Transcriptome classification reveals molecular subtypes in psoriasis

Exploratory multi-omics analysis reveals host-microbe interactions associated with disease severity in psoriatic skin

BACKGROUND

Psoriasis (Pso) is a chronic inflammatory skin disease that poses both physical and psychological challenges. Dysbiosis of the skin microbiome has been implicated in Pso, yet a comprehensive multi-omics analysis of host-microbe interactions is still lacking. To bridge this gap, we conducted an exploratory study by adopting the integrated approach that combines whole metagenomic shotgun sequencing with skin transcriptomics.

METHODS

This was a cross-sectional study, adult patients with plaque-type Psoriasis (Pso) and healthy volunteers were included. Skin microbiota samples and biopsies were collected from both lesional and non-lesional skin areas on the lower back. Weighted Gene Correlation Network Analysis (WGCNA) was employed for co-expression network analysis, and cell deconvolution was conducted to estimate cell fractions. Taxonomic and functional features of the microbiome were identified using whole metagenomic shotgun sequencing. Association between host genes and microbes was analyzed using Spearman correlation.

FINDINGS

Host anti-viral responses and interferon-related networks were identified and correlated with the severity of psoriasis. The skin microbiome showed a greater prevalence of Corynebacterium simulans in the PASI severe-moderate groups, which correlated with interferon-induced host genes. Two distinct psoriatic clusters with varying disease severities were identified. Variations in the expression of cell apoptosis-associated antimicrobial peptides (AMPs) and microbial aerobic respiration I pathway may partly account for these differences in disease severity.

INTERPRETATION

Our multi-omics analysis revealed for the first time anti-viral responses and the presence of C. simulans associated with psoriasis severity. It also identified two psoriatic subtypes with distinct AMP and metabolic pathway expression. Our study provides new insights into understanding the host-microbe interaction in psoriasis and lays the groundwork for developing subtype-specific strategies for managing this chronic skin disease.

FUNDING

The research has received funding from the FP7 (MAARS-Grant 261366) and the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 821511 (BIOMAP). The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This publication reflects only the author's view and the JU is not responsible for any use that may be made of the information it contains. GAM was supported by a scholarship provided by CAPES-PRINT, financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (Brazilian Government Agency). The authors thank all patients who participated in our study.

Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis

Background

Psoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible.

Methods

We performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes.

Results

Based on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A-C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors.

Conclusions

Psoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.

Signaling pathways and targeted therapies for psoriasis

Psoriasis is a common, chronic, and inflammatory skin disease with a high burden on individuals, health systems, and society worldwide. With the immunological pathologies and pathogenesis of psoriasis becoming gradually revealed, the therapeutic approaches for this disease have gained revolutionary progress. Nevertheless, the mechanisms of less common forms of psoriasis remain elusive. Furthermore, severe adverse effects and the recurrence of disease upon treatment cessation should be noted and addressed during the treatment, which, however, has been rarely explored with the integration of preliminary findings. Therefore, it is crucial to have a comprehensive understanding of the mechanisms behind psoriasis pathogenesis, which might offer new insights for research and lead to more substantive progress in therapeutic approaches and expand clinical options for psoriasis treatment. In this review, we looked to briefly introduce the epidemiology, clinical subtypes, pathophysiology, and comorbidities of psoriasis and systematically discuss the signaling pathways involving extracellular cytokines and intracellular transmission, as well as the cross-talk between them. In the discussion, we also paid more attention to the potential metabolic and epigenetic mechanisms of psoriasis and the molecular mechanistic cascades related to its comorbidities. This review also outlined current treatment for psoriasis, especially targeted therapies and novel therapeutic strategies, as well as the potential mechanism of disease recurrence.

Assessment of Treatment-Relevant Immune Biomarkers in Psoriasis and Atopic Dermatitis: Toward Personalized Medicine in Dermatology

Immunologically targeted therapies have revolutionized the treatment of inflammatory dermatoses, including atopic dermatitis and psoriasis. Although immunologic biomarkers hold great promise for personalized classification of skin disease and tailored therapy selection, there are no approved or widely used approaches for this in dermatology. This review summarizes the translational immunologic approaches to measuring treatment-relevant biomarkers in inflammatory skin conditions. Tape strip profiling, microneedle-based biomarker patches, molecular profiling from epidermal curettage, RNA in situ hybridization tissue staining, and single-cell RNA sequencing have been described. We discuss the advantages and limitations of each and open questions for the future of personalized medicine in inflammatory skin disease.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach

Transcriptome profiling techniques, such as microarrays and RNA sequencing (RNA-seq), are valuable tools for deciphering the regulatory network underlying psoriasis and have revealed large number of differentially expressed genes in lesional and non-lesional skin. Such approaches provide a more precise measurement of transcript levels and their isoforms than any other methods. Large cohort transcriptomic analyses have greatly improved our understanding of the physiological and molecular mechanisms underlying disease pathogenesis and progression. Here, we mostly review the findings of some important large scale psoriatic transcriptomic studies, and the benefits of such studies in elucidating potential therapeutic targets and biomarkers for psoriasis treatment. We also emphasised the importance of looking into the alternatively spliced RNA isoforms/transcripts in psoriasis, rather than focussing only on the gene-level annotation. The neutrophil and blood transcriptome signature in psoriasis is also briefly reviewed, as it provides the immune status information of patients and is a less invasive platform. The application of precision medicine in current management of psoriasis, by combining transcriptomic data, improves the clinical response outcome in individual patients. Drugs tailored to individual patient's genetic profile will greatly improve patient outcome and cost savings for the healthcare system.

Predominant Role of mTOR Signaling in Skin Diseases with Therapeutic Potential

The serine/threonine kinase mechanistic target of rapamycin (mTOR) plays a pivotal role in the regulation of cell proliferation, survival, and motility in response to availability of energy and nutrients as well as mitogens. The mTOR signaling axis regulates important biological processes, including cellular growth, metabolism, and survival in many tissues. In the skin, dysregulation of PI3K/AKT/mTOR pathway may lead to severe pathological conditions characterized by uncontrolled proliferation and inflammation, including skin hyperproliferative as well as malignant diseases. Herein, we provide an update on the current knowledge regarding the pathogenic implication of the mTOR pathway in skin diseases with inflammatory features (such as psoriasis, atopic dermatitis, pemphigus, and acne) and malignant characteristics (such as cutaneous T cell lymphoma and melanoma) while we critically discuss current and future perspectives for therapeutic targeting of mTOR axis in clinical practice.

Small plaque psoriasis re-visited: A type of psoriasis mediated by a type-I interferon pathway

TNFα-inhibitor-induced psoriasis is mediated by the type-I interferon pathway, of which IFNα, LL37 and IL-36γ are major players. A subset of patients treated with TNFα inhibitors develop small plaque psoriatic lesions. Small plaque psoriasis is similarly observed in patients on immune checkpoint inhibitors (ICI), and with concurrent systemic lupus erythematosus (SLE) or positive antinuclear antibody (ANA). Small plaque psoriasis is also the predominant phenotype in Asian populations. The association between small plaque psoriasis morphology in various clinical scenarios and the type-I interferon pathway has not been previously studied. A cross-sectional study was conducted of patients who developed small plaque psoriasis and had a biopsy for diagnostic clarification between 2009 and 2017. We obtained skin specimens from 14 adults with small plaque psoriasis: four patients taking anti-TNFα treatment, four patients with antecedent SLE, three patients with concurrent ANA positivity and three patients taking ICI. Controls included three patients with chronic plaque psoriasis. Histology confirmed psoriasiform epidermal hyperplasia with focal lichenoid and spongiotic features. Immunohistochemical analysis revealed higher expression of IFNα-induced MXA, LL37 and IL-36γ in all clinical scenarios of small plaque psoriasis compared to chronic plaque psoriasis. There was decreased CD8 T-cell migration to the epidermis and variability in the number of LAMP3+ cytoplasmic dendritic cells in the dermis of small plaque psoriasis. The findings suggest that small plaque psoriasis is a unique type of psoriasis with a distinct morphology and immune-phenotype, primarily mediated by the type-I interferon pathway. Associating morphology and disease pathogenesis may help identify therapeutic targets for better disease control.

FUT8 Remodeling of EGFR Regulates Epidermal Keratinocyte Proliferation during Psoriasis Development

α-(1,6)-fucosyltransferase 8 (FUT8) is implicated in the pathogenesis of several malignancies, but its role in psoriasis is poorly understood. In this study, we show that FUT8 remodeling of EGFR plays a critical role in the development of psoriasis phenotypes. Notably, elevated FUT8 expression was associated with disease severity in the lesional epidermis of a patient with psoriasis. FUT8 gain of function promoted HaCaT cell proliferation, whereas short hairpin FUT8 reduced cell proliferation and induced a longer S phase with downregulation of cyclin A1 expression. Furthermore, cell proliferation, which is controlled by the activation of EGFR, was shown to be regulated by FUT8 core fucosylation of EGFR. Short hairpin FUT8 significantly reduced EGFR/protein kinase B signaling and slowed EGF‒EGFR complex trafficking to the perinuclear region. Moreover, short hairpin FUT8 reduced ligand-induced EGFR dimerization. Overactivated EGFR was observed in the lesional epidermis of both human patient and psoriasis-like mouse model, whereas conditional knockout of FUT8 in an IL-23 psoriasis-like mouse model ameliorated disease phenotypes and reduced EGFR activation in the epidermis. These findings implied that elevated FUT8 expression in the lesional epidermis is implicated in the development of psoriasis phenotypes, being required for EGFR overactivation and leading to keratinocyte hyperproliferation.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

BACKGROUND

Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive.

OBJECTIVE

To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis.

METHOD

The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE.

RESULTS

A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene.

CONCLUSION

The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Gini Coefficients as a Single Value Metric to Define Chemical Probe Selectivity

Selectivity is a key requirement of high-quality chemical probes and lead medicines; however, methods to quantify and compare the selectivity of small molecules have not been standardized across the field. Herein, we discuss the origins and use of a comprehensive, single value term to quantify selectivity, the Gini coefficient. Case studies presented include compounds that target protein kinases, small molecules that bind RNA structures, and small molecule chimeras that bind to and degrade the target RNA. With an increasing number of transcriptome- and proteome-wide studies, we submit that reporting Gini coefficients as a quantitative descriptor of selectivity should be used broadly.

Keratinocytes Derived from Patient-Specific Induced Pluripotent Stem Cells Recapitulate the Genetic Signature of Psoriasis Disease

Psoriasis is characterized by hyperproliferation and defective differentiation of keratinocytes (KCs). Patients with psoriasis are at a high risk of developing diabetes and cardiovascular diseases. The debate on the genetic origin of psoriasis pathogenesis remains unresolved due to lack of suitable in vitro human models mimicking the disease phenotypes. In this study, we provide the first human induced pluripotent stem cell (iPSC) model for psoriasis carrying the genetic signature of the patients. iPSCs were generated from patients with psoriasis (PsO-iPSCs) and healthy donors (Ctr-iPSCs) and were efficiently differentiated into mature KCs. RNA sequencing of KCs derived from Ctr-iPSCs and PsO-iPSCs identified 361 commonly upregulated and 412 commonly downregulated genes. KCs derived from PsO-iPSCs showed dysregulated transcripts associated with psoriasis and KC differentiation, such as HLA-C, KLF4, chemokines, type I interferon-inducible genes, solute carrier family, IVL, DSG1, and HLA-DQA1, as well as transcripts associated with insulin resistance, such as IRS2, GDF15, GLUT10, and GLUT14. Our data suggest that the KC abnormalities are the main driver triggering psoriasis pathology and highlights the substantial contribution of genetic predisposition in the development of psoriasis and insulin resistance.

Review-Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis

During the last decades, high-throughput assessment of gene expression in patient tissues using microarray technology or RNA-Seq took center stage in clinical research. Insights into the diversity and frequency of transcripts in healthy and diseased conditions provide valuable information on the cellular status in the respective tissues. Growing with the technique, the bioinformatic analysis toolkit reveals biologically relevant pathways which assist in understanding basic pathophysiological mechanisms. Conventional classification systems of inflammatory skin diseases rely on descriptive assessments by pathologists. In contrast to this, molecular profiling may uncover previously unknown disease classifying features. Thereby, treatments and prognostics of patients may be improved. Furthermore, disease models in basic research in comparison to the human disease can be directly validated. The aim of this article is not only to provide the reader with information on the opportunities of these techniques, but to outline potential pitfalls and technical limitations as well. Major published findings are briefly discussed to provide a broad overview on the current findings in transcriptomics in inflammatory skin diseases.

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

Relationship between the efficacy of biologics and clinical plaque psoriasis subtypes in Japanese patients: A single-center pilot study

Although biologics for plaque psoriasis brought epoch-making efficacy, not all patients achieve treatment success with all reagents. The aim of this study was to clarify the correlation between clinical plaque psoriasis subtypes, age at onset, and the efficacy of biologics. Clinical records for patients with plaque psoriasis at Fukuoka University Hospital were reviewed retrospectively. The efficacy of biologics was compared using the survival of the first biologics administered in treatment-naïve patients. The survival of infliximab, adalimumab, and ustekinumab were followed until December 2016. The patients were clinically classified into three subtypes: small, large, or gigantic plaques using the size of the plaques on the back; early onset psoriasis (EOP, onset <40 years); or late-onset psoriasis (LOP, ≥40 years). Eighty-seven patients were enrolled. The survival of biologics was significantly better in large plaques compared with small or gigantic plaques (P = 0.0007). In patients treated with tumor necrosis factor (TNF) inhibitors, large plaques had significantly better survival than did the other types (P = 0.0122), while ustekinumab showed good survival in all three subtypes. The survival of biologics was numerically better in EOP than in LOP, but this was not significant. The efficacy of TNF inhibitors was different among clinical subtypes. Psoriatic patients with small plaques may be less responsive to TNF inhibitors. Further studies are needed.

Identification of the molecular subgroups in coronary artery disease by gene expression profiles

Coronary artery disease (CAD) is the most common type of cardiovascular disease and becomes a leading cause of death worldwide. Aiming to uncover the underlying molecular features for different types of CAD, we classified 352 CAD cases into three subgroups based on gene expression profiles, which were retrieved from the Gene Expression Omnibus database. Also, these subgroups present different expression patterns and clinical characteristics. To uncover the transcriptomic differences between the subgroups, weighted gene co-expression analysis (WGCNA) was used and identified six subgroup-specific WGCNA modules. Characterization of the WCGNA modules revealed that lipid metabolism pathways, specifically upregulated in subgroup I, might be an indicator of increased severity. Moreover, subgroup II was considered as an early-stage of CAD because of normal-like gene expression patterns. In contrast, the mammalian target of rapamycin signaling pathway was significantly upregulated in subgroup III. Although subgroups II and III did not have a significant prognostic difference, their intrinsic biological characteristics were highly different, suggesting that the transcriptome classification may represent risk factors of both age and the intrinsic biological characteristics. In conclusion, the transcriptome classification of CAD cases revealed that cases from different subgroups may have their unique gene expression patterns, indicating that patients in each subgroup should receive more personalized treatment.

Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis

Psoriasis is an immune‑mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non‑lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein‑protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection‑associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon‑α‑inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re‑analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data

Motivation

Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC).

Results

Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling.

Availability and implementation

FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN).

Contact

michael.l.whitfield@dartmouth.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis

Psoriasis is an immune-mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non-lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein-protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection-associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon-α-inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re-analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.

Psoriasis Pathogenesis and Treatment

Research on psoriasis pathogenesis has largely increased knowledge on skin biology in general. In the past 15 years, breakthroughs in the understanding of the pathogenesis of psoriasis have been translated into targeted and highly effective therapies providing fundamental insights into the pathogenesis of chronic inflammatory diseases with a dominant IL-23/Th17 axis. This review discusses the mechanisms involved in the initiation and development of the disease, as well as the therapeutic options that have arisen from the dissection of the inflammatory psoriatic pathways. Our discussion begins by addressing the inflammatory pathways and key cell types initiating and perpetuating psoriatic inflammation. Next, we describe the role of genetics, associated epigenetic mechanisms, and the interaction of the skin flora in the pathophysiology of psoriasis. Finally, we include a comprehensive review of well-established widely available therapies and novel targeted drugs.

Multi-omics integration reveals molecular networks and regulators of psoriasis

BACKGROUND

Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.

METHODS

To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.

RESULTS

This integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.

CONCLUSIONS

The integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.

DNA methylation-based subclassification of psoriasis in the Chinese Han population

Psoriasis (Ps) is an inflammatory skin disease caused by genetic and environmental factors. Previous studies on DNA methylation (DNAm) found genetic markers that are closely associated with Ps, and evidence has shown that DNAm mediates genetic risk in Ps. In this study, Consensus Clustering was used to analyze DNAm data, and 114 Ps patients were divided into three subclassifications. Investigation of the clinical characteristics and copy number variations (CNVs) of DEFB4, IL22, and LCE3C in the three subclassifications revealed no significant differences in gender ratio and in Ps area and severity index (PASI) score. The proportion of late-onset ( ≥ 40 years) Ps patients was significantly higher in type I than in types II and III (P = 0.035). Type III contained the smallest proportion of smokers and the largest proportion of non-smoking Ps patients (P = 0.086). The CNVs of DEFB4 and LCE3C showed no significant differences but the CNV of IL22 significantly differed among the three subclassifications (P = 0.044). This study is the first to profile Ps subclassifications based on DNAm data in the Chinese Han population. These results are useful in the treatment and management of Ps from the molecular and genetic perspectives.

Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

Psoriatic arthritis (PsA) is a complex chronic musculoskeletal condition that occurs in ~30% of psoriasis patients. Currently, no systematic strategy is available that utilizes the differences in genetic architecture between PsA and cutaneous-only psoriasis (PsC) to assess PsA risk before symptoms appear. Here, we introduce a computational pipeline for predicting PsA among psoriasis patients using data from six cohorts with >7000 genotyped PsA and PsC patients. We identify 9 new loci for psoriasis or its subtypes and achieve 0.82 area under the receiver operator curve in distinguishing PsA vs. PsC when using 200 genetic markers. Among the top 5% of our PsA prediction we achieve >90% precision with 100% specificity and 16% recall for predicting PsA among psoriatic patients, using conditional inference forest or shrinkage discriminant analysis. Combining statistical and machine-learning techniques, we show that the underlying genetic differences between psoriasis subtypes can be used for individualized subtype risk assessment.

Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic markers.

GeneGini: Assessment via the Gini Coefficient of Reference "Housekeeping" Genes and Diverse Human Transporter Expression Profiles

The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping" genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.

Psoriasis-Specific RNA Isoforms Identified by RNA-Seq Analysis of 173,446 Transcripts

BACKGROUND

Several studies have been published that investigated potential links between transcriptome changes and psoriasis using microarrays and RNA-seq technologies, but no previous study has analyzed expression profile of alternatively spliced transcripts in psoriasis.

OBJECTIVES

Identification of potential alternatively spliced RNA isoforms with disease-specific expression profile.

METHODS

Using our published RNA sequencing data from lesional psoriatic (LP), non-lesional psoriatic (NLP), and normal control skin (C), we analyzed the differential expression of RNA splicing variants. LP sample was compared with NLP, as was LP with C and NLP with C.

RESULTS

Transcript-based annotation analyzed 173,446 transcripts (RNA isoforms), and around 9,000 transcripts were identified as differentially expressed between study groups. Several previously undescribed RNA variants were found. For instance, transcript ETV3_3 (ENST00000326786) was significantly downregulated in LP and NLP skin. ETV3 is a transcriptional repressor that contributes to the downstream anti-inflammatory effects of IL-10. We also identified diseases-specific transcripts (S100A7A, IL36RN_4, and IL36G_3) of genes already recognized to be involved in inflammation and immune response.

CONCLUSION

Psoriasis is characterized by significant differences in the expression of RNA alternative isoforms. Description of these new isoforms improves our knowledge about this complex disease.

NOD-like receptor signaling and inflammasome-related pathways are highlighted in psoriatic epidermis

Psoriatic skin differs distinctly from normal skin by its thickened epidermis. Most gene expression comparisons utilize full-thickness biopsies, with substantial amount of dermis. We assayed the transcriptomes of normal, lesional, and non-lesional psoriatic epidermis, sampled as split-thickness skin grafts, with 5′-end RNA sequencing. We found that psoriatic epidermis contains more mRNA per total RNA than controls, and took this into account in the bioinformatic analysis. The approach highlighted innate immunity-related pathways in psoriasis, including NOD-like receptor (NLR) signaling and inflammasome activation. We demonstrated that the NLR signaling genes NOD2, PYCARD, CARD6, and IFI16 are upregulated in psoriatic epidermis, and strengthened these findings by protein expression. Interestingly, PYCARD, the key component of the inflammasome, showed an altered expression pattern in the lesional epidermis. The profiling of non-lesional skin highlighted PSORS4 and mitochondrially encoded transcripts, suggesting that their gene expression is altered already before the development of lesions. Our data suggest that all components needed for the active inflammasome are present in the keratinocytes of psoriatic skin. The characterization of inflammasome pathways provides further opportunities for therapy. Complementing previous transcriptome studies, our approach gives deeper insight into the gene regulation in psoriatic epidermis.

Current knowledge on psoriasis and autoimmune diseases

Psoriasis is a prevalent, chronic inflammatory disease of the skin, mediated by crosstalk between epidermal keratinocytes, dermal vascular cells, and immunocytes such as antigen presenting cells (APCs) and T cells. Exclusive cellular “responsibility” for the induction and maintenance of psoriatic plaques has not been clearly defined. Increased proliferation of keratinocytes and endothelial cells in conjunction with APC/T cell/monocyte/macrophage inflammation leads to the distinct epidermal and vascular hyperplasia that is characteristic of lesional psoriatic skin. Despite the identification of numerous susceptibility loci, no single genetic determinant has been identified as responsible for the induction of psoriasis. Thus, numerous other triggers of disease, such as environmental, microbial and complex cellular interactions must also be considered as participants in the development of this multifactorial disease. Recent advances in therapeutics, especially systemic so-called “biologics” have provided new hope for identifying the critical cellular targets that drive psoriasis pathogenesis. Recent recognition of the numerous co-morbidities and other autoimmune disorders associated with psoriasis, including inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, and systemic lupus erythematosus suggest common signaling elements and cellular mediators may direct disease pathogenesis. In this review, we discuss common cellular pathways and participants that mediate psoriasis and other autoimmune disorders that share these cellular signaling pathways.

Gene expression profiling reveals the role of RIG1 like receptor signaling in p53 dependent apoptosis induced by PUVA in keratinocytes

Photochemotherapy using 8-methoxypsoralen in combination with UVA radiation (PUVA) is an effective treatment for various skin dermatosis including psoriasis however its molecular mechanism is not clear. Previously we demonstrated that PUVA differentially regulates miRNA expression profile with a significant up-regulation of hsa-miR-4516. To study in detail the molecular mechanism of PUVA in keratinocytes, we investigated the genome wide transcriptomic changes using Illumina whole genome gene expression beadchip. Microarray analysis revealed 1932 differentially expressed gene and their Insilico analysis revealed Retinoic Acid Inducible Gene-I (RIG-1) signaling, apoptosis and p53 pathway to be associated with PUVA induced effects. We demonstrate that miR-4516 mediated down-regulation of UBE2N promotes p53 nuclear translocation and pro-apoptotic activity of PUVA is independent of IRF3 but is mediated by the RIG-I in a p53 and NFκB dependent manner. Additionally, PUVA inactivated the AKT/mTOR pathway in concert with inhibition of autophagy and suppressed cell migration. Taken together this study broadens our understanding about the mechanism of action of PUVA providing possible new strategy targeting proapoptotic function of RIG-1, a regulator of innate immune response or p53 for psoriasis therapy.

Histological Stratification of Thick and Thin Plaque Psoriasis Explores Molecular Phenotypes with Clinical Implications

Psoriasis, which presents as red, scaly patches on the body, is a common, autoimmune skin disease that affects 2 to 3 percent of the world population. To leverage recent molecular findings into the personalized treatment of psoriasis, we need a strategy that integrates clinical stratification with molecular phenotyping. In this study, we sought to stratify psoriasis patients by histological measurements of epidermal thickness, and to compare their molecular characterizations by gene expression, serum cytokines, and response to biologics. We obtained histological measures of epidermal thickness in a cohort of 609 psoriasis patients, and identified a mixture of two subpopulations—thick and thin plaque psoriasis—from which they were derived. This stratification was verified in a subcohort of 65 patients from a previously published study with significant differences in inflammatory cell infiltrates in the psoriatic skin. Thick and thin plaque psoriasis shared 84.8% of the meta-analysis-derived psoriasis transcriptome, but a stronger dysregulation of the meta-analysis-derived psoriasis transcriptome was seen in thick plaque psoriasis on microarray. RT-PCR revealed that gene expression in thick and thin plaque psoriasis was different not only within psoriatic lesional skin but also in peripheral non-lesional skin. Additionally, differences in circulating cytokines and their changes in response to biologic treatments were found between the two subgroups. All together, we were able to integrate histological stratification with molecular phenotyping as a way of exploring clinical phenotypes with different expression levels of the psoriasis transcriptome and circulating cytokines.

Transcriptional landscape of psoriasis identifies the involvement of IL36 and IL36RN

Background

In present study we performed whole transcriptome analysis in plaque psoriasis patients and compared lesional skin with non-lesional skin and with the skin from healthy controls. We sequenced total RNA from 12 lesional (LP), 12 non-lesional (NLP) and from 12 normal (C) skin biopsies.

Results

Compared with previous gene expression profiling studies we had three groups under analysis - LP, NLP and C. Using NLP samples allows to see the transcriptome of visually normal skin from psoriasis patient. In LP skin S100A12, S100A7A, LCE3E, DEFB4A, IL19 were found up regulated. In addition to already these well-described genes, we also found several other genes related to psoriasis. Namely, KLK9, OAS2, OAS3, PLA2G, IL36G, IL36RN were found to be significantly and consistently related to the psoriatic lesions and this finding is supported also by previous studies. The genes up-regulated in the LP samples were related to the innate immunity, IL17 and IL10 networks. In NLP samples innate immunity and IL17 network were activated, but activation of IL10 network was not evident. The transcriptional changes characteristic in the NLP samples can be considered as a molecular signature of “dormant psoriasis”.

Conclusions

Taken together, our study described the transcriptome profile characteristic for LP and NLP psoriatic skin. RNA profile of the NLP skin is in between the lesional and healthy skin, with its own specific pattern. We found that both LP and NLP have up-regulated IL17 network, whereas LP skin has up regulated IL10 related cytokines (IL19, IL20, IL24). Moreover, IL36G and IL36RN were identified as strong regulators of skin pathology in both LP and NLP skin samples, with stronger influence in LP samples.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1508-2) contains supplementary material, which is available to authorized users.

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework

BACKGROUND

Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies.

RESULTS

A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile.

CONCLUSIONS

The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

Psoriasis

Psoriasis is a common chronic inflammatory skin disease with a spectrum of clinical phenotypes and results from the interplay of genetic, environmental, and immunological factors. Four decades of clinical and basic research on psoriasis have elucidated many of the pathogenic mechanisms underlying disease and paved the way to effective targeted therapies. Here, we review this progress and identify future directions of study that are supported by a more integrative research approach and aim at further improving the patients' life.

Factors affecting response to biologic treatment in psoriasis

Psoriasis is a chronic, immune-mediated inflammatory skin disease, affecting approximately 2-4% of the population in western countries. Patients with a more severe form of the disease are typically considered for systemic therapy, including biologics. In spite of the overall superiority of biologic agents, the treatment response may differ substantially among individual patients. As with other medical conditions, a range of factors contribute to response heterogeneity observed in psoriasis. Proper identification of these factors can significantly improve the therapeutic decisions. This review focuses on potential genetic and nongenetic factors that may affect the treatment response and outcomes in patients with psoriasis.

Looking within the lesion: Large scale transcriptional profiling of psoriatic plaques

Psoriasis is a lifelong, chronic, recurring and highly variable skin disease. Psoriatic plaques are formed through induction of inflammation in the epidermis and deregulation of keratinocyte proliferation and differentiation. This results in red or silvery scaly patches on the surface of the epidermis. To look within the lesions and define the changes in gene expression in psoriasis, investigators compared the transcriptomes of psoriatic plaques, of uninvolved skin of patients and of skin from healthy individuals. In several large studies with many patients, the genes expressed at much higher level in psoriatic plaques included those responsible for the cell cycle, keratinocyte differentiation, and response to wounding; conversely, lipid and fatty acid metabolism enzymes were expressed at reduced levels. The nonlesional and healthy skin appeared fairly similar. The largest study included paired biopsies from 85 individual patients. The same group used transcription profiling to follow the course of treatment in a set of patients, and correlated changes in the transcriptome of blood samples of psoriatic patients. Importantly, a noninvasive technique involving tape-stripping of skin, has been shown effective in transcriptional studies of psoriasis. Current efforts are focused on deconvoluting the contributions of various cell types in psoriasis, keratinocytes, lymphocytes, fibroblasts etc. Taken as a whole, these efforts will lead to personalized medicine, i.e., to specific, individualized treatments of patients with psoriasis.

Integrative biology approach identifies cytokine targeting strategies for psoriasis

Cytokines are critical checkpoints of inflammation. The treatment of human autoimmune disease has been revolutionized by targeting inflammatory cytokines as key drivers of disease pathogenesis. Despite this, there exist numerous pitfalls when translating preclinical data into the clinic. We developed an integrative biology approach combining human disease transcriptome data sets with clinically relevant in vivo models in an attempt to bridge this translational gap. We chose interleukin-22 (IL-22) as a model cytokine because of its potentially important proinflammatory role in epithelial tissues. Injection of IL-22 into normal human skin grafts produced marked inflammatory skin changes resembling human psoriasis. Injection of anti-IL-22 monoclonal antibody in a human xenotransplant model of psoriasis, developed specifically to test potential therapeutic candidates, efficiently blocked skin inflammation. Bioinformatic analysis integrating both the IL-22 and anti-IL-22 cytokine transcriptomes and mapping them onto a psoriasis disease gene coexpression network identified key cytokine-dependent hub genes. Using knockout mice and small-molecule blockade, we show that one of these hub genes, the so far unexplored serine/threonine kinase PIM1, is a critical checkpoint for human skin inflammation and potential future therapeutic target in psoriasis. Using in silico integration of human data sets and biological models, we were able to identify a new target in the treatment of psoriasis.

[What's new in dermatological research?]

In 2013, news from research has clearly shown that dermatology is bound to occupy a more important place in fundamental research. Among these evidences are an increasing number of papers devoted to "Skin" in journals with the highest impact factors and the excellence of the scientific program of the International Investigative Dermatology Meeting held in May in Edinburgh. This paper outlines a selection of scientific works published between September 2012 and August 2013 or presented as communications at the IID Meeting. This selection was made based on the quality of methods used by the authors to obtain results, and on the impact of these scientific results in terms of pathophysiological and therapeutical advances.

Modulation of epidermal transcription circuits in psoriasis: new links between inflammation and hyperproliferation

Background

Whole-genome expression profiling has been used to characterize molecular-level differences between psoriasis lesions and normal skin. Pathway analysis, however, is complicated by the fact that expression profiles have been derived from bulk skin biopsies with RNA derived from multiple cell types.

Results

We analyzed gene expression across a large sample of psoriatic (PP) and uninvolved/normal (PN) skin biopsies (n = 215 patients). We identified 1975 differentially expressed genes, including 8 associated with psoriasis susceptibility loci. To facilitate pathway analysis, PP versus PN differences in gene expression were analyzed with respect to 235 gene modules, each containing genes with a similar expression pattern in keratinocytes and epidermis. We identified 30 differentially expressed modules (DEMs) biased towards PP-increased or PP-decreased expression. These DEMs were associated with regulatory axes involving cytokines (e.g., IFN-γ, IL-17A, TNF-α), transcription factors (e.g., STAT1, NF-κB, E2F, RUNX1) and chromatin modifiers (SETDB1). We identified an interferon-induced DEM with genes encoding anti-viral proteins (designated “STAT1-57”), which was activated in psoriatic epidermis but repressed following biologic therapy. Genes within this DEM shared a motif near the transcription start site resembling the interferon-stimulated response element (ISRE).

Conclusions

We analyzed a large patient cohort and developed a new approach for delineating epidermis-specific pathways and regulatory mechanisms that underlie altered gene expression in psoriasis. Our findings highlight previously unrecognized “transcription circuits” that can provide targets for development of non-systemic therapies.

Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients

BACKGROUND

Psoriasis lesions are characterized by large-scale shifts in gene expression. Mechanisms that underlie differentially expressed genes (DEGs), however, are not completely understood. We analyzed existing datasets to evaluate genome-wide expression in lesions from 163 psoriasis patients. Our aims were to identify mechanisms that drive differential expression and to characterize heterogeneity among lesions in this large sample.

RESULTS

We identified 1233 psoriasis-increased DEGs and 977 psoriasis-decreased DEGs. Increased DEGs were attributed to keratinocyte activity (56%) and infiltration of lesions by T-cells (14%) and macrophages (11%). Decreased DEGs, in contrast, were associated with adipose tissue (63%), epidermis (14%) and dermis (4%). KC/epidermis DEGs were enriched for genes induced by IL-1, IL-17A and IL-20 family cytokines, and were also disproportionately associated with AP-1 binding sites. Among all patients, 50% exhibited a heightened inflammatory signature, with increased expression of genes expressed by T-cells, monocytes and dendritic cells. 66% of patients displayed an IFN-γ-strong signature, with increased expression of genes induced by IFN-γ in addition to several other cytokines (e.g., IL-1, IL-17A and TNF). We show that such differences in gene expression can be used to differentiate between etanercept responders and non-responders.

CONCLUSIONS

Psoriasis DEGs are partly explained by shifts in the cellular composition of psoriasis lesions. Epidermal DEGs, however, may be driven by the activity of AP-1 and cellular responses to IL-1, IL-17A and IL-20 family cytokines. Among patients, we uncovered a range of inflammatory- and cytokine-associated gene expression patterns. Such patterns may provide biomarkers for predicting individual responses to biologic therapy.

Decreased serum level of IL-21 in new-onset systemic lupus erythematosus patients

This study aims to investigate the serum IL-21 levels in systemic lupus erythematosus (SLE) and its relations with clinical and laboratory features. Fifty-seven patients with SLE and 30 healthy volunteers were recruited in the current study. Serum IL-21 levels were detected by enzyme-linked immunosorbent assay. Statistical analyses were performed by SPSS 10.01. Results showed that IL-21 levels were significantly decreased in the serum of patients with SLE compared with controls (P = 0.026). There was no significant difference regarding serum IL-21 level between SLE patients with nephritis and those without nephritis (P = 0.066); no significant difference was found between less active SLE and more active SLE (P = 0.588). The presence of anemia was associated with low serum IL-21 levels (P = 0.030) in SLE patients. In summary, decreased serum level of IL-21 and its association with anemia indicate a possible role of IL-21 in human SLE. However, further studies are needed to confirm this preliminary results.