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      Lipidomics profiling reveals the role of glycerophospholipid metabolism in psoriasis

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          Abstract

          Psoriasis is a common and chronic inflammatory skin disease that is complicated by gene–environment interactions. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of psoriasis, the role of metabolites in psoriasis, particularly of lipids, remains unclear. Lipids not only comprise the bulk of the cellular membrane bilayers but also regulate a variety of biological processes such as cell proliferation, apoptosis, immunity, angiogenesis, and inflammation. In this study, an untargeted lipidomics approach was used to study the lipid profiles in psoriasis and to identify lipid metabolite signatures for psoriasis through ultra-performance liquid chromatography-tandem quadrupole mass spectrometry. Plasma samples from 90 participants (45 healthy and 45 psoriasis patients) were collected and analyzed. Statistical analysis was applied to find different metabolites between the disease and healthy groups. In addition, enzyme-linked immunosorbent assay was performed to validate differentially expressed lipids in psoriatic patient plasma. Finally, we identified differential expression of several lipids including lysophosphatidic acid (LPA), lysophosphatidylcholine (LysoPC), phosphatidylinositol (PI), phosphatidylcholine (PC), and phosphatidic acid (PA); among these metabolites, LPA, LysoPC, and PA were significantly increased, while PC and PI were down-regulated in psoriasis patients. We found that elements of glycerophospholipid metabolism such as LPA, LysoPC, PA, PI, and PC were significantly altered in the plasma of psoriatic patients; this study characterizes the circulating lipids in psoriatic patients and provides novel insight into the role of lipids in psoriasis.

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          Most cited references 51

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          LMSD: LIPID MAPS structure database

          The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available at
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            Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

            The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
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              The IL-23/T17 pathogenic axis in psoriasis is amplified by keratinocyte responses.

              Psoriasis is a complex inflammatory process resulting from activation of the well-defined interleukin (IL)-23/T17 cytokine axis. We review the role of key cytokines IL-17 and IL-23 in psoriasis, as well as tumor necrosis factor (TNF)α, focusing on therapeutic cytokine interventions and what they reveal about psoriatic inflammation. The potential role of recently described epidermal IL-36RN and CARD14 genetic mutations in psoriasis pathogenesis is also explored, because they augment keratinocyte responses to proinflammatory cytokines. The discovery of these genetic mutations in familial and pustular psoriasis suggests new links between cytokine-induced gene products and IL-1 family members from keratinocytes, which may regulate features of the disease, including epidermal hyperplasia and neutrophil infiltrating responses. Copyright © 2012. Published by Elsevier Ltd.
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                Author and article information

                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                October 2017
                05 September 2017
                05 September 2017
                : 6
                : 10
                : 1-11
                Affiliations
                [1 ]Department of Dermatology, Xiangya Hospital, Central South University, Xiangya Road #87 Changsha, Hunan, China, 410008
                [2 ]BGI-Shenzhen, Beishan Industrial Zone, Yantian District, Shenzhen, China, 518083
                [3 ]Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Xiangya Road #87 Changsha, Hunan, China, 410008
                [4 ]China National GeneBank-Shenzhen, Jinsha Road, Dapeng District, Shenzhen, China, 518083
                Author notes
                [* ]Correspondence address. Cong Peng, MD, PhD, Department of Dermatology, Xiangya Hospital, Central South University, Xiangya Road #87, Changsha, Hunan, China, 410008. Tel: +86-731-84327377; Fax: +86-731-84328478; E-mail: pengcongxy@ 123456csu.edu.cn ; Xiang Chen, MD, PhD, Department of Dermatology, Xiangya Hospital, Central South University, Xiangya Road #87, Changsha, Hunan, China, 410008. Tel: +86-731-84327377; Fax: +86-731-84328478; E-mail: chenxiangck@ 123456126.com
                []Equal contribution
                Article
                gix087
                10.1093/gigascience/gix087
                5647792
                29046044
                © The Author 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 11
                Product
                Funding
                Funded by: National Science Foundation
                Award ID: 81572679
                Funded by: National Natural Science Foundation
                Award ID: 2015JJ2161
                Categories
                Research

                psoriasis, metabolomics, lipidomics, glycerophospholipid, ms/ms

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