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      hsp-90 and unc-45 depletion induce characteristic transcriptional signatures in coexpression cliques of C. elegans

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          Abstract

          Nematode development is characterized by progression through several larval stages. Thousands of genes were found in large scale RNAi-experiments to block this development at certain steps, two of which target the molecular chaperone HSP-90 and its cofactor UNC-45. Aiming to define the cause of arrest, we here investigate the status of nematodes after treatment with RNAi against hsp-90 and unc-45 by employing an in-depth transcriptional analysis of the arrested larvae. To identify misregulated transcriptional units, we calculate and validate genome-wide coexpression cliques covering the entire nematode genome. We define 307 coexpression cliques and more than half of these can be related to organismal functions by GO-term enrichment, phenotype enrichment or tissue enrichment analysis. Importantly, hsp-90 and unc-45 RNAi induce or repress many of these cliques in a coordinated manner, and then several specifically regulated cliques are observed. To map the developmental state of the arrested nematodes we define the expression behaviour of each of the cliques during development from embryo to adult nematode. hsp-90 RNAi can be seen to arrest development close to the L4 larval stage with further deviations in daf-16 regulated genes. unc-45 RNAi instead leads to arrested development at young adult stage prior to the programmatic downregulation of sperm-cell specific genes. In both cases processes can be defined to be misregulated upon depletion of the respective chaperone. With most of the defined gene cliques showing concerted behaviour at some stage of development from embryo to late adult, the “clique map” together with the clique-specific GO-terms, tissue and phenotype assignments will be a valuable tool in understanding concerted responses on the genome-wide level in Caenorhabditis elegans.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

              R. Edgar (2002)
              The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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                Author and article information

                Contributors
                klaus.richter@richterlab.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 June 2021
                18 June 2021
                2021
                : 11
                : 12852
                Affiliations
                GRID grid.6936.a, ISNI 0000000123222966, Center for integrated protein research at the Department of Chemistry, , Technische Universität München, ; Lichtenbergstr. 4, 85748 Garching, Germany
                Article
                91690
                10.1038/s41598-021-91690-6
                8213770
                34145311
                d97c3d6d-20ae-4947-961c-11081bfc8d92
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 January 2021
                : 21 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: RI1873/1-4
                Award ID: RI1873/5-1
                Award Recipient :
                Funded by: Technische Universität München (1025)
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                functional clustering,gene regulatory networks,microarrays,germline development,rnai
                Uncategorized
                functional clustering, gene regulatory networks, microarrays, germline development, rnai

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