Integrated metabolomic analysis and cytokine profiling define clusters of immuno-metabolic correlation in new-onset psoriasis

Abstract

The association between the metabolic profile and inflammatory cytokines in psoriasis is poorly understood. We analyzed the metabolic and cytokine/chemokine profiles in serum and skin from patients with new-onset psoriasis and healthy subjects (n = 7/group) by HR-MAS NMR and Bio-Plex immunoassay. Immuno-metabolic correlation matrix was analyzed in skin and serum to identify a potential immune-metabolic signature. Metabolomics analysis showed a significant increase in ascorbate and a decrease in scyllo-inositol, and a trend towards an increase in eight other metabolites in psoriatic skin. In serum, there was a significant increase of dimethylglycine and isoleucine. In parallel, psoriatic skin exhibited an increase of early inflammatory cytokines (IL-6, IL-8, TNF-α, IL-1β) and correlation analysis highlighted some major clusters of immune-metabolic correlations. A cluster comprising scyllo-inositol and lysine showed correlations with T-cell cytokines; a cluster comprising serine and taurine showed a negative correlation with early inflammatory cytokines (IL-6, G-CSF, CCL3). A strong positive correlation was enlightened between glutathione and inflammatory cytokines/angiogenesis promoters of psoriasis. The integration of metabolic and immune data indicated a molecular signature constituted by IL-6, IL1-ra, DMG, CCL4, Ile, Gly and IL-8, which could discriminate patients and healthy subjects and could represent a candidate tool in the diagnosis of new-onset psoriasis.

Figure 1

Relative quantification of metabolites after deconvolution of NMR signals. The bar plots show the relative amounts (mean ± standard errors) of metabolites identified in (a) skin and (b) in serum samples. (b) Some metabolites are reported in two bar charts with different y-axis expansions. Statistically significant (p < 0.05) changes in metabolite levels of Asc and Scy (a), DMG and Ile (b) detected by t-test are indicated by asterisks. Figure produced by Topspin (Bruker) Version 4.0.7 (https://www.bruker.com/content/bruker/int/en/products-and-solutions/magnetic-resonance/nmr-software.html). No permission required.

Figure 2

Scores plots of principal component analysis (PCA) of skin (a) and serum (b); and sparse partial least square-discriminant analysis (sPLS-DA) of skin (c) and serum (d) from deconvoluted NMR signals. Healthy control samples in red and psoriasis samples in green. sPLS-DA loading profiles of latent variable 1 (LV1) from skin (e) and serum samples (f). Figure produced by Microsoft Excel, Power Point, Microsoft Office Professional Plus 2019 (http://www.office.com). No permission required.

Figure 3

Cytokine and chemokine concentration (pg/ml) in skin (a) and serum (b) samples is represented as Box and Whisker Plot. Significance of the differences between psoriasis patients’ and healthy subjects’ control group was calculated using Student’s t-test for unpaired samples or Mann–Whitney test depending on the normality of data distribution. p < 0.05 was considered significant: *p < 0.05. **p < 0.01. Figure produced by Metaboanalyst Version 4.0 (https://www.metaboanalyst.ca) and Microsoft Excel, Office Professional Plus 2019 (http://www.office.com). No permission required.

Figure 4

Heat maps of significant correlations between metabolites and cytokines/chemokines analyzed in skin biopsies and serum samples from psoriasis patients (a) or healthy control subjects (b). Pearson correlation coefficients (r) were used to express the correlation and r values <  − 0.7 and >  + 0.7 were selected to indicate negative and positive correlations, respectively. Green color gradients indicate negative correlations with values < 0.05 and red color gradient indicates positive correlations with p values < 0.05. r values not reaching significance correspond to uncolored squares. The complete table of r and p values of the correlations in the tissues analyzed is reported in supplemental data as supplementary table. Figure produced by Prism Software, version 8.3.0 Inc. USA (https://www.graphpad.com/). No permission required.

Figure 5

Candidate signature molecules were ranked for their potential patients/control discriminant capacity on the basis of the p-values of the difference between their concentration in patients and control groups (-Log10 p-values). (a). The discriminant power of the candidate signature was evaluated using hierarchical clustering in serum samples (b). Hierarchical clustering was applied also to tissue samples, using the 5 candidate molecules expressed also in tissue (IL-6, CCL4, IL1-ra, Gly and IL-8) (c). Figure produced by Metaboanalyst Version 4.0 (https://www.metaboanalyst.ca) and Microsoft Excel, Office Professional Plus 2019 (http://www.office.com). No permission required.

Figure 6

Candidate immune-metabolic signature of new-onset psoriasis. Figure produced by Adobe Illustrator 2020 (https://www.adobe.com/it/products/illustrator.html). No permission required.