Interdependence of Cell Growth and Gene Expression: Origins and Consequences

Science ◽  
2010 ◽  
Vol 330 (6007) ◽  
pp. 1099-1102 ◽  
Author(s):  
M. Scott ◽  
C. W. Gunderson ◽  
E. M. Mateescu ◽  
Z. Zhang ◽  
T. Hwa
Keyword(s):  
Gene Expression ◽  
Cell Growth

scholarly journals Effects of low-to-moderate ethanol consumption on colonic growth and gene expression in young adult and middle-aged male rats

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243499
Author(s):  
Nicole Wells ◽  
Jacqueline Quigley ◽  
Jeremy Pascua ◽  
Natalie Pinkowski ◽  
Lama Almaiman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cell Proliferation ◽  
Cell Differentiation ◽  
Young Adult ◽  
Cell Growth ◽  
Alcohol Consumption ◽  
Ethanol Consumption ◽  
Ethanol Treatment ◽  
Middle Aged

Excessive alcohol consumption is a risk factor associated with colorectal cancer; however, some epidemiological studies have reported that moderate alcohol consumption may not contribute additional risk or may provide a protective effect reducing colorectal cancer risk. Prior research highlights the importance of proliferation, differentiation, and apoptosis as parameters to consider when evaluating colonic cell growth and tumorigenesis. The present study investigated whether chronic low-to-moderate ethanol consumption altered these parameters of colonic cell growth and expression of related genes. Twenty-four nondeprived young adult (109 days old) and 24 nondeprived middle-aged (420 days old) Wistar rats were randomly assigned to an ethanol-exposed or a water control group (n = 12/group). The ethanol group was provided voluntary access to a 20% v/v ethanol solution on alternate days for 13 weeks. Colon tissues were collected for quantitative immunohistochemical analyses of cell proliferation, differentiation and apoptosis using Ki-67, goblet cell and TUNEL, respectively. Gene expression of cyclin D1 (Ccnd1), Cdk2, Cdk4, p21waf1/cip1 (Cdkn1a), E-cadherin (Cdh1) and p53 were determined by quantitative real-time polymerase chain reaction in colonic scraped mucosa. Ethanol treatment resulted in a lower cell proliferation index and proliferative zone, and lower Cdk2 expression in both age groups, as well as trends toward lower Ccnd1 and higher Cdkn1a expression. Cell differentiation was modestly but significantly reduced by ethanol treatment only in older animals. Overall, older rats showed decreases in apoptosis and gene expression of Cdk4, Cdh1, and p53 compared to younger rats, but there was no observed effect of ethanol exposure on these measures. These findings suggest that low-to-moderate ethanol consumption improves at least one notable parameter in colonic tumorigenesis (cell proliferation) and associated gene expression regardless of age, however, selectively decreased cell differentiation among older subjects.

scholarly journals The Tor Pathway Regulates Gene Expression by Linking Nutrient Sensing to Histone Acetylation

2003 ◽  
Vol 23 (2) ◽  
pp. 629-635 ◽  
Author(s):  
John R. Rohde ◽  
Maria E. Cardenas
Keyword(s):  
Gene Expression ◽  
Cell Growth ◽  
Histone Acetylation ◽  
Nutrient Sensing ◽  
Gene Promoters ◽  
Histone H4 ◽  
Tor Pathway ◽  
Histone H4 Acetylation ◽  
Promoter Occupancy ◽  
Histone H4 Deacetylation

ABSTRACT The Tor pathway mediates cell growth in response to nutrient availability, in part by inducing ribosomal protein (RP) gene expression via an unknown mechanism. Expression of RP genes coincides with recruitment of the Esa1 histone acetylase to RP gene promoters. We show that inhibition of Tor with rapamycin releases Esa1 from RP gene promoters and leads to histone H4 deacetylation without affecting promoter occupancy by Rap1 and Abf1. Genetic and biochemical evidence identifies Rpd3 as the major histone deacetylase responsible for reversing histone H4 acetylation at RP gene promoters in response to Tor inhibition by rapamycin or nutrient limitation. Our results illustrate that the Tor pathway links nutrient sensing with histone acetylation to control RP gene expression and cell growth.

scholarly journals Tunable stiffness of graphene oxide/polyacrylamide composite scaffolds regulates cytoskeleton assembly

2018 ◽  
Vol 9 (31) ◽  
pp. 6516-6522 ◽  
Author(s):  
Yupeng Sun ◽  
Kaixiang Zhang ◽  
Ruijie Deng ◽  
Xiaojun Ren ◽  
Can Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Graphene Oxide ◽  
Cell Growth ◽  
Specific Gene ◽  
Composite Scaffolds ◽  
Specific Gene Expression ◽  
Tunable Stiffness

Graphene oxide/polyacrylamide composite scaffolds with tunable stiffness are designed and fabricated to investigate the effect of extracellular matrix (ECM) stiffness on cytoskeleton assembly and specific gene expression during cell growth.

Control of hematopoietic cell growth regulators during mouse fetal development

1987 ◽  
Vol 7 (9) ◽  
pp. 3361-3364
Author(s):  
M Azoulay ◽  
C G Webb ◽  
L Sachs
Keyword(s):  
Gene Expression ◽  
Cell Growth ◽  
Growth Regulators ◽  
Fetal Development ◽  
Hematopoietic Cell ◽  
Regulatory Proteins ◽  
Colony Stimulating Factor ◽  
Cell Lineages ◽  
Stimulating Factor ◽  
Extraembryonic Tissues

Gene expression for the four different growth-regulatory proteins for cells of the myeloid hematopoietic cell lineages was analyzed in mouse fetal and extraembryonic tissues at various stages of development. The macrophage growth inducer MGI-1M (colony-stimulating factor 1) was the only myeloid hematopoietic growth regulator detected as both mRNA and bioactive protein during fetal development. This regulator was produced predominantly in extraembryonic tissues, and the production of hematopoietic growth regulators in embryogenesis was regulated by transcriptional and posttranscriptional controls.

scholarly journals Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics

2020 ◽  
Vol 7 (7) ◽  
pp. 191243
Author(s):  
Ayoub Lasri ◽  
Viktorija Juric ◽  
Maité Verreault ◽  
Franck Bielle ◽  
Ahmed Idbaih ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Cell Growth ◽  
Dna Methyltransferase ◽  
Computational Models ◽  
Phenotypic Selection ◽  
Stochastic Gene Expression ◽  
Phenotypic Resistance ◽  
Methylguanine Dna Methyltransferase ◽  
Wide Range

Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied—particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.

scholarly journals A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

2020 ◽  
Vol 117 (31) ◽  
pp. 18869-18879 ◽  
Author(s):  
Christopher Culley ◽  
Supreeta Vijayakumar ◽  
Guido Zampieri ◽  
Claudio Angione
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Cell Growth ◽  
Yeast Cell ◽  
Metabolic Flux ◽  
Data Augmentation ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Metabolic Reconstruction

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143Saccharomyces cerevisiaemutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

Gene expression profile of endothelin-1-induced growth in glomerular mesangial cells

2003 ◽  
Vol 285 (5) ◽  
pp. C1109-C1115 ◽  
Author(s):  
Rangnath Mishra ◽  
Patrick Leahy ◽  
Michael S. Simonson
Keyword(s):  
Gene Expression ◽  
Growth Factor ◽  
Cell Growth ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Kinase Inhibitor ◽  
Mesangial Cells ◽  
Growth Control ◽  
Glomerular Mesangial Cells ◽  
Regulated Gene Expression

Endothelin (ET)-1 is a vasoconstrictor and mitogen involved in vascular remodeling. Changes in gene expression that underlie control of cell growth by ET-1 remain poorly characterized. To identify pathways of growth control we used microarrays to analyze ET-1-regulated gene expression in human mesangial cells, an important ET-1 vascular target cell in vivo. Statistical assessment of differential expression (significance analysis of microarrays) revealed upregulated transcripts for growth factors [heparin-binding epidermal growth factor (EGF)-like growth factor (HB-EGF), fibroblast growth factor (FGF), interleukin (IL)-6] and downregulated transcripts for genes that inhibit growth (BAX, p27KIP1, DAD1). Consistent with the gene expression profile, quantitative RT-PCR and Western blotting confirmed induction of HB-EGF by ET-1. To test a functional role for HB-EGF in ET-1 signaling, we showed that exogenous HB-EGF stimulated phosphorylation of ErbB1 and growth of mesangial cells. ET-1-induced proliferation was blocked by an ErbB1 receptor-selective kinase inhibitor and by a specific ErbB1 receptor-neutralizing antibody. Proliferation in response to ET-1 was also inhibited by neutralizing antisera against human HB-EGF. Together, these results provide data for modeling ET-1 pathways for growth control and suggest a specific role for HB-EGF gene induction in mesangial cell growth in response to ET-1.

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