TNFα Effects on Adipocytes Are Influenced by the Presence of Lysine Methyltransferases, G9a (EHMT2) and GLP (EHMT1).
Ashley A AbleAllison J RichardJacqueline M StephensPublished in: Biology (2023)
Impaired adipocyte function contributes to systemic metabolic dysregulation, and altered fat mass or function increases the risk of Type 2 diabetes. EHMTs 1 and 2 (euchromatic histone lysine methyltransferases 1 and 2), also known as the G9a-like protein (GLP) and G9a, respectively, catalyze the mono- and di-methylation of histone 3 lysine 9 (H3K9) and also methylate nonhistone substrates; in addition, they can act as transcriptional coactivators independent of their methyltransferase activity. These enzymes are known to contribute to adipocyte development and function, and in vivo data indicate a role for G9a and GLP in metabolic disease states; however, the mechanisms involved in the cell-autonomous functions of G9a and GLP in adipocytes are largely unknown. Tumor necrosis factor alpha (TNFα) is a proinflammatory cytokine typically induced in adipose tissue in conditions of insulin resistance and Type 2 diabetes. Using an siRNA approach, we have determined that the loss of G9a and GLP enhances TNFα-induced lipolysis and inflammatory gene expression in adipocytes. Furthermore, we show that G9a and GLP are present in a protein complex with nuclear factor kappa B (NF-κB) in TNFα-treated adipocytes. These novel observations provide mechanistic insights into the association between adipocyte G9a and GLP expression and systemic metabolic health.
Keyphrases
- adipose tissue
- insulin resistance
- nuclear factor
- gene expression
- rheumatoid arthritis
- high fat diet
- type diabetes
- dna methylation
- toll like receptor
- poor prognosis
- high fat diet induced
- healthcare
- high glucose
- public health
- cardiovascular disease
- drug induced
- cystic fibrosis
- amino acid
- single cell
- fatty acid
- polycystic ovary syndrome
- signaling pathway
- machine learning
- binding protein
- oxidative stress
- risk assessment
- small molecule
- transcription factor
- cancer therapy
- metabolic syndrome
- immune response
- mental health
- heat stress
- protein protein
- cell therapy
- pi k akt
- social media
- deep learning
- pseudomonas aeruginosa