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      Inflammatory signature in acute-on-chronic liver failure includes increased expression of granulocyte genes ELANE, MPO and CD177

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          Abstract

          Acute-on-Chronic Liver Failure (ACLF) is associated with innate immune dysfunction and high short-term mortality. Neutrophils have been identified to influence prognosis in ACLF. Neutrophil biology is under-evaluated in ACLF. Therefore, we investigated neutrophil-specific genes and their association with ACLF outcomes. This is an observational study. Enriched granulocytes, containing neutrophils, isolated from study participants in three groups- ACLF(n = 10), chronic liver disease (CLD, n = 4) and healthy controls (HC, n = 4), were analysed by microarray. Differentially expressed genes were identified and validated by qRT-PCR in an independent cohort of ACLF, CLD and HC (n = 30, 15 and 15 respectively). The association of confirmed overexpressed genes with ACLF 28-day non-survivors was investigated. The protein expression of selected neutrophil genes was confirmed using flow cytometry and IHC. Differential gene expression analysis showed 1140 downregulated and 928 upregulated genes for ACLF versus CLD and 2086 downregulated and 1091 upregulated genes for ACLF versus HC. Significant upregulation of neutrophilic inflammatory signatures were found in ACLF compared to CLD and HC. Neutrophil enriched genes ELANE, MPO and CD177 were highly upregulated in ACLF and their expression was higher in ACLF 28-day non-survivors. Elevated expression of CD177 protein on neutrophil surface in ACLF was confirmed by flow cytometry. IHC analysis in archival post mortem liver biopsies showed the presence of CD177 + neutrophils in the liver tissue of ACLF patients. Granulocyte genes ELANE, MPO and CD177 are highly overexpressed in ACLF neutrophils as compared to CLD or HC. Further, this three-gene signature is highly overexpressed in ACLF 28-day non-survivors.

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          Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

          Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.
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            ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

            Summary: We have developed ClueGO, an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. It can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms. A one-click update option allows ClueGO to automatically download the most recent GO/KEGG release at any time. ClueGO provides an intuitive representation of the analysis results and can be optionally used in conjunction with the GOlorize plug-in. Availability: http://www.ici.upmc.fr/cluego/cluegoDownload.shtml Contact: jerome.galon@crc.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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              Gene Set Knowledge Discovery with Enrichr

              Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2: Searching Enrichr by a single gene or key search term Basic Protocol 3: Preparing raw or processed RNA-seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4: Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5: Using Enrichr in Geneshot Basic Protocol 6: Using Enrichr in ARCHS4 Basic Protocol 7: Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8: Using the Enrichr API Basic Protocol 9: Adding an Enrichr button to a website.
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                Author and article information

                Contributors
                pragyan.acharya@aiims.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 September 2021
                22 September 2021
                2021
                : 11
                : 18849
                Affiliations
                [1 ]GRID grid.413618.9, ISNI 0000 0004 1767 6103, Department of Biochemistry, , All India Institute of Medical Sciences, ; Lab 3002, 3rd floor Teaching Block, New Delhi, 110029 India
                [2 ]GRID grid.444651.6, ISNI 0000 0004 0496 6988, Disease Biology Lab, Department of Biosciences, , Sri Sathya Sai Institute of Higher Learning, ; Puttaparthi, Andhra Pradesh India
                [3 ]GRID grid.413618.9, ISNI 0000 0004 1767 6103, Department of Gastroenterology, , All India Institute of Medical Sciences, ; New Delhi, India
                [4 ]GRID grid.413618.9, ISNI 0000 0004 1767 6103, Department of Pathology, , All India Institute of Medical Sciences, ; New Delhi, India
                Article
                98086
                10.1038/s41598-021-98086-6
                8458283
                34552111
                87c98905-d900-47d0-bdd5-694a31365849
                © 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
                : 18 May 2021
                : 3 September 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001407, Department of Biotechnology, Ministry of Science and Technology, India;
                Award ID: BT/PR18574/BIC/101/151
                Award Recipient :
                Funded by: All-India Institute of Medical Sciences New Delhi Intramural Grant
                Funded by: FundRef http://dx.doi.org/10.13039/501100001411, Indian Council of Medical Research;
                Award ID: Fellowship no.JRF-2016/HRD/LS/98/40800
                Award Recipient :
                Categories
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                © The Author(s) 2021

                Uncategorized
                biochemistry,immunology
                Uncategorized
                biochemistry, immunology

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