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      Key Genes Identified in Nonsyndromic Microtia by the Analysis of Transcriptomics and Proteomics

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          Abstract

          As one of the common birth defects worldwide, nonsyndromic microtia is a complex disease that results from interactions between environmental and genetic factors. However, the underlying causes of nonsyndromic microtia are currently not well understood. The present study determined transcriptomic and proteomic profiles of auricular cartilage tissues in 10 patients with third-degree nonsyndromic microtia and five control subjects by RNA microarray and tandem mass tag-based quantitative proteomics technology. Relative mRNA and protein abundances were compared and evaluated for their function and putative involvement in nonsyndromic microtia. A total of 3971 differentially expressed genes and 256 differentially expressed proteins were identified. Bioinformatics analysis demonstrated that some of these genes and proteins showed potential associations with nonsyndromic microtia. Thirteen proteins with the same trend at the mRNA level obtained by the integrated analysis were validated by parallel reaction monitoring analysis. Several key genes, namely, LAMB2 , COMP, APOA2, APOC2, APOC3, and A2 M, were found to be dysregulated, which could contribute to nonsyndromic microtia. The present study is the first report on the transcriptomic and proteomic integrated analysis of nonsyndromic microtia using the same auricular cartilage sample. Additional studies are required to clarify the roles of potential key genes in nonsyndromic microtia.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              STRING v10: protein–protein interaction networks, integrated over the tree of life

              The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                13 May 2022
                24 May 2022
                : 7
                : 20
                : 16917-16927
                Affiliations
                []ENT institute, Eye & ENT Hospital, Fudan University , Shanghai 200031, China
                []Department of Facial Plastic and Reconstructive Surgery, Eye & ENT Hospital, Fudan University , Shanghai 200031, China
                [§ ]NHC Key Laboratory of Hearing Medicine, Fudan University , Shanghai 200031, China
                []Key Laboratory of Metabolism and Molecular Medicine, Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University , Shanghai 200032, China
                []Department of Bioinformatics, Medical Laboratory of Nantong Zhongke , Nantong, Jiangsu 226133, China
                Author notes
                [* ]Email: duanma@ 123456fudan.edu.cn . Tel: 021-54237441.
                [* ]Email: mj19815208@ 123456yeah.net . Tel: 021-64377134.
                [* ]Email: ty_zhang2021@ 123456163.com . Tel: 021-64377134.
                Author information
                https://orcid.org/0000-0002-4041-9960
                Article
                10.1021/acsomega.1c07059
                9134388
                35647449
                799e2c4a-2650-4cd8-b5ed-80cb43a44197
                © 2022 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 14 December 2021
                : 05 May 2022
                Funding
                Funded by: Natural Science Foundation of Shanghai, doi 10.13039/100007219;
                Award ID: 20ZR1409900
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 81800920
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 81771014
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                Custom metadata
                ao1c07059
                ao1c07059

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