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      Altering heparan sulfate suppresses cell abnormalities and neuron loss in Drosophila presenilin model of Alzheimer Disease

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          Summary

          We examined the function of heparan-sulfate-modified proteoglycans (HSPGs) in pathways affecting Alzheimer disease (AD)-related cell pathology in human cell lines and mouse astrocytes. Mechanisms of HSPG influences on presenilin-dependent cell loss were evaluated in Drosophila using knockdown of the presenilin homolog, Psn, together with partial loss-of-function of sulfateless (sfl), a gene specifically affecting HS sulfation . HSPG modulation of autophagy, mitochondrial function, and lipid metabolism were shown to be conserved in human cell lines, Drosophila, and mouse astrocytes. RNA interference (RNAi) of Ndst1 reduced intracellular lipid levels in wild-type mouse astrocytes or those expressing humanized variants of APOE, APOE3, and APOE4. Neuron-directed knockdown of Psn in Drosophila produced apoptosis and cell loss in the brain, phenotypes suppressed by reductions in sfl expression. Abnormalities in mitochondria, liposomes, and autophagosome-derived structures in animals with Psn knockdown were also rescued by reduction of sfl. These findings support the direct involvement of HSPGs in AD pathogenesis.

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          Highlights

          • Heparan sulfate (HS) affects autophagy, mitochondria, and lipid metabolism

          • AD-related gene transcripts overrepresented in HS-dependent transcriptome

          • Reducing HS sulfation rescues neuronal loss in presenilin-deficient Drosophila

          • Cell deficits mediated by presenilin knockdown suppressed by reduced HS sulfation

          Abstract

          Molecular neuroscience; Cell biology

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          Most cited references113

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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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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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                02 July 2024
                19 July 2024
                02 July 2024
                : 27
                : 7
                : 110256
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
                [2 ]Center for Innovation in Brain Science and Department of Pharmacology, University of Arizona, Tucson, AZ 85721, USA
                [3 ]Department of Biochemistry and Molecular Biology, Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
                Author notes
                []Corresponding author sbs24@ 123456psu.edu
                [4]

                Lead contact

                Article
                S2589-0042(24)01481-0 110256
                10.1016/j.isci.2024.110256
                11302002
                39109174
                831a0c52-7297-42cd-a11c-fc25ddae9c02
                © 2024 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 30 January 2024
                : 6 May 2024
                : 10 June 2024
                Categories
                Article

                molecular neuroscience,cell biology
                molecular neuroscience, cell biology

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