Combining mechanism-based prediction with patient-based profiling for psoriasis metabolomics biomarker discovery

Psoriasis is a chronic, debilitating skin condition that affects approximately 125 million individuals worldwide. The cause of psoriasis appears multifactorial, and no unified mitigating signal or single antigenic target has been identified to date. Metabolomic studies hold great potential for explaining disease mechanism, facilitating early diagnosis, and identifying potential therapeutic areas. Here, we present an integrated disease metabolomic biomarker discovery strategy that combines mechanism-based biomarker discovery with clinical sample-based metabolomic profiling. We applied this strategy in identifying and understanding metabolite biomarkers for psoriasis. The key innovation of our strategy is a novel mechanism-based metabolite prediction system, mmPredict, which assimilates vast amounts of existing knowledge of diseases and metabolites. mmPredict first constructed a psoriasis-specific mouse mutational phenotype profile. It then constructed phenotype profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. Metabolites were then prioritized based on the phenotypic similarities between disease- and metabolites. We evaluated mmPredict using 150 metabolites identified using our in-house metabolome profiling study of psoriasis patient samples. mmPredict found 96 of the 150 metabolites and ranked them highly (recall: 0.64, mean ranking: 8.73%, median ranking: 2.33%, p-value: 4.75E-44). These results show that mmPredict is consistent with, as well as a complement to, traditional human metabolomic profiling studies. We then developed a strategy to combine outputs from both systems and found that the oxidative product of linoleic acid, 13(S)-hydroxy-9Z,11E-octadecadienoic acid (13- HODE), ranked highly by both mmPredict and our in-house experiments. Our integrated analysis indicates that 13- HODE may be a mechanistic link between psoriasis and cardiovascular comorbidities associated with psoriasis. In summary, we developed an integrated metabolomic prediction system that combines both human metabolomic studies and mechanism-based prediction and demonstrated its application in the skin disease psoriasis. Our system is highly general and can be applied to other diseases when patient-based metabolomic profiling data becomes more increasingly available. Data is publicly available at: http://nlp.

edu/public/data/mmPredict_PSO.