Mayte Suárez-Fariñas1, Benjamin Ungar2, Joel Correa da Rosa1, David A Ewald3, Mariya Rozenblit2, Juana Gonzalez1, Hui Xu2, Xiuzhong Zheng1, Xiangyu Peng2, Yeriel D Estrada2, Stacey R Dillon4, James G Krueger1, Emma Guttman-Yassky5. 1. Laboratory for Investigative Dermatology, Rockefeller University, New York, NY. 2. Laboratory for Investigative Dermatology, Rockefeller University, New York, NY; Department of Dermatology and the Laboratory for Inflammatory Skin Diseases, Icahn School of Medicine at Mount Sinai, New York, NY. 3. Laboratory for Investigative Dermatology, Rockefeller University, New York, NY; Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark. 4. ZymoGenetics (a Bristol-Myers Squibb Company), Seattle, Wash. 5. Laboratory for Investigative Dermatology, Rockefeller University, New York, NY; Department of Dermatology and the Laboratory for Inflammatory Skin Diseases, Icahn School of Medicine at Mount Sinai, New York, NY. Electronic address: Emma.Guttman@mountsinai.org.
Abstract
BACKGROUND: Genomic profiling of lesional and nonlesional skin of patients with atopic dermatitis (AD) using microarrays has led to increased understanding of AD and identification of novel therapeutic targets. However, the limitations of microarrays might decrease detection of AD genes. These limitations might be lessened with next-generation RNA sequencing (RNA-seq). OBJECTIVE: We sought to define the lesional AD transcriptome using RNA-seq and compare it using microarrays performed on the same cohort. METHODS: RNA-seq and microarrays were performed to identify differentially expressed genes (criteria: fold change, ≥ 2.0; false discovery rate ≤ 0.05) in lesional versus nonlesional skin from 18 patients with moderate-to-severe AD, with real-time PCR (RT-PCR) and immunohistochemistry used for validation. RESULTS: Both platforms showed robust disease transcriptomes and correlated well with RT-PCR. The common AD transcriptome identified by using both techniques contained 217 genes, including inflammatory (S100A8/A9/A12, CXCL1, and 2'-5'-oligoadenylate synthetase-like [OASL]) and barrier (MKi67, keratin 16 [K16], and claudin 8 [CLDN8]) AD-related genes. Although fold change estimates determined by using RNA-seq showed somewhat better agreement with RT-PCR (intraclass correlation coefficient, 0.57 and 0.70 for microarrays and RNA-seq vs RT-PCR, respectively), bias was not eliminated. Among genes uniquely identified by using RNA-seq were triggering receptor expressed on myeloid cells 1 (TREM-1) signaling (eg, CCL2, CCL3, and single immunoglobulin domain IL1R1 related [SIGIRR]) and IL-36 isoform genes. TREM-1 is a surface receptor implicated in innate and adaptive immunity that amplifies infection-related inflammation. CONCLUSIONS: This is the first report of a lesional AD phenotype using RNA-seq and the first direct comparison between platforms in this disease. Both platforms robustly characterize the AD transcriptome. Through RNA-seq, we unraveled novel disease pathology, including increased expression of the novel TREM-1 pathway and the IL-36 cytokine in patients with AD.
BACKGROUND: Genomic profiling of lesional and nonlesional skin of patients with atopic dermatitis (AD) using microarrays has led to increased understanding of AD and identification of novel therapeutic targets. However, the limitations of microarrays might decrease detection of AD genes. These limitations might be lessened with next-generation RNA sequencing (RNA-seq). OBJECTIVE: We sought to define the lesional AD transcriptome using RNA-seq and compare it using microarrays performed on the same cohort. METHODS: RNA-seq and microarrays were performed to identify differentially expressed genes (criteria: fold change, ≥ 2.0; false discovery rate ≤ 0.05) in lesional versus nonlesional skin from 18 patients with moderate-to-severe AD, with real-time PCR (RT-PCR) and immunohistochemistry used for validation. RESULTS: Both platforms showed robust disease transcriptomes and correlated well with RT-PCR. The common AD transcriptome identified by using both techniques contained 217 genes, including inflammatory (S100A8/A9/A12, CXCL1, and 2'-5'-oligoadenylate synthetase-like [OASL]) and barrier (MKi67, keratin 16 [K16], and claudin 8 [CLDN8]) AD-related genes. Although fold change estimates determined by using RNA-seq showed somewhat better agreement with RT-PCR (intraclass correlation coefficient, 0.57 and 0.70 for microarrays and RNA-seq vs RT-PCR, respectively), bias was not eliminated. Among genes uniquely identified by using RNA-seq were triggering receptor expressed on myeloid cells 1 (TREM-1) signaling (eg, CCL2, CCL3, and single immunoglobulin domain IL1R1 related [SIGIRR]) and IL-36 isoform genes. TREM-1 is a surface receptor implicated in innate and adaptive immunity that amplifies infection-related inflammation. CONCLUSIONS: This is the first report of a lesional AD phenotype using RNA-seq and the first direct comparison between platforms in this disease. Both platforms robustly characterize the AD transcriptome. Through RNA-seq, we unraveled novel disease pathology, including increased expression of the novel TREM-1 pathway and the IL-36 cytokine in patients with AD.
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Authors: Garrett J Patrick; Haiyun Liu; Martin P Alphonse; Dustin A Dikeman; Christine Youn; Jack C Otterson; Yu Wang; Advaitaa Ravipati; Momina Mazhar; George Denny; Roger V Ortines; Emily Zhang; Robert J Miller; Carly A Dillen; Qi Liu; Sabrina J Nolan; Kristine Nguyen; LeeAnn Marcello; Danh C Do; Eric M Wier; Yan Zhang; Gary Caviness; Alexander C Klimowicz; Diane V Mierz; Jay S Fine; Guangping Sun; Raphaela Goldbach-Mansky; Alina I Marusina; Alexander A Merleev; Emanual Maverakis; Luis A Garza; Joshua D Milner; Peisong Gao; Meera Ramanujam; Ernest L Raymond; Nathan K Archer; Lloyd S Miller Journal: J Clin Invest Date: 2021-03-01 Impact factor: 14.808
Authors: Amy C Foulkes; David S Watson; Daniel F Carr; John G Kenny; Timothy Slidel; Richard Parslew; Munir Pirmohamed; Simon Anders; Nick J Reynolds; Christopher E M Griffiths; Richard B Warren; Michael R Barnes Journal: J Invest Dermatol Date: 2018-07-17 Impact factor: 8.551