We are performing omic analyses of psoriasis and psoriatic arthritis (PsA) patients to address the hypothesis that biomarker discovery will inform more individualized care of individuals (endotypes) with psoriasis and their comorbidities. 

We collected whole blood from psoriasis patients (n=59, 16 PsA; mean PASI=8.6 (range 0-33.5) with 9 healthy controls) and PBMCs (n=9 psoriasis, n=5 controls). RNASeq was performed using Illumina TruSeq Total RNA kits and a NextSeq 550 (15M+ paired reads/sample, 75 bp). Significant differentially expressed genes (DEGs; p<0.05) were identified between psoriasis/PsA patients and controls, and pathways were identified by gene set variation analysis. A general psoriasis signature was found vs. controls featuring upregulated interferons that trended toward an increase with age using linear regression. Other psoriatic endotypes were identified using proinflammatory biomarkers that clustered patients by increasing PASI score, gender and ethnicity. 

Network analysis identified interactions between the interferome and metabolic stimuli in psoriatic patients vs. controls. PSA patients uniquely expressed heat shock genes versus patients without joint involvement. Whole blood cytometric analysis revealed increased intermediate monocytes (CD14+ CD16++) in psoriasis vs. controls (p<0.05). Regression analysis between the immunoflow and RNASeq data revealed that psoriasis patients with higher percentages of CD14+ CD16++ monocytes also expressed unique inflammasome genes. Analysis of whole blood versus PBMCs from psoriasis patients revealed unique DEG perhaps reflective of each sample type's cellular composition. 

Our integrated omic and clinical approach is revealing classifiers of patients with the most severe forms of psoriasis and PSA that may be used to improve their standard of care.

Integration of multi-omic data identifies psoriasis endotypes correlating with clinical and immunological phenotypes