Integrated network analysis of transcriptomic and proteomic data in psoriasis

miR‑155 promotes an inflammatory response in HaCaT cells via the IRF2BP2/KLF2/NF‑κB pathway in psoriasis

Psoriasis is a chronic inflammatory skin condition with numerous causes, including genetic, immunological and infectious factors. The course of psoriasis is long and recurrence is common; pathogenesis is not completely understood. However, there is an association between advancement of psoriasis and aberrant microRNA (miR or miRNA)‑155 expression. Through bioinformatics, the present study aimed to analyze the differentially expressed genes and miRNAs in psoriasis and its biological mechanism and function psoriatic inflammation. First of all, differentially expressed genes (DEGs) and miRNAs (DEMs) in patients with psoriasis were identified using GEO2R interactive web application. A psoriasis inflammatory model was established using lipopolysaccharide (LPS)‑treated HaCaT keratinocytes, which were transfected with miR‑155 mimic or inhibitor. Cell Counting Kit‑8 was used for the assessment of cell viability and proliferation, and changes in the cell cycle were examined using flow cytometry. ELISA and reverse transcription‑quantitative PCR (RT‑qPCR) were used to detect the expression levels of the inflammatory factors IL‑1β and IL‑6. The dual‑luciferase reporter assay was used to verify the targeting association between miR‑155‑5p and IFN regulatory factor 2 binding protein 2 (IRF2BP2). To verify the targeting association of miR‑155 and the IRF2BP2/kruppel‑like factor 2 (KLF2)/NF‑κB signaling pathway, expression levels of IRF2BP2, KLF2 and p65 were identified by RT‑qPCR and western blotting. IRF2BP2 levels were also confirmed by immunofluorescence, in conjunction with bioinformatics database analysis. Overexpression of miR‑155 inhibited proliferation of HaCaT cells and increased the number of cells in S phase and decreasing number of cells in G1 and G2 phase. In the LPS‑induced inflammatory state, miR‑155 overexpression heightened the inflammatory response of HaCaT cells while inhibition of miR‑155 lessened it. Suppression of inflammatory cytokine expression by miR‑155‑5p inhibitor was reversed by knockdown of IRF2BP2. miR‑155 was shown to interact with IRF2BP2 to negatively regulate its expression, leading to decreased KLF2 expression and increased p65 expression and secretion of inflammatory factors, intensifying the inflammatory response of HaCaT cells. Therefore, miR‑155 may contribute to development of psoriasis by inducing tissue and cell damage by increasing the inflammatory response of HaCaT cells via the IRF2BP2/KLF2/NF‑κB pathway. In conclusion, the results of the present study offer novel perspectives on the role of miR‑155 in the onset and progression of psoriasis.

Single-cell epigenomic dysregulation of Systemic Sclerosis fibroblasts via CREB1/EGR1 axis in self-assembled human skin equivalents

Systemic sclerosis (SSc) is an autoimmune disease characterized by skin fibrosis, internal organ involvement and vascular dropout. We previously developed and phenotypically characterized an in vitro 3D skin-like tissue model of SSc, and now analyze the transcriptomic (scRNA-seq) and epigenetic (scATAC-seq) characteristics of this model at single-cell resolution. SSc 3D skin-like tissues were fabricated using autologous fibroblasts, macrophages, and plasma from SSc patients or healthy control (HC) donors. SSc tissues displayed increased dermal thickness and contractility, as well as increased α-SMA staining. Single-cell transcriptomic and epigenomic analyses identified keratinocytes, macrophages, and five populations of fibroblasts (labeled FB1 - 5). Notably, FB1 APOE-expressing fibroblasts were 12-fold enriched in SSc tissues and were characterized by high EGR1 motif accessibility. Pseudotime analysis suggests that FB1 fibroblasts differentiate from a TGF-β1-responsive fibroblast population and ligand-receptor analysis indicates that the FB1 fibroblasts are active in macrophage crosstalk via soluble ligands including FGF2 and APP. These findings provide characterization of the 3D skin-like model at single cell resolution and establish that it recapitulates subsets of fibroblasts and macrophage phenotypes observed in skin biopsies.

Multi-omics approach identifies PI3 as a biomarker for disease severity and hyper-keratinization in psoriasis

BACKGROUND

Psoriasis is an immune-mediated inflammatory skin disease. Psoriasis severity evaluation is important for clinicians in the assessment of disease severity and subsequent clinical decision making. However, no objective biomarker is available for accurately evaluating disease severity in psoriasis.

OBJECTIVE

To define and compare biomarkers of disease severity and progression in psoriatic skin.

METHODS

We performed proteome profiling to study the proteins circulating in the serum from patients with psoriasis, psoriatic arthritis and ankylosing spondylitis, and transcriptome sequencing to investigate the gene expression in skin from the same cohort. We then used machine learning approaches to evaluate different biomarker candidates across several independent cohorts. In order to reveal the cell-type specificity of different biomarkers, we also analyzed a single-cell dataset of skin samples. In-situ staining was applied for the validation of biomarker expression.

RESULTS

We identified that the peptidase inhibitor 3 (PI3) was significantly correlated with the corresponding local skin gene expression, and was associated with disease severity. We applied machine learning methods to confirm that PI3 was an effective psoriasis classifier, Finally, we validated PI3 as psoriasis biomarker using in-situ staining and public datasets. Single-cell data and in-situ staining indicated that PI3 was specifically highly expressed in keratinocytes from psoriatic lesions.

CONCLUSION

Our results suggest that PI3 may be a psoriasis-specific biomarker for disease severity and hyper-keratinization.

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.

Proteomic and Metabolomic Changes in Psoriasis Preclinical and Clinical Aspects

Skin diseases such as psoriasis (Ps) and psoriatic arthritis (PsA) are immune-mediated inflammatory diseases. Overlap of autoinflammatory and autoimmune conditions hinders diagnoses and identifying personalized patient treatments due to different psoriasis subtypes and the lack of verified biomarkers. Recently, proteomics and metabolomics have been intensively investigated in a broad range of skin diseases with the main purpose of identifying proteins and small molecules involved in the pathogenesis and development of the disease. This review discusses proteomics and metabolomics strategies and their utility in research and clinical practice in psoriasis and psoriasis arthritis. We summarize the studies, from in vivo models conducted on animals through academic research to clinical trials, and highlight their contribution to the discovery of biomarkers and targets for biological drugs.

Multi-Omics Research Strategies for Psoriasis and Atopic Dermatitis

Psoriasis and atopic dermatitis (AD) are multifactorial and heterogeneous inflammatory skin diseases, while years of research have yielded no cure, and the costs associated with caring for people suffering from psoriasis and AD are a huge burden on society. Integrating several omics datasets will enable coordinate-based simultaneous analysis of hundreds of genes, RNAs, chromatins, proteins, and metabolites in particular cells, revealing networks of links between various molecular levels. In this review, we discuss the latest developments in the fields of genomes, transcriptomics, proteomics, and metabolomics and discuss how they were used to identify biomarkers and understand the main pathogenic mechanisms underlying these diseases. Finally, we outline strategies for achieving multi-omics integration and how integrative omics and systems biology can advance our knowledge of, and ability to treat, psoriasis and AD.

Identification of novel immune subtypes and potential hub genes of patients with psoriasis

BACKGROUND

Psoriasis is a common, chronic and relapsing immune-related inflammatory dermal disease. Patients with psoriasis suffering from the recurrences is mainly caused by immune response disorder. Thus, our study is aimed to identify novel immune subtypes and select targeted drugs for the precision therapy in different subtypes of psoriasis.

METHODS

Differentially expressed genes of psoriasis were identified from the Gene Expression Omnibus database. Functional and disease enrichment were performed by Gene Set Enrichment Analysis and Disease Ontology Semantic and Enrichment analysis. Hub genes of psoriasis were selected from protein-protein interaction networks using Metascape database. The expression of hub genes was validated in human psoriasis samples by RT-qPCR and immunohistochemistry. Further, novel immune subtypes of psoriasis were identified by ConsensusClusterPlus package and its association with hub genes were calculated. Immune infiltration analysis was performed, and its candidate drugs were evaluated by Connectivity Map analysis.

RESULTS

182 differentially expressed genes of psoriasis were identified from GSE14905 cohort, in which 99 genes were significantly up-regulated and 83 genes were down-regulated. We then conducted functional and disease enrichment in up-regulated genes of psoriasis. Five potential hub genes of psoriasis were obtained, including SOD2, PGD, PPIF, GYS1 and AHCY. The high expression of hub genes was validated in human psoriasis samples. Notably, two novel immune subtypes of psoriasis were determined and defined as C1 and C2. Bioinformatic analysis showed C1 and C2 had different enrichment in immune cells. Further, candidate drugs and mechanism of action that applicable to different subtypes were evaluated.

CONCLUSIONS

Our study identified two novel immune subtypes and five potential hub genes of psoriasis. These findings might give insight into the pathogenesis of psoriasis and provide effective immunotherapy regimens for the precise treatment of psoriasis.

Integrated transcriptome-proteome analyses of human stem cells reveal source-dependent differences in their regenerative signature

Mesenchymal stem cells (MSCs) are gaining increasing prominence as an effective regenerative cellular therapy. However, ensuring consistent and reliable effects across clinical populations has proved to be challenging. In part, this can be attributed to heterogeneity in the intrinsic molecular and regenerative signature of MSCs, which is dependent on their source of origin. The present work uses integrated omics-based profiling, at different functional levels, to compare the anti-inflammatory, immunomodulatory, and angiogenic properties between MSCs from neonatal (umbilical cord MSC [UC-MSC]) and adult (adipose tissue MSC [AD-MSC], and bone marrow MSC [BM-MSC]) sources. Using multi-parametric analyses, we identified that UC-MSCs promote a more robust host innate immune response; in contrast, adult-MSCs appear to facilitate remodeling of the extracellular matrix (ECM) with stronger activation of angiogenic cascades. These data should help facilitate the standardization of source-specific MSCs, such that their regenerative signatures can be confidently used to target specific disease processes.

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

Effects of Autophagy Modulators and Dioxin on the Expression of Epidermal Differentiation Proteins on Psoriasis-Like Keratinocytes in vitro and ex vivo

Objective

Psoriasis is a chronic inflammatory skin disorder associated with impairment of epidermal differentiation. Many signaling pathways, including those involved in aryl hydrocarbon receptor (AHR) and autophagy dysfunction, are reportedly associated with the pathogenesis of psoriasis. However, the discrete effects of dioxin via AHR activation or autophagy on the epidermal barrier remain unclear. In the current study, we evaluated the effects of autophagy modulators (chloroquine [CQ] and rapamycin) and the AHR agonist TCDD on the expression of epidermal barrier proteins in psoriasis-like keratinocytes and psoriasis lesional skin tissue culture.

Methods

Polycytokine-stimulated human keratinocytes and psoriasis skin biopsies were treated with TCDD, CQ, or rapamycin, and the expression of keratinocyte differentiation-related factors, such as S100A7, S100A8, HRNR, IVL, FLG, and KRT10, was examined by Western blotting or quantitative-polymerase chain reaction.

Results

TCDD upregulated S100A7 and S100A8 expression in polycytokine-stimulated HaCaT cells compared to that in unstimulated cells. CQ decreased HRNR, IVL, and KRT10 mRNA levels, while rapamycin increased HRNR, IVL, and KRT10 mRNA levels in HaCaT cells relative to that in unstimulated cells. Co-treatment with CQ reversed TCDD-induced elevation in FLG, HRNR, and IVL mRNA expression. In psoriasis skin tissue, TCDD induced the upregulation of HRNR, IVL, S100A7, and S100A8 compared with that in normal skin. In ex vivo cultures treated with CQ, IVL expression in psoriasis skin tissue was repressed compared to that in normal skin tissue.

Conclusion

Our data suggest that autophagy modulation or AHR activation affects processes involved in epidermal differentiation and relates to the pathogenesis of chronic inflammatory skin diseases with skin barrier abnormalities such as psoriasis.

Proteomic Studies of Psoriasis

In this review paper, we discuss the contribution of proteomic studies to the discovery of disease-specific biomarkers to monitor the disease and evaluate available treatment options for psoriasis. Psoriasis is one of the most prevalent skin disorders driven by a Th17-specific immune response. Although potential patients have a genetic predisposition to psoriasis, the etiology of the disease remains unknown. During the last two decades, proteomics became deeply integrated with psoriatic research. The data obtained in proteomic studies facilitated the discovery of novel mechanisms and the verification of many experimental hypotheses of the disease pathogenesis. The detailed data analysis revealed multiple differentially expressed proteins and significant changes in proteome associated with the disease and drug efficacy. In this respect, there is a need for proteomic studies to characterize the role of the disease-specific biomarkers in the pathogenesis of psoriasis, develop clinical applications to choose the most efficient treatment options and monitor the therapeutic response.

LC-MS/MS analysis of lesional and normally looking psoriatic skin reveals significant changes in protein metabolism and RNA processing

Background

Plaque psoriasis is a chronic autoimmune disorder characterized by the development of red scaly plaques. To date psoriasis lesional skin transcriptome has been extensively studied, whereas only few proteomic studies of psoriatic skin are available.

Aim

The aim of this study was to compare protein expression patterns of lesional and normally looking skin of psoriasis patients with skin of the healthy volunteers, reveal differentially expressed proteins and identify changes in cell metabolism caused by the disease.

Methods

Skin samples of normally looking and lesional skin donated by psoriasis patients (n = 5) and samples of healthy skin donated by volunteers (n = 5) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). After protein identification and data processing, the set of differentially expressed proteins was subjected to protein ontology analysis to characterize changes in biological processes, cell components and molecular functions in the patients’ skin compared to skin of the healthy volunteers. The expression of selected differentially expressed proteins was validated by ELISA and immunohistochemistry.

Results

The performed analysis identified 405 and 59 differentially expressed proteins in lesional and normally looking psoriatic skin compared to healthy control. In normally looking skin of the patients, we discovered decreased expression of KNG1, APOE, HRG, THBS1 and PLG. Presumably, these changes were needed to protect the epidermis from spontaneous activation of kallikrein-kinin system and delay the following development of inflammatory response. In lesional skin, we identified several large groups of proteins with coordinated expression. Mainly, these proteins were involved in different aspects of protein and RNA metabolism, namely ATP synthesis and consumption; intracellular trafficking of membrane-bound vesicles, pre-RNA processing, translation, chaperoning and degradation in proteasomes/immunoproteasomes.

Conclusion

Our findings explain the molecular basis of metabolic changes caused by disease in skin lesions, such as faster cell turnover and higher metabolic rate. They also indicate on downregulation of kallikrein-kinin system in normally looking skin of the patients that would be needed to delay exacerbation of the disease. Data are available via ProteomeXchange with identifier PXD021673.

Application of omics technologies in dermatological research and skin management

BACKGROUND

"Omics" are usually based on the use of high-throughput analysis methods for global analysis of biological samples and the discovery of biomarkers, and may provide new insights into biological phenomena. Over the last few years, the development of omics technologies has considerably accelerated the pace of dermatological research.

AIMS

The purpose of this article was to review the development of omics in recent decades and their application in dermatological research.

METHODS

An extensive literature search was conducted on omics technologies since the first research on omics.

RESULTS

This article summarizes the history and main research methods of the six omics technologies, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics. Their application in certain skin diseases and cosmetics research and development are also summarized.

CONCLUSIONS

This information will help to understand the mechanism of some skin diseases and the discovery of potential biomarkers, and provide new insights for skin health management and cosmetics research and development.

Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis

(1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis’ etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein–protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein–protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic interactions. (4) Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed workflow could be of interest to a broader range of future biological network analysis studies.

Comprehensive Proteomic Analysis Reveals Intermediate Stage of Non-Lesional Psoriatic Skin and Points out the Importance of Proteins Outside this Trend

To better understand the pathomechanism of psoriasis, a comparative proteomic analysis was performed with non-lesional and lesional skin from psoriasis patients and skin from healthy individuals. Strikingly, 79.9% of the proteins that were differentially expressed in lesional and healthy skin exhibited expression levels in non-lesional skin that were within twofold of the levels observed in healthy and lesional skin, suggesting that non-lesional skin represents an intermediate stage. Proteins outside this trend were categorized into three groups: I. proteins in non-lesional skin exhibiting expression similar to lesional skin, which might be predisposing factors (i.e., CSE1L, GART, MYO18A and UGDH); II. proteins that were differentially expressed in non-lesional and lesional skin but not in healthy and lesional skin, which might be non-lesional characteristic alteration (i.e., CHCHD6, CHMP5, FLOT2, ITGA7, LEMD2, NOP56, PLVAP and RRAS); and III. proteins with contrasting differential expression in non-lesional and lesional skin compared to healthy skin, which might contribute to maintaining the non-lesional state (i.e., ITGA7, ITGA8, PLVAP, PSAPL1, SMARCA5 and XP32). Finally, proteins differentially expressed in lesions may indicate increased sensitivity to stimuli, peripheral nervous system alterations, furthermore MYBBP1A and PRKDC were identified as potential regulators of key pathomechanisms, including stress and immune response, proliferation and differentiation.

The use of biomarkers as a tool for novel psoriatic disease drug discovery

INTRODUCTION

Psoriatic disease is a relatively new term which encompasses psoriatic arthritis, psoriasis, and associated comorbidities. In this heterogeneous condition, the study of biomarkers is necessary to direct best therapy. Resulting in significant disability and socioeconomic burden, recent recommendations stress the need for tight control in psoriatic disease. Areas covered: The authors outline recent advances in the understanding of psoriatic disease pathogenesis which has highlighted multiple biomarkers that have been pursued as drug targets with varying degrees of success. Current drugs targeting biomarkers and therapies in development are evaluated. The methods of biomarker discovery through genomics, transcriptomics, proteomics, metabolomics, and study of the microbiome are also discussed. Expert opinion: Targeting biomarkers for therapeutic benefit appears to a promising field with multiple success stories, notably those associated with signaling through T-helper-17 cells. The use of genomics, transcriptomics, proteomics, and more recently metabolomics will help individualize targeted biomarker therapies, assist in monitoring therapeutic success, and ultimately yield novel therapeutic targets. Advances in the development of novel biologic molecules targeting more than one cytokine may offer additional gains in therapeutic response.

Molecular Mechanisms and Management of a Cutaneous Inflammatory Disorder: Psoriasis

Psoriasis is a complex chronic inflammatory cutaneous disorder. To date, robust molecular mechanisms of psoriasis have been reported. Among diverse aberrant immunopathogenetic mechanisms, the current model emphasizes the role of Th1 and the IL-23/Th17 axis, skin-resident immune cells and major signal transduction pathways involved in psoriasis. The multiple genetic risk loci for psoriasis have been rapidly revealed with the advent of a novel technology. Moreover, identifying epigenetic modifications could bridge the gap between genetic and environmental risk factors in psoriasis. This review will provide a better understanding of the pathogenesis of psoriasis by unraveling the complicated interplay among immunological abnormalities, genetic risk foci, epigenetic modification and environmental factors of psoriasis. With advances in molecular biology, diverse new targets are under investigation to manage psoriasis. The recent advances in treatment modalities for psoriasis based on targeted molecules are also discussed.

Prediction of chemo-response in serous ovarian cancer

Background

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA.

Methods

We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model’s accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets.

Results

The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets.

Conclusions

These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-016-0548-9) contains supplementary material, which is available to authorized users.

Integrating transcriptome and proteome profiling: Strategies and applications

Discovering the gene expression signature associated with a cellular state is one of the basic quests in majority of biological studies. For most of the clinical and cellular manifestations, these molecular differences may be exhibited across multiple layers of gene regulation like genomic variations, gene expression, protein translation and post-translational modifications. These system wide variations are dynamic in nature and their crosstalk is overwhelmingly complex, thus analyzing them separately may not be very informative. This necessitates the integrative analysis of such multiple layers of information to understand the interplay of the individual components of the biological system. Recent developments in high throughput RNA sequencing and mass spectrometric (MS) technologies to probe transcripts and proteins made these as preferred methods for understanding global gene regulation. Subsequently, improvements in "big-data" analysis techniques enable novel conclusions to be drawn from integrative transcriptomic-proteomic analysis. The unified analyses of both these data types have been rewarding for several biological objectives like improving genome annotation, predicting RNA-protein quantities, deciphering gene regulations, discovering disease markers and drug targets. There are different ways in which transcriptomics and proteomics data can be integrated; each aiming for different research objectives. Here, we review various studies, approaches and computational tools targeted for integrative analysis of these two high-throughput omics methods.

The role of Eph receptors and Ephrins in the skin

Eph receptors and Ephrin ligands are widely expressed in the skin. Various studies have been carried out to identify the effects of these molecules on many aspects of skin development. Here we summarize the literature that has identified roles for Eph receptors and Ephrins in the skin, focusing mainly on the epidermis, hair follicles, and cutaneous innervation. This review may help direct and focus further investigations into the role of Eph receptors and Ephrins in the development, maintenance, and repair processes in cutaneous biology.

Biomarkers of An Autoimmune Skin Disease--Psoriasis

Psoriasis is one of the most prevalent autoimmune skin diseases. However, its etiology and pathogenesis are still unclear. Over the last decade, omics-based technologies have been extensively utilized for biomarker discovery. As a result, some promising markers for psoriasis have been identified at the genome, transcriptome, proteome, and metabolome level. These discoveries have provided new insights into the underlying molecular mechanisms and signaling pathways in psoriasis pathogenesis. More importantly, some of these markers may prove useful in the diagnosis of psoriasis and in the prediction of disease progression once they have been validated. In this review, we summarize the most recent findings in psoriasis biomarker discovery. In addition, we will discuss several emerging technologies and their potential for novel biomarker discovery and diagnostics for psoriasis.

Cytokines in psoriasis

Psoriasis is a common inflammatory skin disease with an incompletely understood etiology. The disease is characterized by red, scaly and well-demarcated skin lesions formed by the hyperproliferation of epidermal keratinocytes. This hyperproliferation is driven by cytokines secreted by activated resident immune cells, an infiltrate of T cells, dendritic cells and cells of the innate immune system, as well as the keratinocytes themselves. Psoriasis has a strong hereditary character and has a complex genetic background. Genome-wide association studies have identified polymorphisms within or near a number of genes encoding cytokines, cytokine receptors or elements of their signal transduction pathways, further implicating these cytokines in the psoriasis pathomechanism. A considerable number of inflammatory cytokines have been shown to be elevated in lesional psoriasis skin, and the serum concentrations of a subset of these also correlate with psoriasis disease severity. The combined effects of the cytokines found in psoriasis lesions likely explain most of the clinical features of psoriasis, such as the hyperproliferation of keratinocytes, increased neovascularization and skin inflammation. Thus, understanding which cytokines play a pivotal role in the disease process can suggest potential therapeutic targets. A number of cytokines have been therapeutically targeted with success, revolutionizing treatment of this disease. Here we review a number of key cytokines implicated in the pathogenesis of psoriasis.

Proteomics of skin proteins in psoriasis: from discovery and verification in a mouse model to confirmation in humans

Herein, we demonstrate the efficacy of an unbiased proteomics screening approach for studying protein expression changes in the KC-Tie2 psoriasis mouse model, identifying multiple protein expression changes in the mouse and validating these changes in human psoriasis. KC-Tie2 mouse skin samples (n = 3) were compared with littermate controls (n = 3) using gel-based fractionation followed by label-free protein expression analysis. 5482 peptides mapping to 1281 proteins were identified and quantitated: 105 proteins exhibited fold-changes ≥2.0 including: stefin A1 (average fold change of 342.4 and an average p = 0.0082; cystatin A, human ortholog); slc25a5 (average fold change of 46.2 and an average p = 0.0318); serpinb3b (average fold change of 35.6 and an average p = 0.0345; serpinB1, human ortholog); and kallikrein related peptidase 6 (average fold change of 4.7 and an average p = 0.2474; KLK6). We independently confirmed mouse gene expression-based increases of selected genes including serpinb3b (17.4-fold, p < 0.0001), KLK6 (9-fold, p = 0.002), stefin A1 (7.3-fold; p < 0.001), and slc25A5 (1.5-fold; p = 0.05) using qRT-PCR on a second cohort of animals (n = 8). Parallel LC/MS/MS analyses on these same samples verified protein-level increases of 1.3-fold (slc25a5; p < 0.05), 29,000-fold (stefinA1; p < 0.01), 322-fold (KLK6; p < 0.0001) between KC-Tie2 and control mice. To underscore the utility and translatability of our combined approach, we analyzed gene and protein expression levels in psoriasis patient skin and primary keratinocytes versus healthy controls. Increases in gene expression for slc25a5 (1.8-fold), cystatin A (3-fold), KLK6 (5.8-fold), and serpinB1 (76-fold; all p < 0.05) were observed between healthy controls and involved lesional psoriasis skin and primary psoriasis keratinocytes. Moreover, slc25a5, cystatin A, KLK6, and serpinB1 protein were all increased in lesional psoriasis skin compared with normal skin. These results highlight the usefulness of preclinical disease models using readily-available mouse skin and demonstrate the utility of proteomic approaches for identifying novel peptides/proteins that are differentially regulated in psoriasis that could serve as sources of auto-antigens or provide novel therapeutic targets for the development of new anti-psoriatic treatments.

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.

Proteomics focusing on immune markers in psoriatic arthritis

The immune-pathogenesis of psoriatic arthritis represents a subject of intense research, as a still unknown factor can trigger the chronic inflammation that, upon a defective immune terrain, generates this auto-immune/auto-inflammatory condition. The pathogenesis complexity of psoriatic arthritis resides in the psoriatic synovitis milieu, where intricate immune relations are emerging during disease development. Innate immune response generates inflammatory cytokines driving effectors functions for immune and non-immune cells that sustain the chronical character of the synovitis. Herein, we review the updated information regarding biomarkers/immune markers that sustain the heterogeneity and complexity of psoriatic arthritis pathogenesis, this complexity leading to multifaceted methodological approaches for disease investigation. New immune proteomic or genomic biomarkers can enlarge and identify new therapeutic targets.

Comparisons of gene expression in normal, lesional, and non-lesional psoriatic skin using DNA microarray techniques

OBJECTIVES

This study was designed to explore the pathogenesis of psoriasis and to identify potential bio-targets. Genome array technology was used to analyze the gene expression profiles of lesional and non-lesional psoriatic skin samples and normal skin samples.

METHODS

Gene expression profile GSE14905 was downloaded from the Gene Expression Omnibus (GEO) database. This included skin biopsy samples from normal healthy donors (n = 21), lesional skin biopsy samples from psoriasis patients (n = 33), and non-lesional skin biopsy samples from psoriasis patients (n = 28). Differentially expressed genes (DEGs) were identified using the Limma package in R language. Functions of specific DEGs were predicted by Gene Ontology (GO) enrichment analysis. A protein-protein interaction network was constructed to display the interactions among common DEGs. Finally, DAVID and WebGestalt were used to achieve a functional analysis of common DEGs.

RESULTS

Totals of 1020, 562, and 643 genes, respectively, were identified as being differentially expressed in normal versus lesional, normal versus non-lesional, and lesional versus non-lesional samples. The specific DEGs in the three groups were enriched for several GO terms, including mitotic cell cycle, immune response, and response to organic matter. The 40 common DEGs in the three groups may be involved in the defense response pathway in the development of psoriasis. Furthermore, three genes (RGS1, SOCS3, and NAMPT) may play key roles in distinguishing lesional and non-lesional tissues from normal tissues, and 10 genes (PTRRC, ALDH1A3, SAMSA1, C15orf48, ZC3H12A, SOD2, IL8, LTF, RHCG, and IL7R) may play key roles in distinguishing non-lesional from normal and lesional samples.

CONCLUSIONS

These genes may be considered as potential diagnostic markers and targets of therapeutics in 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 omics analysis. A study based on Plasmodium falciparum mRNA and protein data

Background

Technological improvements have shifted the focus from data generation to data analysis. The availability of large amounts of data from transcriptomics, protemics and metabolomics experiments raise new questions concerning suitable integrative analysis methods. We compare three integrative analysis techniques (co-inertia analysis, generalized singular value decomposition and integrative biclustering) by applying them to gene and protein abundance data from the six life cycle stages of Plasmodium falciparum. Co-inertia analysis is an analysis method used to visualize and explore gene and protein data. The generalized singular value decomposition has shown its potential in the analysis of two transcriptome data sets. Integrative Biclustering applies biclustering to gene and protein data.

Results

Using CIA, we visualize the six life cycle stages of Plasmodium falciparum, as well as GO terms in a 2D plane and interpret the spatial configuration. With GSVD, we decompose the transcriptomic and proteomic data sets into matrices with biologically meaningful interpretations and explore the processes captured by the data sets. IBC identifies groups of genes, proteins, GO Terms and life cycle stages of Plasmodium falciparum. We show method-specific results as well as a network view of the life cycle stages based on the results common to all three methods. Additionally, by combining the results of the three methods, we create a three-fold validated network of life cycle stage specific GO terms: Sporozoites are associated with transcription and transport; merozoites with entry into host cell as well as biosynthetic and metabolic processes; rings with oxidation-reduction processes; trophozoites with glycolysis and energy production; schizonts with antigenic variation and immune response; gametocyctes with DNA packaging and mitochondrial transport. Furthermore, the network connectivity underlines the separation of the intraerythrocytic cycle from the gametocyte and sporozoite stages.

Conclusion

Using integrative analysis techniques, we can integrate knowledge from different levels and obtain a wider view of the system under study. The overlap between method-specific and common results is considerable, even if the basic mathematical assumptions are very different. The three-fold validated network of life cycle stage characteristics of Plasmodium falciparum could identify a large amount of the known associations from literature in only one study.

Study on Certain Biomarkers of Inflammation in Psoriasis Through "OMICS" Platforms

Background:

In recent years, research on psoriasis has focused on the identification of biomarkers for the diagnosis, pathogenesis, prognosis, or therapeutic response of the disease. These studies could provide insights into the susceptibility and natural history of psoriasis. The identification of biomarkers related to comorbidities in psoriasis, such as arthritis, cardiovascular disease, and the metabolic syndrome, is of special clinical interest.

Materials and Methods:

We performed an extensive review on psoriasis biomarkers, including cytokine and growth factors, in the literature published between 1997 and 2013, including cross-references of any retrieved articles. We also included some data from our own studies.

Results:

This review presents current knowledge of soluble biomarkers in psoriasis, including cytokines, chemokines, proangiogenic mediators, growth factors, antimicrobial proteins, neuropeptides, and oxidative stress markers.

Conclusion:

In conclusion, a number of studies have been conducted with the aim of establishing soluble biomarkers for psoriasis. Most of the biomarkers that have been studied do not meet the criteria for a clinically useful biomarker. Further work is needed to establish a role for soluble biomarkers in the diagnosis and treatment of psoriasis, with a special focus on biomarkers for psoriasis comorbidities, such as arthritis, cardiovascular disease, and the metabolic syndrome.

Mapping the protein profile involved in the biotransformation of organoarsenicals using an arsenic metabolizing bacterium

Insight into the organoarsenic metabolism of Alkaliphilus oremlandii OhILAs by comprehensive proteomic analysis.

Moving H5N1 studies into the era of systems biology

The dynamics of H5N1 influenza virus pathogenesis are multifaceted and can be seen as an emergent property that cannot be comprehended without looking at the system as a whole. In past years, most of the high-throughput studies on H5N1-host interactions have focused on the host transcriptomic response, at the cellular or the lung tissue level. These studies pointed out that the dynamics and magnitude of the innate immune response and immune cell infiltration is critical to H5N1 pathogenesis. However, viral-host interactions are multidimensional and advances in technologies are creating new possibilities to systematically measure additional levels of 'omic data (e.g. proteomic, metabolomic, and RNA profiling) at each temporal and spatial scale (from the single cell to the organism) of the host response. Natural host genetic variation represents another dimension of the host response that determines pathogenesis. Systems biology models of H5N1 disease aim at understanding and predicting pathogenesis through integration of these different dimensions by using intensive computational modeling. In this review, we describe the importance of 'omic studies for providing a more comprehensive view of infection and mathematical models that are being developed to integrate these data. This review provides a roadmap for what needs to be done in the future and what computational strategies should be used to build a global model of H5N1 pathogenesis. It is time for systems biology of H5N1 pathogenesis to take center stage as the field moves toward a more comprehensive view of virus-host interactions.

Integrated analysis of global mRNA and protein expression data in HEK293 cells overexpressing PRL-1

BACKGROUND

The protein tyrosine phosphatase PRL-1 represents a putative oncogene with wide-ranging cellular effects. Overexpression of PRL-1 can promote cell proliferation, survival, migration, invasion, and metastasis, but the underlying mechanisms by which it influences these processes remain poorly understood.

METHODOLOGY

To increase our comprehension of PRL-1 mediated signaling events, we employed transcriptional profiling (DNA microarray) and proteomics (mass spectrometry) to perform a thorough characterization of the global molecular changes in gene expression that occur in response to stable PRL-1 overexpression in a relevant model system (HEK293).

PRINCIPAL FINDINGS

Overexpression of PRL-1 led to several significant changes in the mRNA and protein expression profiles of HEK293 cells. The differentially expressed gene set was highly enriched in genes involved in cytoskeletal remodeling, integrin-mediated cell-matrix adhesion, and RNA recognition and splicing. In particular, members of the Rho signaling pathway and molecules that converge on this pathway were heavily influenced by PRL-1 overexpression, supporting observations from previous studies that link PRL-1 to the Rho GTPase signaling network. In addition, several genes not previously associated with PRL-1 were found to be significantly altered by its expression. Most notable among these were Filamin A, RhoGDIα, SPARC, hnRNPH2, and PRDX2.

CONCLUSIONS AND SIGNIFICANCE

This systems-level approach sheds new light on the molecular networks underlying PRL-1 action and presents several novel directions for future, hypothesis-based studies.

Pharmacological control of receptor of advanced glycation end-products and its biological effects in psoriasis

Receptor for advanced glycation end-products is implicated in a development of chronic inflammatory response. Aim of this paper is to provide a review on commercial and experimental medicines that can interfere with RAGE and signaling through RAGE. We searched three bibliographical databases (PubMed, Web of Science and MEDLINE) for the publications from 2005 to March 2012 and identified 5 major groups of agents that can interfere with RAGE biological effects. In the first part of this paper, we discuss AGE crosslink breakers. These chemicals destroy advanced glycation end products (AGEs) that are crosslinked to the extracellular matrix proteins and can interact with RAGE as ligands. Then, we describe two non-conventional agents SAGEs and KIOM-79 that abolish certain biological effects of RAGE and have a strong anti-inflammatory potential. In the third part, we evaluate the inhibitors of the signaling cascades that underlie RAGE. Particularly, we discuss two groups of kinase inhibitors tyrphostins and the inhibitors of JAK kinases. Considering RAGE as a potential master regulator of processes that are crucial for the pathogenesis of psoriasis, we propose that these medicins may help in controlling the disease by abolishing the chronic inflammation in skin lesions.

Expanding the psoriasis disease profile: interrogation of the skin and serum of patients with moderate-to-severe psoriasis

Psoriasis is a complex disease with an expanding definition of its pathological features. We sought to expand/refine the psoriasis transcriptome using 85 paired lesional and non-lesional samples from a cohort of patients with moderate-to-severe psoriasis vulgaris who were not receiving active psoriasis therapy. This new analysis identified 4,175 probe sets (representing 2,725 unique known genes) as being differentially expressed in psoriasis lesions compared with matched biopsies of non-lesional skin when the following criteria were applied: >2-fold change and false discovery rate <0.05. These probe sets represent the largest and most comprehensive set of genes defining psoriasis at the molecular level and within the previously unidentified genes, a link to functional pathways associated with metabolic diseases/diabetes and to cardiovascular risk pathways is identified. In addition, we profiled the serum of moderate-to-severe psoriatics compared with healthy controls to assess the overlap of overexpressed lesional genes with overexpressed systemic proteins. We identified linkage of functional pathways in lesional skin associated with metabolic diseases/diabetes and cardiovascular risk with those pathways overexpressed in the serum, suggesting a potential linkage between altered gene transcription in the skin and comorbidities commonly seen in patients with moderate-to-severe psoriasis.

Expanding the psoriasis disease profile: interrogation of the skin and serum of patients with moderate-to-severe psoriasis

Psoriasis is a complex disease with an expanding definition of its pathological features. We sought to expand/refine the psoriasis transcriptome using 85 paired lesional and non-lesional samples from a cohort of patients with moderate-to-severe psoriasis vulgaris who were not receiving active psoriasis therapy. This new analysis identified 4,175 probe sets (representing 2,725 unique known genes) as being differentially expressed in psoriasis lesions compared with matched biopsies of non-lesional skin when the following criteria were applied: >2-fold change and false discovery rate <0.05. These probe sets represent the largest and most comprehensive set of genes defining psoriasis at the molecular level and within the previously unidentified genes, a link to functional pathways associated with metabolic diseases/diabetes and to cardiovascular risk pathways is identified. In addition, we profiled the serum of moderate-to-severe psoriatics compared with healthy controls to assess the overlap of overexpressed lesional genes with overexpressed systemic proteins. We identified linkage of functional pathways in lesional skin associated with metabolic diseases/diabetes and cardiovascular risk with those pathways overexpressed in the serum, suggesting a potential linkage between altered gene transcription in the skin and comorbidities commonly seen in patients with moderate-to-severe psoriasis.

Study of the genetic factors predisposing to the development of psoriasis

Background papers on psoriasis epidemiology, pathogenesis and genetics are presented. Special attention is given to genetic factors of the aptitude to psoriasis development. Were analysed researches, dedicated to the genome-wide screening of associations of polymorphic genetic locus with psoriasis development. Obtained results allow to reveal pathogenic psoriasis mechanisms, to forecast the character of the clinical course of the disease, as well as the efficiency of therapy and forecast the risk of psoriasis origination at patient’s relatives.

Gender-specific cytokine pathways, targets, and biomarkers for the switch from health to adenoma and colorectal cancer

Studies focusing on gender have shown that differences exist in how the immune system responds to disease and therapy. Understanding how gender influences immunological mechanisms in health and disease and identifying gender-specific biomarkers could lead to specifically tailored treatment and ultimately improve therapeutic success rates. T helper1 (Th1) and Th2 cytokines (Th1/Th2) have pivotal roles in the homeostasis of Th1 and Th2 cell network functions in the immune response but sex steroids affect Th1/Th2 production in different ways and a natural sexual dimorphism in the immune response has been shown. In order to investigate these differences further, we developed Th-cytokine data-driven models of the immune response and evaluated healthy subject peripheral blood samples. Independent cohorts of colorectal cancer and adenoma patients were also studied for comparison purposes. Our results show that the interferon (IFN)γ production pathway for immune response homeostasis is specific to men whilst the interleukin- (IL-) 6 production pathway for immune response homeostasis is specific to women. The IL-10 pathway for restoring immune system resting homeostasis was common to both but was controlled by the respective gender-specific pathways. These gender pathways could well be used as targets and biomarkers in translational research into developing new clinical strategies.

Eph/ephrin signaling in epidermal differentiation and disease

Eph receptor tyrosine kinases mediate cell-cell communication by interacting with ephrin ligands residing on adjacent cell surfaces. In doing so, these juxtamembrane signaling complexes provide important contextual information about the cellular microenvironment that helps orchestrate tissue morphogenesis and maintain homeostasis. Eph/ephrin signaling has been implicated in various aspects of mammalian skin physiology, with several members of this large family of receptor tyrosine kinases and their ligands present in the epidermis, hair follicles, sebaceous glands, and underlying dermis. This review focuses on the emerging role of Eph receptors and ephrins in epidermal keratinocytes where they can modulate proliferation, migration, differentiation, and death. The activation of Eph receptors by ephrins at sites of cell-cell contact also appears to play a key role in the maturation of intercellular junctional complexes as keratinocytes move out of the basal layer and differentiate in the suprabasal layers of this stratified, squamous epithelium. Furthermore, alterations in the epidermal Eph/ephrin axis have been associated with cutaneous malignancy, wound healing defects and inflammatory skin conditions. These collective observations suggest that the Eph/ephrin cell-cell communication pathway may be amenable to therapeutic intervention for the purpose of restoring epidermal tissue homeostasis and integrity in dermatological disorders.

Integrative network biology: graph prototyping for co-expression cancer networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure.

Interorgan coordination of the murine adaptive response to fasting

Starvation elicits a complex adaptive response in an organism. No information on transcriptional regulation of metabolic adaptations is available. We, therefore, studied the gene expression profiles of brain, small intestine, kidney, liver, and skeletal muscle in mice that were subjected to 0-72 h of fasting. Functional-category enrichment, text mining, and network analyses were employed to scrutinize the overall adaptation, aiming to identify responsive pathways, processes, and networks, and their regulation. The observed transcriptomics response did not follow the accepted "carbohydrate-lipid-protein" succession of expenditure of energy substrates. Instead, these processes were activated simultaneously in different organs during the entire period. The most prominent changes occurred in lipid and steroid metabolism, especially in the liver and kidney. They were accompanied by suppression of the immune response and cell turnover, particularly in the small intestine, and by increased proteolysis in the muscle. The brain was extremely well protected from the sequels of starvation. 60% of the identified overconnected transcription factors were organ-specific, 6% were common for 4 organs, with nuclear receptors as protagonists, accounting for almost 40% of all transcriptional regulators during fasting. The common transcription factors were PPARα, HNF4α, GCRα, AR (androgen receptor), SREBP1 and -2, FOXOs, EGR1, c-JUN, c-MYC, SP1, YY1, and ETS1. Our data strongly suggest that the control of metabolism in four metabolically active organs is exerted by transcription factors that are activated by nutrient signals and serves, at least partly, to prevent irreversible brain damage.