E4BP4/NFIL3 modulates the epigenetically repressed RAS effector RASSF8 function through histone methyltransferases

Karthik, Isai Pratha; Desai, Pavitra; Sukumar, Sunanda; Dimitrijević, Aleksandra; Rajalingam, Krishnaraj; Mahalingam, Sundarasamy

doi:10.1074/jbc.ra117.000623

Cited by 10 publications

(5 citation statements)

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“…RASSF8 encodes a protein that is a member of the transmembrane 4 superfamily and is a lung tumor–suppressor gene candidate. It plays important roles in the regulation of localization, methylation, cell–cell adhesion, cell migration, cell death, response to hypoxia, mitosis, cell growth, wound healing, contact inhibition, and epithelial cell migration (Falvella et al, 2006 ; Wang et al, 2017 ; Karthik et al, 2018 ; Shi L. et al, 2018 ). Accumulated evidence suggests that RASSF8 is associated with aging (Geigl et al, 2004 ; Shi Z. et al, 2018 ; Pagliai et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…For the best prediction model, we listed the top 50 genes (according to the absolute value of coefficients) and their coefficients in (Falvella et al, 2006;Wang et al, 2017;Karthik et al, 2018;. Accumulated evidence suggests that RASSF8 is associated with aging (Geigl et al, 2004;Shi Z. et al, 2018;Pagliai et al, 2019).…”

Section: Optimal Gene Set Of Predicted Age and Functional Analysismentioning

confidence: 99%

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Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

et al. 2020

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Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available. In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues. The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.

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Section: Resultsmentioning

confidence: 99%

Section: Optimal Gene Set Of Predicted Age and Functional Analysismentioning

confidence: 99%

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

et al. 2020

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“…RASSF8 plays a role in cell growth, regulation of the Wnt and NFκB pathways, and cell-cell adhesion ( Lock et al, 2010 ). Moreover, the RASSF8 overexpression has been found to affect the process of cell growth, cell apoptosis ( Karthik et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Gene Expression Profiles Reveal Distinct Molecular Characteristics of the Goose Granulosa Cells

Gao

Zhang

et al. 2021

Front. Genet.

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Granulosa cells (GCs) are decisive players in follicular development. In this study, the follicle tissues and GCs were isolated from the goose during the peak-laying period to perform hematoxylin-eosin staining and RNA-seq, respectively. Moreover, the dynamic mRNA and lncRNA expression profiles and mRNA-lncRNA network analysis were integrated to identify the important genes and lncRNAs. The morphological analysis showed that the size of the GCs did not significantly change, but the thickness of the granulosa layer cells differed significantly across the developmental stages. Subsequently, 14,286 mRNAs, 3,956 lncRNAs, and 1,329 TUCPs (transcripts with unknown coding potential) were detected in the GCs. We identified 37 common DEGs in the pre-hierarchical and hierarchical follicle stages, respectively, which might be critical for follicle development. Moreover, 3,089 significant time-course DEGs (Differentially expressed genes) and 13 core genes in 4 clusters were screened during goose GCs development. Finally, the network lncRNA G8399 with CADH5 and KLF2, and lncRNA G8399 with LARP6 and EOMES were found to be important for follicular development in GCs. Thus, the results would provide a rich resource for elucidating the reproductive biology of geese and accelerate the improvement of the egg-laying performance of geese.

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“…Colony formation assay was performed as described previously (48). Percentage survival was calculated using the following formula: plating efficiency ϭ number of colonies counted/ number of cells plated; surviving fraction ϭ (number of colonies counted/number of cells plated)/plating efficiency of control.…”

Section: Colony Formation Assaymentioning

confidence: 99%

The non-enzymatic RAS effector RASSF7 inhibits oncogenic c-Myc function

Kumaraswamy

Mamidi

Desai

et al. 2018

Journal of Biological Chemistry

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is a proto-oncogene controlling expression of multiple genes involved in cell growth and differentiation. Although the functional role of c-Myc as a transcriptional regulator has been intensively studied, targeting this protein in cancer remains a challenge. Here, we report a trimodal regulation of c-Myc function by the Ras effector, Ras-association domain family member 7 (RASSF7), a nonenzymatic protein modulating protein-protein interactions to regulate cell proliferation. Using HEK293T and HeLa cell lines, we provide evidence that RASSF7 destabilizes the c-Myc protein by promoting Cullin4B-mediated polyubiquitination and degradation. Furthermore, RASSF7 competed with MYC-associated factor X (MAX) in the formation of a heterodimeric complex with c-Myc and attenuated its occupancy on target gene promoters to regulate transcription. Consequently, RASSF7 inhibited c-Myc-mediated oncogenic transformation, and an inverse correlation between the expression levels of the and genes was evident in human cancers. Furthermore, we found that RASSF7 interacts with c-Myc via its RA and leucine zipper (LZ) domains and LZ domain peptide is sufficient to inhibit c-Myc function, suggesting that this peptide might be used to target oncogenic c-Myc. These results unveil that RASSF7 and c-Myc are functionally linked in the control of tumorigenesis and open up potential therapeutic avenues for targeting the "undruggable" c-Myc protein in a subset of human cancers.

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E4BP4/NFIL3 modulates the epigenetically repressed RAS effector RASSF8 function through histone methyltransferases

Cited by 10 publications

References 50 publications

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

Genome-Wide Gene Expression Profiles Reveal Distinct Molecular Characteristics of the Goose Granulosa Cells

The non-enzymatic RAS effector RASSF7 inhibits oncogenic c-Myc function

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