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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 references51

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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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            metaX: a flexible and comprehensive software for processing metabolomics data

            Background Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. Results We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface (http://metax.genomics.cn) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/. Conclusions The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1579-y) contains supplementary material, which is available to authorized users.
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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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                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
                24db43d3-ffbf-4d84-8d92-16d7843f63d0
                © 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.

                History
                : 27 August 2017
                : 08 May 2017
                : 08 July 2017
                Page count
                Pages: 11
                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
                psoriasis, metabolomics, lipidomics, glycerophospholipid, ms/ms

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