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Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

ABSTRACT: The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease as well as common cellular and molecular pathways. Disease-specific signatures were leveraged to build a multi-disease classifier, which predicted the diagnosis of publicly and prospectively collected expression profiles with 93% accuracy. In one sample, the molecular classifier differed from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinical presentation evolved. Finally, integration of IFN-regulated gene programs with the skin database revealed a significant inverse correlation between IFN-β and IFN-γ programs across all conditions. Our study provides an integrative approach to the study of gene signatures from multiple skin conditions, elucidating mechanisms of disease pathogenesis. In addition, these studies provide a framework for developing tools for personalized medicine toward the precise prediction, prevention, and treatment of disease on an individual level.

SUBMITTER: Inkeles MS

PROVIDER: S-EPMC4268388 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

Inkeles Megan S MS Scumpia Philip O PO Swindell William R WR Lopez David D Teles Rosane M B RMB Graeber Thomas G TG Meller Stephan S Homey Bernhard B Elder James T JT Gilliet Michel M Modlin Robert L RL Pellegrini Matteo M

The Journal of investigative dermatology 20140811 1

The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease as well as common cellular and molecular pathways. Disease-specific signatures w ...[more]

PMID: 25111617

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Project description:BackgroundRadiation-induced skin injury (RISI) commonly manifests in cancer patients undergoing radiotherapy (RT). However, a universally accepted standard for treating radiation injury has not yet been established. Our objective was to provide a detailed molecular overview of skin pre- and post-radiation therapy, aiming to enhance our understanding of the subclusters and molecular mechanisms contributing to radiodermatitis.MethodsC57BL/6 mice were subjected to a single fraction (20 Gy) of RT targeting the right dorsal skin. We then employed integrated single-cell RNA sequencing (scRNA-seq) to analyze skin samples from mice at 7 and 30 days after radiation exposure, as well as from non-irradiated mice. The Seurat analysis pipeline, Cellchat, SCP, and ssGSEA were used to define the cell types and mechanisms involved in radiation-induced skin injury. Reverse transcription polymerase chain reaction (RT-PCR), multiplex immunofluorescent staining, and other datasets (GSE130183, GSE193564, and GSE193807) were used to validate our findings.ResultsThirty-two distinct cell clusters encompassing 71,412 cells were identified. We discovered that cycling keratinocytes (KCs), with the BMP signaling pathway enriched, could activate the Wnt pathway, as well as the SMAD pathways, driving the wound healing and fibrosis processes in RISI. Terminally differentiated secretory-papillary fibroblasts (Fibs) are capable of attracting immune cells, which contributes to the pathogenesis of RISI. Lymphatic endothelial cells (ECs) with pro-inflammatory properties play a critical role in the pathogenesis of RISI by facilitating leukocyte migration. Our analysis also highlighted enhanced ligand-receptor interactions, notably the interactions between chemokines like CXCL10, CCL2, and ACKR1, across subclusters of inflammatory KCs, Fibs, ECs, and immune cells, underscoring their pivotal role in leukocyte recruitment in RISI.ConclusionsCycling KCs, secretory-papillary Fibs, and lymphatic ECs play critical roles in RISI progression. Targeting the interactions of these subclusters with immune cells might help improve the severity of RISI. Furthermore, our study provides a valuable resource for understanding the interactions among immune cells in the context of RISI.

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Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

Publications

Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

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