mtDNA Haplogroup N9a Increases the Risk of Type 2 Diabetes by Altering Mitochondrial Function and Intracellular Mitochondrial Signals.
Hezhi FangNianqi HuQiongya ZhaoBingqian WangHuaibin ZhouQingzi FuLijun ShenXiong ChenFeixia ShenJianxin LyuPublished in: Diabetes (2018)
Mitochondrial DNA (mtDNA) haplogroups have been associated with the incidence of type 2 diabetes (T2D); however, their underlying role in T2D remains poorly elucidated. Here, we report that mtDNA haplogroup N9a was associated with an increased risk of T2D occurrence in Southern China (odds ratio 1.999 [95% CI 1.229-3.251], P = 0.005). By using transmitochondrial technology, we demonstrated that the activity of respiratory chain complexes was lower in the case of mtDNA haplogroup N9a (N9a1 and N9a10a) than in three non-N9a haplogroups (D4j, G3a2, and Y1) and that this could lead to alterations in mitochondrial function and mitochondrial redox status. Transcriptome analysis revealed that OXPHOS function and metabolic regulation differed markedly between N9a and non-N9a cybrids. Furthermore, in N9a cybrids, insulin-stimulated glucose uptake might be inhibited at least partially through enhanced stimulation of ERK1/2 phosphorylation and subsequent TLR4 activation, which was found to be mediated by the elevated redox status in N9a cybrids. Although it remains unclear whether other signaling pathways (e.g., Wnt pathway) contribute to the T2D susceptibility of haplogroup N9a, our data indicate that in the case of mtDNA haplogroup N9a, T2D is affected, at least partially through ERK1/2 overstimulation and subsequent TLR4 activation.
Keyphrases
- mitochondrial dna
- copy number
- signaling pathway
- cell proliferation
- pi k akt
- toll like receptor
- oxidative stress
- immune response
- inflammatory response
- type diabetes
- stem cells
- genome wide
- risk assessment
- electronic health record
- dna methylation
- risk factors
- gene expression
- epithelial mesenchymal transition
- single cell
- metabolic syndrome
- insulin resistance
- induced apoptosis
- big data
- blood pressure
- artificial intelligence
- protein kinase