Functional genetics reveals the contribution of delta opioid receptor to type 2 diabetes and beta-cell function.
Sarah MeulebrouckJudith MerrheimGurvan QueniatCyril BourouhMehdi DerhourhiMathilde BoisselXiaoyan YiAlaa BadreddineRaphaël BoutryAudrey LeloireBénédicte ToussaintSouhila AmanzougareneEmmanuel VaillantEmmanuelle DurandHélène LoiselleMarlène HuyvaertAurélie DechaumeVictoria ScherrerPiero MarchettiBeverley BalkauGuillaume CharpentierSylvia FrancMichel MarreRonan RousselRaphael ScharfmannGabriel SantosMickaël CanouilMorgane BaronPhillippe FroguelAmélie BonnefondPublished in: Nature communications (2024)
Functional genetics has identified drug targets for metabolic disorders. Opioid use impacts metabolic homeostasis, although mechanisms remain elusive. Here, we explore the OPRD1 gene (encoding delta opioid receptor, DOP) to understand its impact on type 2 diabetes. Large-scale sequencing of OPRD1 and in vitro analysis reveal that loss-of-function variants are associated with higher adiposity and lower hyperglycemia risk, whereas gain-of-function variants are associated with lower adiposity and higher type 2 diabetes risk. These findings align with studies of opium addicts. OPRD1 is expressed in human islets and beta cells, with decreased expression under type 2 diabetes conditions. DOP inhibition by an antagonist enhances insulin secretion from human beta cells and islets. RNA-sequencing identifies pathways regulated by DOP antagonism, including nerve growth factor, circadian clock, and nuclear receptor pathways. Our study highlights DOP as a key player between opioids and metabolic homeostasis, suggesting its potential as a therapeutic target for type 2 diabetes.
Keyphrases
- type diabetes
- insulin resistance
- growth factor
- glycemic control
- chronic pain
- induced apoptosis
- endothelial cells
- genome wide
- cardiovascular disease
- pain management
- copy number
- single cell
- cell cycle arrest
- poor prognosis
- binding protein
- adipose tissue
- emergency department
- signaling pathway
- oxidative stress
- cell death
- long non coding rna
- pluripotent stem cells
- weight loss
- electronic health record
- data analysis