
C.R. Rose ◽  
J.-Y. Chatton
Keyword(s):  
Metabolic Coupling ◽  
The Brain

scholarly journals Transcriptional Evidence for Inferred Pattern of Pollen Tube-Stigma Metabolic Coupling during Pollination

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107046 ◽  
Author(s):  
Xun Yue ◽  
Xin-Qi Gao ◽  
Fang Wang ◽  
YuXiu Dong ◽  
XingGuo Li ◽  
...  
Keyword(s):  
Pollen Tube ◽  
Metabolic Coupling

scholarly journals Acute effect of lactic acid on tumor-endothelial cell metabolic coupling in the tumor microenvironment

2016 ◽  
Vol 12 (5) ◽  
pp. 3478-3484 ◽  
Author(s):  
Guanqun Zhu ◽  
Degui Wang ◽  
Shenqian Li ◽  
Xuecheng Yang ◽  
Yanwei Cao ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Endothelial Cell ◽  
Tumor Microenvironment ◽  
Acute Effect ◽  
Tumor Endothelial Cell ◽  
Metabolic Coupling

scholarly journals Cancer-Associated Fibroblasts Accelerate Malignant Progression of Non-Small Cell Lung Cancer via Connexin 43-Formed Unidirectional Gap Junctional Intercellular Communication

2018 ◽  
Vol 51 (1) ◽  
pp. 315-336 ◽  
Author(s):  
Min Luo ◽  
Yanmei Luo ◽  
Naiquan Mao ◽  
Guolin Huang ◽  
Cuifang Teng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Intercellular Communication ◽  
Connexin 43 ◽  
Small Cell ◽  
Migration And Invasion ◽  
Cancer Associated Fibroblasts ◽  
Gap Junctional Intercellular Communication ◽  
Small Cell Lung ◽  
Metabolic Coupling ◽  
Gap Junctional

Background/Aims: Gap junctions, which are assembled by connexins, can directly connect the cytoplasm of adjacent cells and enable gap junctional intercellular communication (GJIC) as well as metabolic coupling between neighboring cells. Here, we investigated the role of connexin 43 (Cx43) and its derived GJIC in the interplay between non-small cell lung cancer (NSCLC) cells and cancer-associated fibroblasts (CAFs). Methods: CAFs and NSCLC cells were co-cultured with direct contact and separated using flow cytometry. Glucose uptake, lactate production, and the expression and activity of PKM-2 and LDH-A in sorted CAFs were measured by a colorimetric assay, western blotting, and enzyme-linked immunosorbent assay (ELISA). Meanwhile, E-cadherin and N-cadherin expression and the migration and invasion of sorted NSCLC cells were detected by western blotting, wound width, and Transwell assays. Pyruvate, acetyl-CoA, and citric acid levels, ATP levels, and LDH-B and α-KG activity in sorted NSCLC cells were determined by a colorimetric or fluorometric assay and ELISA, respectively. Functional GJIC between cells and the subcellular location of connexins were detected by a “Parachute” assay and immunofluorescence. Levels of α-SMA, Cx43, and LDH-B in tissue from patients with NSCLC were determined by immunohistochemistry. Results: Cx43 accumulated in the plasma membrane, which favored the assembly of asymmetric unidirectional GJIC from CAFs to NSCLC cells. CAFs underwent increased aerobic glycolysis and promoted the epithelial-mesenchymal transition, migration, and invasion of NSCLC cells. In contrast, NSCLC cells experienced enhanced oxidative phosphorylation upon CAF stimulation, with an increase in ATP generation and thereby activation of the PI3K/Akt and MAPK/ERK pathways. Metabolic coupling between CAFs and NSCLC cells was under the strict control of Cx43-formed unidirectional GJIC. Patients with high tri-expression of α-SMA, Cx43, and LDH-B had the shortest overall survival and relapse-free survival compared with those with individual overexpression or high bi-expression. Conclusion: Cx43-formed unidirectional GJIC plays a critical role in mediating close metabolic cooperation between CAFs and NSCLC cells to support the malignant progression of NSCLC.

Quantification of gap junctional intercellular communication based on digital image analysis

2009 ◽  
Vol 297 (2) ◽  
pp. R243-R247 ◽  
Author(s):  
Johannes P. Hofgaard ◽  
Sarah Mollerup ◽  
Niels-Henrik Holstein-Rathlou ◽  
Morten Schak Nielsen
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Intercellular Communication ◽  
Connexin 43 ◽  
Digital Image Analysis ◽  
Fluorescent Dyes ◽  
Cell Counting ◽  
C6 Glioma Cells ◽  
Gap Junctional Intercellular Communication ◽  
Metabolic Coupling

Intercellular communication via gap junction channels can be quantified by several methods based on diffusion of fluorescent dyes or metabolites. Given the variation in intercellular coupling of cells, even under untreated control conditions, it is of essence to quantify the coupling between numerous cells to obtain reliable estimates of metabolic coupling. Quantification is often based on manual counting of fluorescent cells, which is time consuming and may include some degree of subjectivity. In this report, we introduce a technique based on digital image analysis, and the software for the analysis is presented together with a detailed protocol in the online supplemental material ( http://bmi.ku.dk/matlab_program/ ). Fluorescent dye was introduced in connexin 43-expressing C6 glioma cells by in situ electroporation, and fluorescence intensity was measured in the electroporated cells and in cells receiving dye by intercellular diffusion. The analysis performed is semiautomatic, and comparison with traditional cell counting shows that this method reliably determines the effect of uncoupling by several interventions. This new method of analysis yields a rapid and objective quantification process with a high degree of reproducibility.

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Model Development ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Intercellular Signaling ◽  
Growth Data ◽  
Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

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