{"id":2416,"date":"2014-03-17T08:00:11","date_gmt":"2014-03-17T12:00:11","guid":{"rendered":"http:\/\/admin.acceleratingscience.com\/?p=2416"},"modified":"2014-03-17T08:00:11","modified_gmt":"2014-03-17T12:00:11","slug":"silicon-metabolism-in-marine-diatoms-a-quantitative-proteomic-and-genomic-analysis","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/proteomics\/silicon-metabolism-in-marine-diatoms-a-quantitative-proteomic-and-genomic-analysis\/","title":{"rendered":"Silicon Metabolism in Marine Diatoms: A Quantitative Proteomic and Genomic Analysis"},"content":{"rendered":"<p>Diatoms are highly successful marine and freshwater organisms. These eukaryotic phytoplankton are extremely productive in the world&rsquo;s oceans, responding sensitively to environmental conditions and adapting to cope with nutrient abundance and scarcity. This adaptation includes their metabolism of silicon, a vital constituent in the organism&#8217;s cell wall and one&nbsp;that can lead to cell cycle arrest if in short supply.<\/p>\n<p><span>Metabolic pathways controlling silicon use are poorly understood in diatoms. Although researchers have previously investigated their genomic<sup>1<\/sup> and proteomic<sup>2<\/sup> profiles, proteins involved in cellular metabolic activity and their actions could only be deduced by analyzing abundance.&nbsp;In this study, however,&nbsp;Du et al. (2013)<sup>&nbsp;<\/sup><span>use isobaric tagging for relative and absolute quantitation (iTRAQ) to quantify the whole-cell metabolic response of <a href=\"http:\/\/en.wikipedia.org\/wiki\/Thalassiosira_pseudonana\" target=\"_blank\"><em>Thalassiosira pseudonana<\/em> <\/a>to silicon starvation and refeeding<\/span>.<sup><span style=\"font-size: xx-small\">3<\/span><\/sup><\/span><\/p>\n<p><span>The researchers&nbsp;synchronized&nbsp;cultures of <em>T. pseudonana<\/em> in <span>a<\/span> <span>silicon-free, enriched<\/span> artificial seawater medium before replacing the missing element. Using fluorescent and bright-field microscopy, Du et al. examined the diatoms, noting salient time points in the cell growth cycle that coincided with anatomical development involving silicon incorporation. They used a fluorescent dye, 2-(4-pyridyl)-5-{[4-(2-dimethylaminoethyl-aminocarbamoyl)methoxy]phenyl}oxazole (PDMPO), to highlight the areas where silica was deposited and were thus able to characterize peak uptake from the nutrient culture medium. <\/span><\/p>\n<p><span>From this important initial characterization, the researchers chose time points 0 (silicon starvation), 1 (girdle band synthesis), 5 (valve formation) and 7 (cell separation) hours post-silicon refeeding to harvest and extract proteins. Once extracted, the proteins were digested with trypsin prior to incubation with 8-plex iTRAQ reagents. Researchers analyzed the prepared samples using nanoelectrospray ionization followed by tandem mass spectrometry on a Q Exactive mass spectrometer (Thermo Scientific).<\/span><\/p>\n<p><span>Proteomic analysis identified 1,831 proteins in total from the <em>T. pseudonana <\/em>samples. The data showed that over the time points examined, 165 proteins were expressed differentially, including those involved in cell wall synthesis, silicon transport and cell cycle events. The greatest changes in proteomic profiles happened between 1 hour and 7 hours post-silicon refeeding, where 52 proteins increased in abundance and 38 declined. Between the time points 0 and 7 hours, 44 proteins increased and 27 decreased, according to iTRAQ quantification. <\/span><\/p>\n<p><span>The researchers chose seven of these differentially expressed proteins for further analysis by quantitative reverse transcription polymerase chain reaction (RT-PCR) to explore genomic changes in the cells. They also investigated the expression of the <em>SIT2<\/em> (Thaps3|41392) gene, which encodes a silicon transporter known to be responsive to silicon incorporation.<sup>4<\/sup> Interestingly, from their iTRAQ data, Du et al. found that SIT2 did not reflect cellular silicon incorporation, instead peaking at 1 hour and then decreasing in abundance even though the cells were in active silicon-deposition mode. RT-PCR data agreed with iTRAQ data for six out of the eight proteins chosen, including SIT2.<\/span><\/p>\n<p><span>The research team also investigated silicon metabolism in the diatoms by looking at SIT co-regulated protein abundance, theorizing that identifying these proteins might show how SIT2 relates to silicon incorporation in <em>T. pseudonana<\/em>. They examined the iTRAQ data and identified 69 associated proteins that correlated well with the changes in SIT2 (&gt;0.9) described above. Using GO enrichment analysis, Du et al. found that these proteins associated most strongly with coated vesicles, suggesting that this cell component plays a role in SIT2-mediated silicon transport.&nbsp;<\/span><span>Other proteins identified as altered, following re-addition of silicon to the medium, included those involved in intracellular processes such as wall synthesis, cell cycle progression and energy metabolism. <\/span><\/p>\n<p><span>The researchers consider their findings useful in understanding the metabolism of marine diatoms. Noting that their conclusions are based solely on the proteins identifiable in the analysis, they suggest that future&nbsp;identification of the more than 500 unidentified proteins detected may shed even more light on marine diatom activity.<\/span><\/p>\n<p><span>&nbsp;<em>image suggestion here:&nbsp;<a href=\"http:\/\/en.wikipedia.org\/wiki\/File:Diatoms_through_the_microscope.jpg\" target=\"_blank\">http:\/\/en.wikipedia.org\/wiki\/File:Diatoms_through_the_microscope.jpg<\/a><\/em><\/span><\/p>\n<p><span><\/span>&nbsp;<\/p>\n<p><span><strong>References<\/strong><\/span><\/p>\n<p><span>1.&nbsp;<\/span>Mock, T.,&nbsp;et al. (2008) &#8220;<a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/18212125\" target=\"_blank\">Whole-genome expression profiling of the marine diatom <em>Thalassiosira pseudonana <\/em>identifies genes involved in silicon bioprocesses<\/a>,&#8221;&nbsp;Proceedings of the National Academy of Sciences of the&nbsp;USA, 105 (pp. 1579&ndash;84).<\/p>\n<p>2. Frigeri, L.G., et al. (2006) &#8220;<a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16207702\" target=\"_blank\">Identification of proteins from a cell wall fraction of the diatom <em>Thalassiosira pseudonana<\/em>: Insights into silicastructure formation<\/a>,&#8221; Molecular and Cellular Proteomics,<strong>&nbsp;<\/strong>5 (pp. 182&ndash;93).<\/p>\n<p>3. Du, C., et al. (2013, December) &#8220;<a href=\"http:\/\/pubs.acs.org\/doi\/abs\/10.1021\/pr400803w\" target=\"_blank\">iTRAQ-based proteomic analysis of the metabolism mechanism associated with silicon response in the marine diatom <em>Thalassiosira pseudonana<\/em><\/a>,&#8221; Journal of Proteome Research [e-pub ahead of print],&nbsp;doi:<strong>&nbsp;<\/strong>10.1021\/pr400803w.<\/p>\n<p>4. Thamatrakoln, K., and Hildebrand, M. (2007) &#8220;<a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC1797941\/\" target=\"_blank\">Analysis of&nbsp;<em>Thalassiosira pseudonana&nbsp;<\/em>silicon transporters indicates distinct regulatory levels and transport activity through the cell cycle<\/a>,&#8221; Eukaryotic Cell, 6 (pp. 271&ndash;9).<\/p>\n<p>&nbsp;<\/p>\n<p><i>Post Author: Amanda Maxwell. Mixed media artist; blogger and social media communicator; clinical scientist and writer.<\/p>\n<p>A digital space explorer, engaging readers by translating complex theories and subjects creatively into everyday language.<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Diatoms are highly successful marine and freshwater organisms. These eukaryotic phytoplankton are extremely productive in the world&rsquo;s oceans, responding sensitively to environmental conditions and adapting to cope with nutrient abundance and scarcity. This adaptation includes their metabolism of silicon, a vital constituent in the organism&#8217;s cell wall and one&nbsp;that can lead to cell cycle arrest<\/p>\n","protected":false},"author":21,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[24],"tags":[267,173,268,231],"division":[],"class_list":{"0":"post-2416","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-non-human-proteomics","7":"tag-genomics","8":"tag-itraq","9":"tag-msms","10":"tag-quantitative-proteomics","11":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"<!-- 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