Association of Key Genes and Pathways with Atopic Dermatitis by Bioinformatics Analysis

Single-cell RNA sequencing reveals 2D cytokine assay can model atopic dermatitis more accurately than immune-competent 3D setup

Modelling atopic dermatitis (AD) in vitro is paramount to understand the disease pathophysiology and identify novel treatments. Previous studies have shown that the Th2 cytokines IL-4 and IL-13 induce AD-like features in keratinocytes in vitro. However, it has not been systematically researched whether the addition of Th2 cells, their supernatants or a 3D structure is superior to model AD compared to simple 2D cell culture with cytokines. For the first time, we investigated what in vitro option most closely resembles the disease in vivo based on single-cell RNA sequencing data (scRNA-seq) obtained from skin biopsies in a clinical study and published datasets of healthy and AD donors. In vitro models were generated with primary fibroblasts and keratinocytes, subjected to cytokine treatment or Th2 cell cocultures in 2D/3D. Gene expression changes were assessed using qPCR and Multiplex Immunoassays. Of all cytokines tested, incubation of keratinocytes and fibroblasts with IL-4 and IL-13 induced the closest in vivo-like AD phenotype which was observed in the scRNA-seq data. Addition of Th2 cells to fibroblasts failed to model AD due to the downregulation of ECM-associated genes such as POSTN. While keratinocytes cultured in 3D showed better stratification than in 2D, changes induced with AD triggers did not better resemble AD keratinocyte subtypes observed in vivo. Taken together, our comprehensive study shows that the simple model using IL-4 or IL-13 in 2D most accurately models AD in fibroblasts and keratinocytes in vitro, which may aid the discovery of novel treatment options.

Toward Precision Medicine in Atopic Dermatitis Using Molecular-Based Approaches

Atopic dermatitis is the most common chronic inflammatory skin disorder, affecting up to 20% of children and 10% of adults in developed countries. The pathophysiology of atopic dermatitis is complex and involves a strong genetic predisposition and T-cell driven inflammation. Although our understanding of the pathology and drivers of this disease has improved in recent years, there are still knowledge gaps in the immune pathways involved. Therefore, advances in new omics technologies in atopic dermatitis will play a key role in understanding the pathogenesis of this burden disease and could develop preventive strategies and personalized treatment strategies. In this review, we discuss the latest developments in genetics, transcriptomics, epigenomics, proteomics, and metagenomics and understand how integrating multiple omics datasets will identify potential biomarkers and uncover nets of associations between several molecular levels.

Toward Precision Medicine in Atopic Dermatitis Using Molecular-Based Approaches

Atopic dermatitis is the most common chronic inflammatory skin disorder, affecting up to 20% of children and 10% of adults in developed countries. The pathophysiology of atopic dermatitis is complex and involves a strong genetic predisposition and T-cell driven inflammation. Although our understanding of the pathology and drivers of this disease has improved in recent years, there are still knowledge gaps in the immune pathways involved. Therefore, advances in new omics technologies in atopic dermatitis will play a key role in understanding the pathogenesis of this burden disease and could develop preventive strategies and personalized treatment strategies. In this review, we discuss the latest developments in genetics, transcriptomics, epigenomics, proteomics, and metagenomics and understand how integrating multiple omics datasets will identify potential biomarkers and uncover nets of associations between several molecular levels.

Genome-Wide Differential Transcription of Long Noncoding RNAs in Psoriatic Skin

Long noncoding RNAs (lncRNAs) may contribute to the formation of psoriatic lesions. The present study's objective was to identify long lncRNA genes that are differentially expressed in patient samples of psoriasis through computational analysis techniques. By using previously published RNA sequencing data from psoriatic and healthy patients (n = 324), we analysed the differential expression of lncRNAs to determine transcripts of heightened expression. We computationally screened lncRNA transcripts as annotated by GENCODE across the human genome and compared transcription in psoriatic and healthy samples from two separate studies. We observed 54 differentially expressed genes as seen in two independent datasets collected from psoriasis and healthy patients. We also identified the differential expression of LINC01215 and LINC1206 associated with the cell cycle pathway and psoriasis pathogenesis. SH3PXD2A-AS1 was identified as a participant in the STAT3/SH3PXD2A-AS1/miR-125b/STAT3 positive feedback loop. Both the SH3PXD2A-AS1 and CERNA2 genes have already been recognised as part of the IFN-γ signalling pathway regulation. Additionally, EPHA1-AS1, CYP4Z2P and SNHG12 gene upregulation have all been previously linked to inflammatory skin diseases. Differential expression of various lncRNAs affects the pathogenesis of psoriasis. Further characterisation of lncRNAs and their functions are important for developing our understanding of psoriasis.

Type 2 Inflammation Contributes to Skin Barrier Dysfunction in Atopic Dermatitis

Skin barrier dysfunction, a defining feature of atopic dermatitis (AD), arises from multiple interacting systems. In AD, skin inflammation is caused by host-environment interactions involving keratinocytes as well as tissue-resident immune cells such as type 2 innate lymphoid cells, basophils, mast cells, and T helper type 2 cells, which produce type 2 cytokines, including IL-4, IL-5, IL-13, and IL-31. Type 2 inflammation broadly impacts the expression of genes relevant for barrier function, such as intracellular structural proteins, extracellular lipids, and junctional proteins, and enhances Staphylococcus aureus skin colonization. Systemic anti‒type 2 inflammation therapies may improve dysfunctional skin barrier in AD.

Epidermal PPARγ Is a Key Homeostatic Regulator of Cutaneous Inflammation and Barrier Function in Mouse Skin

Both agonist studies and loss-of-function models indicate that PPARγ plays an important role in cutaneous biology. Since PPARγ has a high level of basal activity, we hypothesized that epidermal PPARγ would regulate normal homeostatic processes within the epidermis. In this current study, we performed mRNA sequencing and differential expression analysis of epidermal scrapings from knockout mice and wildtype littermates. Pparg-/-^epi mice exhibited a 1.5-fold or greater change in the expression of 11.8% of 14,482 identified transcripts. Up-regulated transcripts included those for a large number of cytokines/chemokines and their receptors, as well as genes associated with inflammasome activation and keratinization. Several of the most dramatically up-regulated pro-inflammatory genes in Pparg-/-^epi mouse skin included Igfl3, 2610528A11Rik, and Il1f6. RT-PCR was performed from RNA obtained from non-lesional full-thickness skin and verified a marked increase in these transcripts, as well as transcripts for Igflr1, which encodes the receptor for Igfl3, and the 2610528A11Rik receptor (Gpr15). Transcripts for Il4 were detected in Pparg-/-^epi mouse skin, but transcripts for Il17 and Il22 were not detected. Down-regulated transcripts included sebaceous gland markers and a number of genes associated with lipid barrier formation. The change in these transcripts correlates with an asebia phenotype, increased transepidermal water loss, alopecia, dandruff, and the appearance of spontaneous inflammatory skin lesions. Histologically, non-lesional skin showed hyperkeratosis, while inflammatory lesions were characterized by dermal inflammation and epidermal acanthosis, spongiosis, and parakeratosis. In conclusion, loss of epidermal Pparg alters a substantial set of genes that are associated with cutaneous inflammation, keratinization, and sebaceous gland function. The data indicate that epidermal PPARγ plays an important role in homeostatic epidermal function, particularly epidermal differentiation, barrier function, sebaceous gland development and function, and inflammatory signaling.

The stratum corneum transcriptome in atopic dermatitis can be assessed by tape stripping

BACKGROUND

Skin biopsies represent a gold standard in skin immunology and pathology but can cause pain and induce scarring. Non-invasive techniques will facilitate study recruitment of e.g. patients with paediatric atopic dermatitis (AD), hand eczema or facial dermatitis.

OBJECTIVE

By RNA sequencing, we examined whether the stratum corneum transcriptome in AD skin can be assessed by tape stripping, as compared to the epidermal transcriptome of AD in skin biopsies. To make the procedure clinically relevant tape strips were stored and shipped at room temperature for up to 3 days.

METHODS

Nine adult Caucasian AD patients and three healthy volunteers were included. Tape samples were collected from non-lesional and lesional skin. Biopsies were collected from lesional skin and were split into epidermis and dermis. Total RNA was extracted, and shotgun sequencing was performed.

RESULTS

Shotgun sequencing could be performed on skin cells obtained from two consecutive tape strips which had been stored and shipped at room temperature for up to three days. The most prominent differences between the tape strip and biopsy derived transcriptome were due to structural genes, while established molecular markers of AD, including CCL17, CCL22, IL17A and S100A7-S100A9, were also identified in tape strip samples. Furthermore, the tape strip derived transcriptome showed promise in also analysing the skin microbiome.

CONCLUSION

Our study shows that the stratum corneum (SC) transcriptome of AD can be assessed by tape stripping the skin, supporting that this method may be central in future skin biomarker research. NCBI GEO data accession: GSE160501.

Integrated bioinformatics analysis identifies microRNA-376a-3p as a new microRNA biomarker in patient with coronary artery disease

INTRODUCTION

Coronary artery disease (CAD) is a major global health problem with high incidence and mortality. Despite many advances in treatment, the prognosis of patients with CAD still remains poor. Therefore, this study aimed to identify potential biomarkers and targets associated with the progression of CAD.

METHODS

Two gene expression profile datasets (GSE20681 and GSE12288), and two microRNA (miRNA) expression profile datasets (GSE59421 and GSE105449) were downloaded from the Gene Expression Omnibus (GEO) database; R language was used to screen out the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs), respectively. In addition, five online bioinformatics tools (miRWalk et al.) were used to identify the target genes of DEMs, and miRNA-gene network was constructed using Cytoscape software. Moreover, CCK-8, flow cytometry assays were used to detect the cell proliferation and apoptosis in human umbilical vein endothelial cells (HUVECs). Meanwhile, the dual luciferase reporter system assay was used to explore the interaction of miR-376a-3p and NRIP1 in HUVECs.

RESULTS

In the present study, 150 common DEGs and 5 common DEMs were screened using a Venn diagram in R language. First, a total of 6812 target genes were identified from the overlapping DEMs. Second, 26 overlapping dysregulated genes from 150 overlapping DEGs and 6812 miRNA target genes were identified. Meanwhile, 43 miRNA-gene regulatory pairs were obtained between the 5 common DEMs and 26 dysregulated genes. Downregulation of miR-376a-3p significantly inhibited the proliferation of HUVECs via inducing apoptosis. Moreover, overexpression of miR-376a-3p markedly inhibited the growth of HUVECs via downregulating NRIP1.

CONCLUSION

In this study, miR-376a-3p-NRIP1 pair might involve in the progression of CAD, implying that miR-376a-3p may be a therapeutic target for the treatment of CAD.

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.

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.