<i>In silico</i> knockout matrix and the corresponding PN.

<p>(A) A small PN, consisting of three places, protein <i>A</i>, protein <i>B</i>, and a protein complex <i>AB</i>, and four transitions, <i>SynA</i>, <i>SynB</i>, <i>Bin</i>, and <i>Out</i>, which describe the synthesis of <i>A</i>, the synthesis of <i>B</i>, the binding of both proteins, and the outflow of the complex to the environment, respectively. (B) The <i>in silico</i> knockout matrix of the PN shown in part A. The matrix has a row for each input transition, <i>SynA</i> and <i>SynB</i>, and columns for each substance, <i>A</i>, <i>B</i>, and <i>AB</i>. The binary values of an entry are color-coded by either a red or a green circle. An entry becomes red, if the corresponding place is not the outgoing place of a transition that is part of a still functional T-invariant. The entry is green, if the corresponding place is still the output place of a transition that is part of a T-invariant. For the PN in part A, the knockout of either <i>SynA</i> or <i>SynB</i> is sufficient to have a negative effect on all substances, <i>A</i>, <i>B</i>, and <i>AB</i>. Consequently, all entries in the matrix are red. (C) The PN is modified by adding the output transitions, <i>DegA</i> and <i>DegB</i>. Now, the knockout analysis becomes more specific. The knockout of <i>SynA</i> blocks the production of <i>A</i> and complex <i>AB</i>, but <i>B</i> is not affected. Vice versa, the knockout of <i>SynB</i> leaves <i>A</i> unaffected. (D) The corresponding <i>in silico</i> knockout matrix of the modified PN shown in part C.</p

PN model of <i>Salmonella</i> xenophagy.

<p>The PN model comprises 61 places, including nine logical places represented by different colors, and 69 transitions connected by 184 arcs. All places and transitions, including a description and a reference, are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.s007" target="_blank">S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.s008" target="_blank">S2</a> Tables.</p

Schematic model of <i>Salmonella</i> xenophagy.

<p>Inside lumen of the mammalian small intestine, <i>Salmonella</i> invades epithelial cells and resides within the SCV. A fraction of the bacteria does not persist inside the SCV and enters the host cytosol. Before <i>Salmonella</i> gets cytosolic, <i>Salmonella</i> is inside a damaged SCV and is partially exposed to the cytosol. (A) Galectin-8 can bind to host glycans, which are normally hidden inside the SCV. Galectin-8 can target damaged SCV to the autophagic pathway by binding the autophagy receptor, NDP52. (B) Besides galectin-8, ubiquitin is an important eat-me signal for targeting <i>Salmonella</i> inside a damaged SCV to the autophagy pathway. <i>Salmonella</i> gets ubiquitinated by the E3 ubiquitin ligase, LRSAM1, and other E3 ubiquitin ligases. The autophagy receptors, p62, NDP52, and OPTN, serve as adaptors, which can bind both ubiquitin-chains and LC3/GABARAP molecules on phagophores. The binding affinity of OPTN with LC3 can be enhanced by TBK1 phosphorylation of OPTN. The hypothetical mechanism for TLR4-independent activation of TBK1 is depicted in a detailed view. TBK1 is recruited via the Nap1/Sintbad-NDP52 complex or the Nap1/Sintbad-NDP52 complex and OPTN to ubiquitinated <i>Salmonella</i>. This recruitment induces a high local concentration of TBK1 dimers, resulting in their oligomerization and autophosphorylation. (C) <i>Salmonella</i> that is already fully cytosolic, is only targeted by ubiquitin to the xenophagy pathway and not by galectin-8. (D) Autophagy is induced by intracellular amino acid (AA) starvation at 1–2 hr post infection (p.i.), which is assumed to be triggered by the damage of the SCV. The intracellular AA starvation results in the inhibition of mTOR—a subunit of mTORC1. Under AA starvation conditions, mTORC1 is inactivated and dissociates from the ULK1 complex, recovering the kinase activity of the ULK1 complex. The activated ULK1 complex seems to be required for the phagophore formation. The intracellular AA pool normalizes 3–4 hr p.i., and mTORC1 localizes at the surface of the SCV and gets reactivated.</p

<i>In silico</i> knockout matrix.

<p>A row represents a protein knockout and a column the effect of the perturbation on a macromolecular <i>Salmonella</i> complex. A green entry indicates no effect and a red entry a negative effect, i.e., a reduced formation of a macromolecular complex. The numbers in some entries represent the reference literature of experimentally investigated effects: ¹ Huett et al. 2012 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref034" target="_blank">34</a>]; ² Thurston et al. 2012 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref006" target="_blank">6</a>], Li et al. 2013 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref033" target="_blank">33</a>]; ³ Thurston et al. 2012 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref006" target="_blank">6</a>]; ⁴ Cemma et al. 2011 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref038" target="_blank">38</a>]; ⁵ Zheng et al. 2009 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref007" target="_blank">7</a>], Cemma et al. 2011 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref038" target="_blank">38</a>]; ⁶ Wild et al. 2011 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref009" target="_blank">9</a>]; ⁷ Li et al. 2013 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref033" target="_blank">33</a>]; ⁸ Cemma et al. 2011 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref038" target="_blank">38</a>], Thurston et al. 2009 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref008" target="_blank">8</a>]; ⁹ Wild et al. 2011 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref009" target="_blank">9</a>], Radtke et al. 2007 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005200#pcbi.1005200.ref025" target="_blank">25</a>]. The results marked by an asterisk are biologically obvious and need no further experimental investigation. The knockout of the seven places <i>Ap:Gal8, Ap:Gal8:Ub, Ap:Gal8:Ub:N/S, Ap:Ub, Ap:Ub:N/S, Ap:Gal8:Ub:OPTNp, Ap:Ub:OPTNp</i> is experimentally investigated, if the fraction of LC3/GABARAP-positive <i>Salmonella</i> has been observed in the experiments.</p

Sensitivity matrix of the <i>in silico</i> knockout analysis.

<p>The rows and the columns indicate the proteins knocked out. Thus, the diagonal of the matrix shows the results of the single knockouts and the other entries of the double knockouts. The numbers represent the percentage of T-invariants that are affected by a knockout. The colors indicate the impact on the xenophagy pathway (red = high, green = low).</p

Automated Image Analysis of Hodgkin Lymphoma

<p>Hodgkin lymphoma is an unusual type of lymphoma, arising from malignant B-cells. Morphological and immunohistochemical features<br>of malignant cells and their distribution differ from other cancer types. Based on systematic tissue image analysis, computer-aided exploration<br>can provide new insights into Hodgkin lymphoma pathology.</p> <p>Here, we report results from an image analysis of CD30 immunostained classical Hodgkin lymphoma (cHL) tissue section images. We have imple-<br>mented an automatic procedure to handle and explore image data in Aperio's SVS format. We use pre-processing approaches to separate the image<br>objects from the background, then select regions of interest and split the large images into tiles. Then, we use a CellProfiler pipeline to detect primary objects. Therefore, the images are split into their color stains using a color deconvolution approach. By setting a threshold in the CD30 stain image we identify CD30 positive cells and compute their shape descriptors. We label the cells based on size, elongation and compactness. We present results for a small set of nodular sclerosis, mixed type and non-lymphoma images.</p> <p> </p

Relative abundance of proteins mapped to the functional subnetwork of calcium homeostasis.

<p>A Impact of APP deletion. B Impact of the NexCre-cDKO. Change in abundance of more than ±10% is reflected by increasing sizes of nodes. The color code corresponds to the degree of up- (magenta) and downregulation (green). Nodes in yellow represent proteins with changes in abundance of less than ±10%. Abbreviations are the respective gene names of individual proteins as given in UniProt database and in the supplementary information <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004832#pcbi.1004832.s001" target="_blank">S1 Table</a>.</p

The interactome of the native hippocampal PAZ core proteome.

<p>A Proteins grouped according to their localization (localization layout). B Community structure layout (function) of the network. The size of the rings corresponds to the respective number of proteins. The color code corresponds to the pie chart diagram (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004832#pcbi.1004832.g001" target="_blank">Fig 1E</a>). C Impact of APP deletion on relative protein abundance mapped according to their localizations. D Impact of the APP-KO on relative protein abundance mapped according to the community structure. E Impact of the NexCre-cDKO on relative protein abundance mapped according to their localizations. F Impact of the NexCre-cDKO on relative protein abundance mapped according to the community structure. Change in abundance of more than ±10% is reflected by increasing sizes of nodes. The color code corresponds to the degree of up- (magenta) and downregulation (green). Nodes in yellow represent proteins with changes in abundance of less than ±10%. Each node (dot in the rings) within this network represents a protein and each edge (connection) represents a reported physical interaction between two proteins. Edges are bundled for clarity.</p

Relative abundance of proteins mapped to the functional subnetwork of the synaptic vesicle cycle.

<p>A Impact of APP deletion. B Impact of the NexCre-cDKO. Change in abundance of more than ±10% is reflected by increasing sizes of nodes. The color code corresponds to the degree of up- (magenta) and downregulation (green). Nodes in yellow represent proteins with changes in abundance of less than ±10%. Abbreviations are the respective gene names of individual proteins as given in UniProt database and in the supplementary information <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004832#pcbi.1004832.s001" target="_blank">S1 Table</a>.</p

Scheme illustrating the regulatory role of APP in a context-sensitive manner at the hippocampal PAZ. R, regulator; M, mediator, C, central player.

<p>Color code: magenta, upregulation (in APP-mutants); green, downregulation (in APP-mutants); yellow, unaltered.</p