Integrated transcriptomic and metabolomic analysis identifies a core gene-metabolite network linking lysine...

Psoriasis is a complex chronic inflammatory disease with cutaneous manifestations, driven by intricate interactions between genetic, immunological, and metabolic dysregulations. However, the integrated molecular networks connecting transcriptional alterations to metabolic reprogramming remain incompletely elucidated.

We performed a comprehensive integrated analysis of transcriptomics and targeted metabolomics using serum samples from 11 psoriasis vulgaris patients and 11 healthy controls to dissect the core gene-metabolite regulatory axes underlying psoriasis.

RNA sequencing identified 5,186 differentially expressed genes (DEGs), with a striking predominance of downregulation (5,147 DEGs), and targeted metabolomic profiling detected 208 significantly altered metabolites, including upregulated propanoic acid and downregulated L-allysine. KEGG pathway enrichment analysis revealed six dysregulated metabolic pathways shared by DEGs and altered metabolites, notably lysine degradation, propanoate metabolism, and central carbon metabolism in cancer-with the latter identified as a novel pathogenic pathway in psoriasis vulgaris. Weighted Gene Co-expression Network Analysis (WGCNA) further delineated strong correlations between core genes (TP53, MTOR, AKT3) and key metabolites (propanoic acid, L-allysine), forming a functional network that links cellular energy metabolism to immune-inflammatory responses. Protein-protein interaction network analysis confirmed these core genes as hub regulators within the dysregulated pathways.

In this exploratory pilot study, integrated transcriptomic and metabolomic profiling revealed preliminary associations between lysine degradation, propanoate metabolism, and psoriasis status. The observed crosstalk with central carbon metabolism, potentially involving TP53, MTOR, and AKT3, generates hypotheses that require validation in independent, larger cohorts before any clinical or mechanistic inferences can be drawn.