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      How reversible are the effects of silver nanoparticles on macrophages? A proteomic-instructed view

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          Silver nanoparticles are known to have profounds effects on living cells, but little is known on how and to which extent cells recover after an acute exposure to silver nanoparticles. This is studied on macrophages in this work.


          Silver nanoparticles are known to strongly affect biological systems, and numerous toxicological studies have investigated their effects. Most of these studies examine the effects immediately following acute exposure. In this work, we have conducted further investigation by studying not only the acute, post-exposure response, but also the cellular response after a 72 hour-recovery-phase post exposure. As a biological model we have used macrophages, which are very important cells with respect to their role in the immune response to particulate materials. To investigate the response of macrophages to nanoparticles and their recovery post exposure, we have used a combination of proteomics and targeted experiments. These experiments provided evidence that the cellular reaction to nanoparticles, including the reaction during the recovery phase, is a very active process involving massive energy consumption. Pathways such as the oxidative stress response, central and lipid metabolism, protein production and quality control are strongly modulated during the cellular response to nanoparticles, and restoration of basic cellular homeostasis occurs during the recovery period. However, some specialized macrophage functions, such as lipopolysaccharide-induced cytokine and nitric oxide production, did not return to their basal levels even 72 hours post exposure, showing that some effects of silver nanoparticles persist even after exposure has ceased.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Is Open Access

            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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              Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis.

              Mononuclear phagocytes, including monocytes, macrophages, and dendritic cells, contribute to tissue integrity as well as to innate and adaptive immune defense. Emerging evidence for labor division indicates that manipulation of these cells could bear therapeutic potential. However, specific ontogenies of individual populations and the overall functional organization of this cellular network are not well defined. Here we report a fate-mapping study of the murine monocyte and macrophage compartment taking advantage of constitutive and conditional CX(3)CR1 promoter-driven Cre recombinase expression. We have demonstrated that major tissue-resident macrophage populations, including liver Kupffer cells and lung alveolar, splenic, and peritoneal macrophages, are established prior to birth and maintain themselves subsequently during adulthood independent of replenishment by blood monocytes. Furthermore, we have established that short-lived Ly6C(+) monocytes constitute obligatory steady-state precursors of blood-resident Ly6C(-) cells and that the abundance of Ly6C(+) blood monocytes dynamically controls the circulation lifespan of their progeny. Copyright © 2013 Elsevier Inc. All rights reserved.

                Author and article information

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                Environmental Science: Nano
                Environ. Sci.: Nano
                Royal Society of Chemistry (RSC)
                October 10 2019
                : 6
                : 10
                : 3133-3157
                [1 ]Chemistry and Biology of Metals
                [2 ]Univ. Grenoble Alpes
                [3 ]CNRS UMR5249
                [4 ]CEA
                [5 ]IRIG
                [6 ]Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO)
                [7 ]Université de Strasbourg
                [8 ]CNRS
                [9 ]IPHC UMR 7178
                [10 ]67000 Strasbourg
                [11 ]INRA
                [12 ]CNRS UMR5168
                [13 ]Inserm U1216 Grenoble Institut des Neurosciences
                [14 ]38000 Grenoble
                [15 ]France
                [16 ]Modelization and Exploration of Materials
                [17 ]CEA-DRF-IRIG-DEPHY-MEM-LEMMA
                [18 ]F-38000 Grenoble
                [19 ]Univ. Grenoble-Alpes
                [20 ]CNRS UMR 5819
                [21 ]SyMMES
                © 2019




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