scholarly journals E2F transcription factors are differentially expressed in murine gametes and early embryos

2000 ◽  
Vol 97 (1-2) ◽  
pp. 211-215 ◽  
Author(s):  
Antonella Palena ◽  
Rosamaria Mangiacasale ◽  
Anna Rosa Magnano ◽  
Laura Barberi ◽  
Roberto Giordano ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Differentially Expressed ◽  
Early Embryos ◽  
E2f Transcription Factors

The Role of Retinoblastoma Protein in Cell Cycle Regulation: An Updated Review

2021 ◽  
Vol 20 ◽  
Author(s):  
Rabih Roufayel ◽  
Rabih Mezher ◽  
Kenneth B. Storey
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Transcription Factors ◽  
Cell Cycle Control ◽  
Expression Of Genes ◽  
E2f Transcription Factor ◽  
Cellular Energetics ◽  
Development And Differentiation ◽  
E2f Transcription Factors ◽  
Rb Phosphorylation

: Selected transcription factors have critical roles to play in organism survival by regulating the expression of genes that control the adaptations needed to handle stress conditions. The retinoblastoma (Rb) protein coupled with the E2F transcription factor family was demonstrated to have roles in controlling the cell cycle during freezing and associated environmental stresses (anoxia, dehydration). Rb phosphorylation or acetylation at different sites provide a mechanism for repressing cell proliferation that is under the control of E2F transcription factors in animals facing stresses that disrupt cellular energetics or cell volume controls. Other central regulators of the cell cycle including Cyclins, Cyclin dependent kinases (Cdks), and checkpoint proteins detect DNA damage or any improper replication, blocking further progression of cell cycle and interrupting cell proliferation. This review provides an insight into the molecular regulatory mechanisms of cell cycle control, focusing on Rb-E2F along with Cyclin-Cdk complexes typically involved in development and differentiation that need to be regulated in order to survive extreme cellular stress.

scholarly journals Transcriptome analyses identify five transcription factors differentially expressed in the hypothalamus of post- versus prepubertal Brahman heifers1

2016 ◽  
Vol 94 (9) ◽  
pp. 3693-3702 ◽  
Author(s):  
M. R. S. Fortes ◽  
L. T. Nguyen ◽  
M. M. D. C. A. Weller ◽  
A. Cánovas ◽  
A. Islas-Trejo ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Differentially Expressed ◽  
Transcriptome Analyses

scholarly journals Transcriptome analyses and virus induced gene silencing identify genes in the Rpp4-mediated Asian soybean rust resistance pathway

2013 ◽  
Vol 40 (10) ◽  
pp. 1029 ◽  
Author(s):  
Aguida M. A. P. Morales ◽  
Jamie A. O'Rourke ◽  
Martijn van de Mortel ◽  
Katherine T. Scheider ◽  
Timothy J. Bancroft ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Silencing ◽  
Differentially Expressed ◽  
Soybean Rust ◽  
Phakopsora Pachyrhizi ◽  
R Gene ◽  
Virus Induced Gene Silencing ◽  
Asian Soybean Rust ◽  
Defence Responses

Rpp4 (Resistance to Phakopsora pachyrhizi 4) confers resistance to Phakopsora pachyrhizi Sydow, the causal agent of Asian soybean rust (ASR). By combining expression profiling and virus induced gene silencing (VIGS), we are developing a genetic framework for Rpp4-mediated resistance. We measured gene expression in mock-inoculated and P. pachyrhizi-infected leaves of resistant soybean accession PI459025B (Rpp4) and the susceptible cultivar (Williams 82) across a 12-day time course. Unexpectedly, two biphasic responses were identified. In the incompatible reaction, genes induced at 12 h after infection (hai) were not differentially expressed at 24 hai, but were induced at 72 hai. In contrast, genes repressed at 12 hai were not differentially expressed from 24 to 144 hai, but were repressed 216 hai and later. To differentiate between basal and resistance-gene (R-gene) mediated defence responses, we compared gene expression in Rpp4-silenced and empty vector-treated PI459025B plants 14 days after infection (dai) with P. pachyrhizi. This identified genes, including transcription factors, whose differential expression is dependent upon Rpp4. To identify differentially expressed genes conserved across multiple P. pachyrhizi resistance pathways, Rpp4 expression datasets were compared with microarray data previously generated for Rpp2 and Rpp3-mediated defence responses. Fourteen transcription factors common to all resistant and susceptible responses were identified, as well as fourteen transcription factors unique to R-gene-mediated resistance responses. These genes are targets for future P. pachyrhizi resistance research.

scholarly journals Regulation of Geminin and Cdt1 expression by E2F transcription factors

Oncogene ◽  
2004 ◽  
Vol 23 (21) ◽  
pp. 3802-3812 ◽  
Author(s):  
Kenichi Yoshida ◽  
Ituro Inoue
Keyword(s):  
Transcription Factors ◽  
E2f Transcription Factors

scholarly journals Genes Encoding Transcription Factors TaDREB5 and TaNFYC-A7 Are Differentially Expressed in Leaves of Bread Wheat in Response to Drought, Dehydration and ABA

2018 ◽  
Vol 9 ◽  
Author(s):  
Lyudmila Zotova ◽  
Akhylbek Kurishbayev ◽  
Satyvaldy Jatayev ◽  
Gulmira Khassanova ◽  
Askar Zhubatkanov ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Bread Wheat ◽  
Differentially Expressed ◽  
Genes Encoding

scholarly journals Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

2021 ◽  
Vol 8 ◽  
Author(s):  
Tingshan He ◽  
Liwen Huang ◽  
Jing Li ◽  
Peng Wang ◽  
Zhiqiao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Transcription Factors ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Immune Genes ◽  
Immune Biomarkers

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

scholarly journals A Comparison of Gene Expression Changes in the Blood of Individuals Consuming Diets Supplemented with Olives, Nuts or Long-Chain Omega-3 Fatty Acids

Nutrients ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3765
Author(s):  
Virginie Bottero ◽  
Judith A. Potashkin
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Fatty Acids ◽  
Transcription Factors ◽  
Mediterranean Diet ◽  
Differentially Expressed ◽  
Omega 3 Fatty Acids ◽  
Omega 3 ◽  
The Mediterranean ◽  
Long Chain

Background: The Mediterranean diet, which is rich in olive oil, nuts, and fish, is considered healthy and may reduce the risk of chronic diseases. Methods: Here, we compared the transcriptome from the blood of subjects with diets supplemented with olives, nuts, or long-chain omega-3 fatty acids and identified the genes differentially expressed. The dietary genes obtained were subjected to network analysis to determine the main pathways, as well as the transcription factors and microRNA interaction networks to elucidate their regulation. Finally, a gene-associated disease interaction network was performed. Results: We identified several genes whose expression is altered after the intake of components of the Mediterranean diets compared to controls. These genes were associated with infection and inflammation. Transcription factors and miRNAs were identified as potential regulators of the dietary genes. Interestingly, caspase 1 and sialophorin are differentially expressed in the opposite direction after the intake of supplements compared to Alzheimer’s disease patients. In addition, ten transcription factors were identified that regulated gene expression in supplemented diets, mild cognitive impairment, and Alzheimer’s disease. Conclusions: We identified genes whose expression is altered after the intake of the supplements as well as the transcription factors and miRNAs involved in their regulation. These genes are associated with schizophrenia, neoplasms, and rheumatic arthritis, suggesting that the Mediterranean diet may be beneficial in reducing these diseases. In addition, the results suggest that the Mediterranean diet may also be beneficial in reducing the risk of dementia.

scholarly journals Comprehensive Transcriptome Analysis Revealed the Effects of the Light Quality, Light Intensity, and Photoperiod on Phlorizin Accumulation in Lithocarpus polystachyus Rehd.

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 995 ◽  
Author(s):  
Yantong Zhang ◽  
Limei Lin ◽  
Yuehong Long ◽  
Hongyu Guo ◽  
Zhuo Wang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Light Intensity ◽  
Light Exposure ◽  
Southern China ◽  
Green Light ◽  
Differentially Expressed ◽  
Chromatography Method ◽  
Pal Activity ◽  
Study Results ◽  
Regulatory Effects

Lithocarpus polystachyus Rehd. is an important medicinal plant species grown in southern China, with phlorizin as its main active substance. The effects of light conditions on phlorizin biosynthesis in L. polystachyus remain unclear. Thus, we analyzed the transcriptomes of L. polystachyus plants cultivated under diverse light qualities, light intensities, and photoperiods. The light treatments resulted in 5977–8027 differentially expressed genes (DEGs), which were functionally annotated based on the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Genes encoding transcription factors from 89 families were differentially expressed after the light treatments, implying these transcription factors are photoresponsive. Phenylalanine ammonia lyase (PAL) and 4-coumarate-CoA ligase (4CL) are the key enzymes for the accumulation of phlorizin. The transcription levels of PAL2, PAL, 4CL1 (DN121614), 4CLL7, and 4CL1 (DN102161) were positively correlated with phlorizin accumulation, suggesting that these genes are important for phlorizin biosynthesis. An ultra-high-performance liquid chromatography method was used to quantify the phlorizin content. Phlorizin accumulated in response to the green light treatment and following appropriate decreases in the light intensity or appropriate increases in the duration of the light exposure. The green light, 2000 lx, and 3000 lx treatments increased the PAL activity of L. polystachyus, but the regulatory effects of the light intensity treatments on PAL activity were relatively weak. This study represents the first comprehensive analysis of the light-induced transcriptome of L. polystachyus. The study results may form the basis of future studies aimed at elucidating the molecular mechanism underlying phlorizin biosynthesis in L. polystachyus. Moreover, this study may be relevant for clarifying the regulatory effects of light on the abundance of bioactive components in medicinal plants.

A conserved role for γ-tubulin as a regulator of E2F transcription factors

2020 ◽  
Vol 71 (4) ◽  
pp. 1199-1202
Author(s):  
Cécile Raynaud ◽  
Maherun Nisa
Keyword(s):  
Transcription Factors ◽  
Experimental Botany ◽  
E2f Transcription Factors

This article comments on: Kállai BM, Kourová H, Chumová J, Papdi C, Trögelová L, Kofroňová O, Hozák P, Filimonenko V, Mészáros T, Magyar Z, Bögre L, Binarová P. 2020. γ-Tubulin interacts with E2F transcription factors to regulate proliferation and endocycling in Arabidopsis. Journal of Experimental Botany 71, 1265–1277.

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