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      Tear Proteomics in Keratoconus: A Quantitative SWATH-MS Analysis


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          To elucidate dysregulated proteins in keratoconus (KC) to provide a better understanding of the molecular mechanisms that lead to the development of the disease using sequential window acquisition of all theoretical mass spectra (SWATH-MS) as a protein quantification tool of the tear proteomic profile.


          Prospective cross-sectional study that includes 25 keratoconic eyes and 25 healthy eyes. All participants underwent a clinical, tomographic, and aberrometric exam. Tear sample was collected using Schirmer strips and analyzed by liquid chromatography with tandem mass spectrometry. SWATH-MS was used as a quantification tool of the tear proteomic profile. The expression of the quantified proteins was compared between groups, and the biological and molecular functions of the dysregulated proteins as well as their functional relationships were studied by in silico analysis.


          A total of 203 proteins were quantified in tear samples of patients with KC and control participants, of which 18 showed differential expression between groups ( P < 0.05). An increase in the expression of 7 proteins and a decrease in the expression of 11 proteins were observed. Protein–protein interactions and gene ontology analysis showed the involvement of these dysregulated proteins in structural, inflammatory-immune, iron homeostasis, oxidative stress, and extracellular matrix proteolysis processes.


          Tear protein quantification has revealed the dysregulation of proteins involved in biological processes previously associated with KC. Among them, iron homeostasis should be highlighted as a relevant pathway in the KC pathophysiology, and it should be taken into account in the development of therapeutic targets to cope with tissue damage derived from iron accumulation and toxicity.

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          Most cited references63

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          Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

          Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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            The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

            A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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              FunRich: An open access standalone functional enrichment and interaction network analysis tool.

              As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich (http://www.funrich.org) is user-friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).

                Author and article information

                Invest Ophthalmol Vis Sci
                Invest Ophthalmol Vis Sci
                Investigative Ophthalmology & Visual Science
                The Association for Research in Vision and Ophthalmology
                25 August 2021
                August 2021
                : 62
                : 10
                : 30
                [1 ]Department of Surgery and Medical-Surgical Specialties. Faculty of Optics and Optometry, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
                [2 ]Clinical Neurosciences Research Laboratory, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
                [3 ]Proteomic Unit, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
                [4 ]Department of Pharmacology, Pharmacy and Pharmaceutical Technology. Universidade de Santiago de Compostela (USC), Campus Vida, Santiago de Compostela, Spain
                [5 ]Instituto Galego de Oftalmoloxía (INGO), Hospital Provincial de Conxo, Santiago de Compostela, Spain
                Author notes
                Correspondence: Isabel Lema, Instituto Galego de Oftalmoloxía, Hospital Provincial de Conxo, Compostela 15706—Santiago de Compostela (Spain); mariaisabel.lema@ 123456usc.es.
                Pablo Hervella, Clinical Neurosciences Research Laboratory, Health Research Institute of Santiago de Compostela, 15706—Santiago de Compostela (Spain); pablo.hervella.lorenzo@ 123456sergas.es.

                MLL and UR have equally contributed to this work.

                Copyright 2021 The Authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                : 02 August 2021
                : 05 May 2021
                Page count
                Pages: 10

                keratoconus,tear fluid,proteomics,mass spectrometry,swath-ms


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