Proteomic Analysis of Alterations Induced by Perinatal Hypoxic–Ischemic Brain Injury

Perinatal hypoxic–ischemic brain injury is an important cause of neurological deficits still causing mortality and morbidity in the early period of life. As efficient clinical or pharmaceutical strategies to prevent or reduce the outcome of perinatal hypoxic–ischemic brain damage are limited, the development of new therapies is of utmost importance. To evolve innovative therapeutic concepts, elucidation of the mechanisms contributing to the neurological impairments upon hypoxic–ischemic brain injury is necessary. Therefore, we aimed for the identification of proteins that are affected by hypoxic–ischemic brain injury in neonatal rats. To assess changes in protein expression two days after induction of brain damage, a 2D-DIGE based proteome analysis was performed. Among the proteins altered after hypoxic–ischemic brain injury, Calcineurin A, Coronin-1A, as well as GFAP were identified, showing higher expression in lesioned hemispheres. Validation of the changes in Calcineurin A expression by Western Blot analysis demonstrated several truncated forms of this protein generated by limited proteolysis after hypoxia–ischemia. Further analysis revealed activation of calpain, which is involved in the limited proteolysis of Calcineurin. Active forms of Calcineurin are associated with the dephosphorylation of Darpp-32, an effect that was also demonstrated in lesioned hemispheres after perinatal brain injury

DAF-16 targets and their distribution in HSP90 RNAi induced expression clusters.

<p>All the genes distributed into the clusters of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.g002" target="_blank">Fig 2</a> are evaluated, whether they are ranked DAF-16 Class I (upregulated) or DAF-16 Class II (downregulated) targets. To this end the ranking was employed from Tepper <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.ref063" target="_blank">63</a>]. A) For each cluster the position of the genes is marked by a point at the respective ranking position. The top 1663 genes represent targets considered upregulated by DAF-16; the bottom 1733 genes represent targets downregulated by DAF-16 according to this ranking. B) Histograms for the two clusters <i>daf-21</i>Up_1 and <i>daf-21</i>Up_2, where the bar on the left side roughly represents the 1663 Class I targets and the bar on the right side the 1733 Class II targets. C) Histograms for the clusters downregulated after Hsp90 RNAi.</p

Influence of Hsp90-depletion on the <i>C</i>. <i>elegans</i> proteome.

<p>A) Control and Hsp90 RNAi treated N2 nematodes at the moment of harvesting for analysis. The developing phenotype can be seen at the vulva site (yellow arrow) and the Hsp90-depleted nematodes are slightly smaller at that stage. B) Proteomic changes based on isotope-labelled mass spectrometry. The two independent experiments are plotted in the two dimensions. Blue spots indicate genes, for which the identification scores based on the number of detected peptides imply a reliable quantification. C) Network of upregulated proteins and corresponding GO enrichment analysis. The network was generated based on hit-to-hit relationships in a coexpression database as described in the Materials and Methods section. D) Network of downregulated proteins and corresponding GO enrichment analysis. The network was constructed with identical settings as in C.</p

Hsp90-downregulation influences the heat-shock response, innate immune response and onset of oocyte development in nematodes

<div><p>Hsp90 is a molecular chaperone involved in the regulation and maturation of kinases and transcription factors. In <i>Caenorhabditis elegans</i>, it contributes to the development of fertility, maintenance of muscle structure, the regulation of heat-shock response and <i>dauer</i> state. To understand the consequences of Hsp90-depletion, we studied Hsp90 RNAi-treated nematodes by DNA microarrays and mass spectrometry. We find that upon development of phenotypes the levels of chaperones and Hsp90 cofactors are increased, while specific proteins related to the innate immune response are depleted. In microarrays, we further find many differentially expressed genes related to gonad and larval development. These genes form an expression cluster that is regulated independently from the immune response implying separate pathways of Hsp90-involvement. Using fluorescent reporter strains for the differentially expressed immune response genes <i>skr-5</i>, <i>dod-24</i> and <i>clec-60</i> we observe that their activity in intestinal tissues is influenced by Hsp90-depletion. Instead, effects on the development are evident in both gonad arms. After Hsp90-depletion, changes can be observed in early embryos and adults containing fluorescence-tagged versions of SEPA-1, CAV-1 or PUD-1, all of which are downregulated after Hsp90-depletion. Our observations identify molecular events for Hsp90-RNAi induced phenotypes during development and immune responses, which may help to separately investigate independent Hsp90-influenced processes that are relevant during the nematode’s life and development.</p></div

Immune response genes respond to <i>daf-21</i>-RNAi in intestinal tissues.

<p>DIC and fluorescence images of Hsp90-treated (right panels) and control nematodes (left panels) are shown. A) SHK207 nematodes were imaged to visualize SKR-5::GFP protein. Control nematodes showed rare expression in Int5, Int6 and Int7 cells. Hsp90-depleted nematodes generally showed induction in the full intestine. B) qRT-PCR has been performed for <i>skr-5</i> in control nematodes and in Hsp90-depleted nematodes as described in Materials and Methods. C) AU185 nematodes were imaged to visualize <i>clec-60p</i>-driven GFP expression. Control nematodes express in Int8 and Int9 cells and rarely in more anterior intestinal cells (left panel). Hsp90-depleted nematodes show induction in most parts of the intestine (right panel). D) AU10 nematodes were imaged to visualize expression of <i>dod-24p</i>-driven GFP. Control nematodes show induction in all parts of the intestine (left panel). Expression is substantially reduced in Hsp90-depleted nematodes (right panel). The scale bar represents 100 μm.</p

Regulation of separated clusters during <i>daf</i>-21 RNAi-response and other conditions.

<p>A) Comparison between the three replicates regarding the up- or downregulation of specific clusters. The included genes are used to determine the UpRegScore of the respective cluster in each of the experiments as described in Materials and Methods. The score was calculyted as described in the Materials and Methods section. The results for the respective replicate on the single-gene basis are show in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.s002" target="_blank">S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.s003" target="_blank">S3</a> Figs and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.s004" target="_blank">S4B Fig</a>) Calculation of the UpRegScore for each gene cluster in the microarray experiments for RNAi against <i>sams-1</i> or <i>sbp-1</i> (left side), <i>V</i>. <i>cholerae</i> VC109/VC110 exposure and <i>P</i>. <i>aeruginosa</i> exposure (right side). The corresponding results on the single-gene basis are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.s005" target="_blank">S5</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186386#pone.0186386.s008" target="_blank">S8 Fig</a>.</p

Genes from cluster <i>daf-21</i>Down_2 are suppressed by Hsp90 RNAi in gonads and embryos.

<p>A) HZ455 nematodes were imaged to visualize the expression and subcellular localization of SEPA-1 protein. Yellow arrows indicate the position of developed embryos, while green arrows indicate the fluorescence prior to passage through the spermathecum. B) RT688 nematodes were imaged to visualize subcellular localization of CAV-1 protein. The CAV-1 protein in the meiotic zone of the gonad arm is indicated in both panels with a green arrow, while yellow arrows indicate the position of developed embryos. C) BC12422 nematodes were imaged to visualize the cells expressing <i>zip-8</i>. The scale bar represents 100 μm. Yellow arrows show the position of a <i>bzip-8</i>::GFP expressing embryo. D) GFP::PUD-1.1 can be observed in intestinal tissues in later larval stages. After Hsp90 RNAi this expression is strongly diminished. The scale bar represents 100 μm.</p

Transcriptional networks affected by RNAi against <i>daf-21</i>.

<p>Networks for upregulated (A) and downregulated (B) genes after Hsp90-depletion. The networks were constructed as described in the Materials and Methods section. The color code uses four shadings of blue and red to indicate the expression differences with darkest blue being log₂(DiffExp) < -1, lightest blue being log₂(DiffExp) < -0.25, darkest red being log₂(DiffExp) > 1 and lightest red being log₂(DiffExp) > 0.25. In between 0.25 and -0.25 the nodes are white as indicated in the legend. Large clusters have been subjected to GO-term enrichment analysis and the results are depicted adjacent to the respective cluster in the network figure.</p

PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface

Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia

Overlapping coalition formation games in wireless communication networks

This brief introduces overlapping coalition formation games (OCF games), a novel mathematical framework from cooperative game theory that can be used to model, design and analyze cooperative scenarios in future wireless communication networks. The concepts of OCF games are explained, and several algorithmic aspects are studied. In addition, several major application scenarios are discussed. These applications are drawn from a variety of fields that include radio resource allocation in dense wireless networks, cooperative spectrum sensing for cognitive radio networks, and resource management for crowd sourcing. For each application, the use of OCF games is discussed in detail in order to show how this framework can be used to solve relevant wireless networking problems. Overlapping Coalition Formation Games in Wireless Communication Networks provides researchers, students and practitioners with a concise overview of existing works in this emerging area, exploring the relevant fundamental theories, key techniques, and significant applications.