Chronic activation of pDCs in autoimmunity is linked to dysregulated ER stress and metabolic responses.
Vidyanath ChaudharyMarie Dominique Ah KioonSung-Min HwangBikash MishraKimberly S LakinKyriakos A KirouJeffrey Zhang-SunR Luke WisemanRobert F SpieraMary K CrowJessica K GordonJuan R Cubillos-RuizFranck J BarratPublished in: The Journal of experimental medicine (2022)
Plasmacytoid dendritic cells (pDCs) chronically produce type I interferon (IFN-I) in autoimmune diseases, including systemic sclerosis (SSc) and systemic lupus erythematosus (SLE). We report that the IRE1α-XBP1 branch of the unfolded protein response (UPR) inhibits IFN-α production by TLR7- or TLR9-activated pDCs. In SSc patients, UPR gene expression was reduced in pDCs, which inversely correlated with IFN-I-stimulated gene expression. CXCL4, a chemokine highly secreted in SSc patients, downregulated IRE1α-XBP1-controlled genes and promoted IFN-α production by pDCs. Mechanistically, IRE1α-XBP1 activation rewired glycolysis to serine biosynthesis by inducing phosphoglycerate dehydrogenase (PHGDH) expression. This process reduced pyruvate access to the tricarboxylic acid (TCA) cycle and blunted mitochondrial ATP generation, which are essential for pDC IFN-I responses. Notably, PHGDH expression was reduced in pDCs from patients with SSc and SLE, and pharmacological blockade of TCA cycle reactions inhibited IFN-I responses in pDCs from these patients. Hence, modulating the IRE1α-XBP1-PHGDH axis may represent a hitherto unexplored strategy for alleviating chronic pDC activation in autoimmune disorders.
Keyphrases
- dendritic cells
- systemic lupus erythematosus
- immune response
- gene expression
- end stage renal disease
- systemic sclerosis
- ejection fraction
- newly diagnosed
- endoplasmic reticulum stress
- chronic kidney disease
- dna methylation
- poor prognosis
- prognostic factors
- inflammatory response
- signaling pathway
- toll like receptor
- multiple sclerosis
- interstitial lung disease
- oxidative stress
- binding protein
- transcription factor
- bioinformatics analysis