CCDC76 Blocking Peptide
- Known as:
- CCDC76 Blocking Peptide
- Catalog number:
- 33r-7618
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Fitzgerald industries international
- Gene target:
- CCDC76 Blocking Peptide
Related genes to: CCDC76 Blocking Peptide
- Gene:
- TRMT13 NIH gene
- Name:
- tRNA methyltransferase 13 homolog
- Previous symbol:
- CCDC76
- Synonyms:
- FLJ10287, FLJ11219
- Chromosome:
- 1p21.2
- Locus Type:
- gene with protein product
- Date approved:
- 2006-02-15
- Date modifiied:
- 2016-03-31
Related products to: CCDC76 Blocking Peptide
Related articles to: CCDC76 Blocking Peptide
- Viral infections are a major cause of cardiac metabolic dysfunction, leading to diseases like viral myocarditis and heart failure. In this study, we used G3-YSD, a synthetic DNA mimic from the HIV-1 genome, to model cytoplasmic viral DNA stress and aim to elucidate the mechanistic roles of TRMT13 and SPAST in cardiomyocyte metabolic impairment under conditions mimicking viral infection. Through functional assays such as ATP quantification and Seahorse XF metabolic stress tests on H9C2 and AC16 cells, we found that G3-YSD treatment suppressed cardiomyocyte energy metabolism in a dose- and time-dependent manner, with upregulation of TRMT13 and SPAST. Further analysis revealed a strong positive association between these two genes. Functional knockdown experiments showed that silencing TRMT13 or SPAST alleviated G3-YSD-induced energy metabolic impairment. Mechanistically, miR-409-3p was identified as a key intermediary directly targeting the 3'UTRs of TRMT13 and SPAST, with TRMT13 mRNA acting as a ceRNA to promote SPAST expression. In vivo validation using a G3-YSD-treated mouse model confirmed that knocking down TRMT13 or SPAST, or delivering miR-409-3p mimics, improved cardiac function and systemic metabolic activity, while SPAST overexpression negated these effects. Our study identifies a novel TRMT13-miR-409-3p-SPAST regulatory axis mediating cardiomyocyte energy metabolic decline in response to cytoplasmic viral DNA stress, revealing a nonmethyltransferase, nonimmune, ceRNA-based mechanism of a newly characterised tRNA methyltransferase in viral infection-induced cardiac metabolic dysfunction and proposing new molecular targets for therapeutic intervention. - Source: PubMed
Liu LiangyongWu HongWang YanHuang ZeyuLi XiangqiZhu Qingyun - To investigate gene mutation characteristics of primary central nervous system lymphoma (PCNSL) through whole exome sequencing (WES) to 18 patients with PCNSL. - Source: PubMed
Jin Qi-QiJiang Hao-YunHan YeLi Cui-CuiZhang Li-TianWu Chong-Yang - The recently discovered gene encodes a type of RNA methylase and is a member of the family (also called CCDC76). Here, we delineate its role in papillary thyroid cancer (PTC). Bioinformatics analysis shows significant TRMT13 and ANAPC4 downregulation in PTC and reveals that the expression levels of both genes are linearly correlated. Subsequent analyses confirm that both TRMT13 and ANAPC4 expressions are downregulated in PTC tissues and that this change in expression has a significant impact on cancer diagnosis. We conduct assays on PTC cells subjected to and silencing or overexpression to assess the biological effects of these genes. We also perform rescue experiments to validate the regulatory effects of TRMT13 on ANAPC4. A nude mouse tumor model is used to evaluate the effects of TRMT13 and ANAPC4 on PTC tumorigenesis. TRMT13 expression is decreased in PTC tissues and cell lines and is positively correlated with that of ANAPC4. Cell assays reveal that TRMT13/ANAPC4 attenuates the malignancy of PTC cells by restraining cell proliferation, migration and invasion, while rescue experiments corroborate that is a downstream target of TRMT13. In the nude mouse xenograft model, both TRMT13 and ANAPC4 inhibit tumor growth, and TRMT13 and ANAPC4 expression levels are significantly associated with survival. Taken together, these findings lead to the conclusion that TRMT13 inhibits PTC growth via ANAPC4, indicating a new role of TRMT13 and providing insights into the tRNA methyltransferase and coiled-coil domain-containing protein families. - Source: PubMed
Liu LianyongWang YanZou MeiChen ShiweiWu FengyingLi Xiangqi - In this study, an attempt is made to cluster the gene expression data and neuroimaging markers using an interpretable neural network model to identify Mild Cognitive Impairment (MCI) subtypes. For this, structural Magnetic Resonance (MR) brain images and gene expression data of early and late MCI subjects are considered from a public database. A neural network model is employed to cluster the gene expression data and regional MR volumes. To evaluate the performance of model, clustering metrics are employed and model is explained using perturbation-based method. Results indicate that the developed model is able to identify MCI subtypes. The network learns latent embeddings of disease-specific genes and MR images markers. The clustering metrics are found to be highest when both the imaging and genetic markers are employed. Volumes of lateral ventricles, hippocampus, amygdala and thalamus are found to be associated with late MCI. Significant scores suggest that genes such as StAR, CCDC108, APOO, TRMT13, RASAL2 and ZNF43 play a key role in identifying the MCI subtypes.Clinical Relevance-Identifying distinct MCI subtypes offer potential for precision diagnostics and targeted clinical recruitment. - Source: PubMed
S R ManuskandanS Sreelakshmi