Transcriptional determinants of individualized inflammatory responses at anatomically separate sites

Multi-omics integration reveals a core network involved in host defence and hyperkeratinization in psoriasis

OBJECTIVES

The precise pathogenesis of psoriasis remains incompletely explored. We aimed to better understand the underlying mechanisms of psoriasis, using a systems biology approach based on transcriptomics and microbiome profiling.

METHODS

We collected the skin tissue biopsies and swabs in both lesional and non-lesional skin of 13 patients with psoriasis, 15 patients with psoriatic arthritis and healthy skin from 12 patients with ankylosing spondylitis. To study the similarities and differences in the molecular profiles between these three conditions, and the associations between the host defence and microbiota composition, we performed high-throughput RNA-sequencing to quantify the gene expression profile in tissues. The metagenomic composition of 16S on local skin sites was quantified by clustering amplicon sequences and counted into operational taxonomic units. We further analysed associations between the transcriptome and microbiome profiling.

RESULTS

We found that lesional and non-lesional samples were remarkably different in terms of their transcriptome profiles. The functional annotation of differentially expressed genes showed a major enrichment in neutrophil activation. By using co-expression gene networks, we identified a gene module that was associated with local psoriasis severity at the site of biopsy. From this module, we found a 'core' set of genes that was functionally involved in neutrophil activation, epidermal cell differentiation and response to bacteria. Skin microbiome analysis revealed that the abundances of Enhydrobacter, Micrococcus and Leptotrichia were significantly correlated with the genes in core network.

CONCLUSIONS

We identified a core gene network that associated with local disease severity and microbiome composition, involved in the inflammation and hyperkeratinization in psoriatic skin.

Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach

Psoriasis is a chronic inflammatory skin disorder. Although it has been studied extensively, the molecular mechanisms driving the disease remain unclear. In this study, we utilized a tree-based machine learning approach to explore the gene regulatory networks underlying psoriasis. We then validated the regulators and their networks in an independent cohort. We identified some key regulators of psoriasis, which are candidates to serve as potential drug targets and disease severity biomarkers. According to the gene regulatory network that we identified, we suggest that interferon signaling represents a key pathway of psoriatic inflammation.

Cytokine responses in nonlesional psoriatic skin as clinical predictor to anti-TNF agents

BACKGROUND

A major issue with the current management of psoriasis is our inability to predict treatment response.

OBJECTIVE

Our aim was to evaluate the ability to use baseline molecular expression profiling to assess treatment outcome for patients with psoriasis.

METHODS

We conducted a longitudinal study of 46 patients with chronic plaque psoriasis treated with anti-TNF agent etanercept, and molecular profiles were assessed in more than 200 RNA-seq samples.

RESULTS

We demonstrated correlation between clinical response and molecular changes during the course of the treatment, particularly for genes responding to IL-17A/TNF in keratinocytes. Intriguingly, baseline gene expressions in nonlesional, but not lesional, skin were the best marker of treatment response at week 12. We identified USP18, a known regulator of IFN responses, as positively correlated with Psoriasis Area and Severity Index (PASI) improvement (P = 9.8 × 10^-4) and demonstrate its role in regulating IFN/TNF responses in keratinocytes. Consistently, cytokine gene signatures enriched in baseline nonlesional skin expression profiles had strong correlations with PASI improvement. Using this information, we developed a statistical model for predicting PASI75 (ie, 75% of PASI improvement) at week 12, achieving area under the receiver-operating characteristic curve value of 0.75 and up to 80% accurate PASI75 prediction among the top predicted responders.

CONCLUSIONS

Our results illustrate feasibility of assessing drug response in psoriasis using nonlesional skin and implicate involvement of IFN regulators in anti-TNF responses.

Persistence of Inflammatory Phenotype in Residual Psoriatic Plaques in Patients on Effective Biologic Therapy

Many psoriasis patients treated with biologics do not achieve total skin clearance. These patients possess residual plaques despite ongoing biologic treatment. To elucidate mechanisms of plaque persistence despite overall good drug response, we studied 50 subjects: psoriasis patients with residual plaques treated with one of three different biologics, untreated patients, and healthy controls. Skin biopsies from all subjects were characterized using three methods: mRNA expression, histology, and FACS of hematopoietic skin cells. Although all three methods provided evidence of drug effect, gene expression analysis revealed the persistence of key psoriasis pathways in treated plaques, including granulocyte adhesion and diapedesis, T helper type17 activation pathway, and interferon signaling with no novel pathways emerging. Focal decreases in parakeratosis and keratinocyte proliferation and differential reduction in IL-17 producing CD103- T cells, but no change in CD103+ tissue-resident memory T cells were observed. Of note, antitumor necrosis factor increased the interferon signaling pathway already present. Interestingly mast cells were the dominant source of IL-22 in all psoriasis subjects. These data suggest that while subtle differences can be observed in drug-treated plaques, underlying biologic mechanisms are similar to those present in untreated psoriatic lesions.