A recent study published in eBioMedicine performed a multi-omics analysis of host-microbe interactions in psoriasis.
Background
Psoriasis is a common systemic inflammatory disease that affects up to 3% of the global population. It can cause comorbidities such as diabetes, psoriatic arthritis, and cardiovascular disease.
Based on disease characteristics, there are several clinical subtypes of psoriasis. Various factors, such as the epidermal barrier, environmental factors, and the immune system, have been implicated in the development and progression of psoriasis.
Psoriasis lacks a definitive cure and remains a significant psychological and economic burden. Psoriatic skin microbiome varies in composition and diversity compared to healthy skin.
Host-microbe interactions have been suggested to be involved in psoriasis development. Further, skin microbiome dysbiosis has been reported in psoriasis; however, research on interactions between the microbiota and host using multilayer omics data is lacking.
About the study
In the present study, researchers conducted a multi-omics analysis of host-microbe interactions in psoriasis. They used data from the microbes in allergy and autoimmunity related to the skin (MAARS) cohort.
Individuals with plaque-type psoriasis and healthy volunteers were recruited. People with autoimmune diseases, recent antibiotic use, phototherapy, biologics use, or immunosuppressive therapy were excluded.
Skin biopsies and microbiome samples were obtained from active disease sites and adjacent non-lesional areas on the lower back from psoriasis patients. Samples from corresponding regions were obtained from healthy individuals.
All subjects underwent a physical examination, and their medical histories were obtained. DNA was extracted from microbiome samples for shotgun metagenomic sequencing, and RNA was isolated from biopsy samples for transcriptional analysis.
Weighted gene correlation network analysis (WGCNA) was performed using gene expression data. Whole metagenomic shotgun sequencing was performed to identify functional and taxonomic features of the microbiome.
Mann-Whitney and Kruskal-Wallis tests were used for group comparisons. Spearman correlations were estimated and adjusted for age and sex. Multivariable associations between phenotypes and microbiota were analyzed.
Findings
In total, 116 psoriasis patients and 102 healthy individuals were included. The skin transcriptome of psoriatic lesions was highly distinct from non-lesional psoriatic samples. WGCNA identified six modules annotated with gene ontology (GO) terms.
One module was positively associated with psoriasis area and severity index (PASI) score and was enriched in inflammation-related pathways.
Spearman correlations between the PASI score and host genes were separately estimated for lesional and non-lesional groups.
This revealed functions related to antiviral response in both groups. Interferon (IFN)-associated networks were identified in protein-protein interaction (PPI) networks in both groups.
Further, a leucocyte deconvolution algorithm was used to detect psoriasis-related cellular changes. The algorithm revealed significant differences in the cell fractions of lesional skin compared to those of healthy and non-lesional psoriatic skin.
It predicted an increase in monocytes, endothelial cells, cluster of differentiation 4 (CD4) T cells, keratinocytes, and plasmacytoid dendritic cells in the lesional skin and a decrease in fibroblasts, subcutaneous adipocytes, and adipose stem cells.
Further, 13 microbial species were enriched across mild, moderate, and severe PASI categories. Of these, 11 species were significantly associated with the psoriatic lesion.
The abundance of Corynebacterium simulans was elevated in the PASI moderate and severe categories in both lesional and non-lesional groups.
However, Cutibacterium acnes was less abundant in psoriatic skin than in healthy skin. There was a positive correlation between C. simulans and the abundance of CD4 T cells, dendritic cells, and keratinocytes. In addition, the team found significant interactions between the skin microbiome and cutaneous gene expression in psoriasis.
Specifically, the abundance of C. acnes and C. simulans showed significant correlations with the expression of various host genes, especially IFN-inducible genes, such as IFI27 and IFIH1.
Besides, the functional features of the microbiome were significantly different between psoriatic lesions and non-lesions and healthy skin. Hierarchical clustering of microbial gene families revealed two distinct clusters within the psoriatic lesional group.
Micrococcus luteus was less abundant in psoriatic lesions than in healthy or non-lesional psoriatic skin and cluster 1 relative to cluster 2.
Cluster 1 had lower expression of microbial metabolic pathways, except for aerobic respiration I, whereas the expression of host genes, such as interleukin (IL)-19 and IL-36A, was upregulated. Cluster 1 was enriched for pathways related to lipopolysaccharide response and cellular response to biotic stimuli.
Conclusions
The study investigated the relationship between host genes and microbial features in psoriasis. The findings indicate associations of antiviral responses and C. simulans with psoriatic severity.
Two psoriatic clusters with distinct host and microbial features were identified. Further, the results suggest that concurrent microbiota modulation and immunomodulatory therapies might benefit psoriasis patients.