Pairwise disjoint sectors of all possible configurations (wNS*,wFN*) within the rectangular domain 〈0, 10〉 × 〈0, 20〉, which simultaneously preserve a particular ranking for each individual dataset.

<p>The red crosses mark the reference configuration <math><mrow><mrow><mo>(</mo><msubsup><mi>w</mi><mrow><mi>N</mi><mi>S</mi></mrow><mo>*</mo></msubsup><mo>,</mo><msubsup><mi>w</mi><mrow><mi>F</mi><mi>N</mi></mrow><mo>*</mo></msubsup><mo>)</mo></mrow><mo>=</mo><mrow><mo>(</mo><mn>5</mn><mo>,</mo><mn>10</mn><mo>)</mo></mrow></mrow></math> used for compiling the reference rankings. The upper-right corners outlined by the white lines consist of the configurations, which break the minimality condition given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144959#pone.0144959.e056" target="_blank">Eq (13)</a>. The colors encode the numbers of transpositions in the particular rankings compared to the reference ones. No transposition occurs in the pink regions, one transposition occurs in the blue regions, and two transpositions occur in the tiny yellow region.</p

An example of calculating the <i>AOGM</i> measure for a reference graph (upper left) formed of circular vertices and black edges and a computed graph (upper right) formed of rectangular vertices and dark pink edges.

<p>In both graphs, the vertical axis represents the temporal domain and the horizontal axis represents the spatial domain (i.e., each vertex has a certain spatial extent). The <i>AOGM</i> measure is the weighted sum of the following quantities (bottom): the number of splits (<i>NS</i> = 5, black asterisks in pink-white rectangles) computed as the difference between the number of true positive vertices (20 green circles) and the number of white and pink-white rectangles containing at least one green circle (15), the number of false negative vertices (<i>FN</i> = 5, white circles with the black plus sign), the number of false positive vertices (<i>FP</i> = 3, white rectangles with the black cross), the number of redundant edges (<i>ED</i> = 1, black cross), the number of missing edges (<i>EA</i> = 16, small red circles), and finally the number of edges with wrong semantics (<i>EC</i> = 2, small blue circles).</p

An example of tracking results represented by an acyclic oriented graph.

<p>Individual tracks are visualized using different colors. Solid lines correspond to track links, whereas parent links are depicted using dashed lines.</p

The temporal evolution of the <i>AOGM</i> measure, averaged over the two sequences per each real dataset, for the four tested algorithms (A1–A4).

<p>The dashed vertical lines indicate the frames at which the algorithm ranking compiled using the <i>AOGM</i> measure has changed.</p

The distribution of errors committed by the four tested algorithms for individual datasets.

<p>Each group of four horizontal bars corresponds to the tested algorithms depicted in the same order for each dataset.</p

The SUMOylation Pathway Restricts Gene Transduction by Adeno-Associated Viruses

<div><p>Adeno-associated viruses are members of the genus dependoviruses of the parvoviridae family. AAV vectors are considered promising vectors for gene therapy and genetic vaccination as they can be easily produced, are highly stable and non-pathogenic. Nevertheless, transduction of cells <i>in vitro</i> and <i>in vivo</i> by AAV in the absence of a helper virus is comparatively inefficient requiring high multiplicity of infection. Several bottlenecks for AAV transduction have previously been described, including release from endosomes, nuclear transport and conversion of the single stranded DNA into a double stranded molecule. We hypothesized that the bottlenecks in AAV transduction are, in part, due to the presence of host cell restriction factors acting directly or indirectly on the AAV-mediated gene transduction. In order to identify such factors we performed a whole genome siRNA screen which identified a number of putative genes interfering with AAV gene transduction. A number of factors, yielding the highest scores, were identified as members of the SUMOylation pathway. We identified Ubc9, the E2 conjugating enzyme as well as Sae1 and Sae2, enzymes responsible for activating E1, as factors involved in restricting AAV. The restriction effect, mediated by these factors, was validated and reproduced independently. Our data indicate that SUMOylation targets entry of AAV capsids and not downstream processes of uncoating, including DNA single strand conversion or DNA damage signaling. We suggest that transiently targeting SUMOylation will enhance application of AAV <i>in vitro</i> and <i>in vivo</i>.</p></div

Knockdown of enzymes of the SUMOylation pathway does not alter expression of a stably integrated CMV-eGFP gene.

<p>A HeLa cell line stably expressing eGFP was transfected with four different siRNAs targeting Ubc9 and Sae2, respectively. Forty-eight h later, the cells were infected with scAAV2-renilla luciferase at an MOI_GC of 10³. Twenty-four h after infection, cells were harvested by trypsinization. One half of a well of a 6-well plate was proceeded for FACS-analysis (a), the other half was used to determine luciferase activity (b). Mean values and standard deviation of two independent experiments of mean fluorescent intensity (MFI) and relative light units (RLU), respectively, are shown. The values obtained after transfection with <i>AllStars negative control siRNA</i> (Qiagen; ‘scrambled’) were set to 100 in both cases.</p

Prediction of putative SUMOylation sites in the AAV VP1 proteins.

<p>Potential SUMOylation sites and SUMO-interacting motifs (SIM) were predicted using GPS-SUMO [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005281#ppat.1005281.ref077" target="_blank">77</a>]. Number and specific position of potential SUMOylation sites and SIMs are shown on the left hand side. AAV1, 2, 3, 6, 7, 8, 9, 10 and 13 harbor a potential lysine (K) which can serve as SUMOylation target (yellow). Also AAV4, 11 and 12 expose a potential SUMOylation target K at a position nearby (green).</p

Components of the SUMOylation pathway identified as putative AAV host cell restriction factors.

<p>The screening of the genome-wide siRNA library revealed several factors of the SUMOylation pathway influencing AAV transduction. Protein and gene names are indicated. The screens were carried out in duplicates and each gene was targeted by three different siRNAs. The screen identified 740 putative host cell restriction factors, the table shows the ranking of the factors according to their z-score. Note: in the manuscript the protein identifiers are also used in reference to the corresponding gene.</p

Knockdown of SUMOylation key enzymes increased transduction with different serotypes and capsid variants but not that of autonomous parvovirus H1 or human papillomavirus.

<p>A: HeLa cells were transfected with siRNAs targeting Ubc9 or Sae2. 46 h later, the cells were infected with ss-firefly luciferase vectors of AAV 1, 8, 9 and capsid variants thereof (left part) and scAAV5- or scAAV9-renilla luciferase (right part) at an MOI of 10⁴. The variants of AAV 1, 8, and 9 harbored heptamer insertions at the threefold spikes in position corresponding to amino acid 588 of AAV2. NYS: Peptide NYSRGVD; NEA: peptide NEAVRE. 25 h after infection, cells were lysed and analyzed for luciferase activity. The RLU values in the case of transfection of the control siRNA ‘AllStars negative control siRNA’ were set to 100. The mean values and standard deviation of three independent experiments are shown. B; C; D: HeLa cells were transfected with siRNA targeting Ubc9 (B) or Sae2 (C and D) 48 h before they were transduced with different recombinant vectors encoding luciferase reporters. Luciferase activity was determined 24 h post infection. The graphs show the ratio of luciferase activity of cells treated with siRNAs targeting Ubc9 or Sae2, respectively and cells treated with <i>AllStars negative control siRNA</i> (scrambled). Shown are the mean of three independent experiments with standard deviations.</p