Holistic View on the Extended Substrate Specificities of Orthologous Granzymes

As proteases sculpt the proteome in both homeostatic and pathogenic processes, unraveling their primary signaling pathways and key substrates is of utmost importance. Hence, with the development of procedures enriching for proteolysis-indicative peptides and the availability of more sensitive mass spectrometers, protease degradomics technologies are ideally suited to gain insight into a protease’s substrate repertoire and substrate-specificity profile. Especially, knowledge on discriminating sequence features among closely related homologues and orthologues may aid in identifying key targets and developing protease-specific inhibitors. Although clever labeling strategies allow one to compare the substrate repertoires and critical protease–substrate recognition motifs of several proteases in a single analysis, comprehensive views of (differences in) substrate subsite occupancies of entire protease families is lacking. Therefore, we here describe a hierarchical cluster analysis of the positional proteomics determined cleavage sites of a family of serine proteases: the granzymes. We and others previously assigned clear murine orthologues for all 5 human granzymes. As such, hierarchical clustering of the sequences surrounding granzyme cleavage sites reveals detailed insight into granzyme-specific differences in substrate selection and thereby deorphanizes the substrate specificity profiles and repertoires of the human and murine orthologous granzymes A, B, H/C, M, and K

Additional file 1: Figure S1. of Systems-based approach to examine the cytokine responses in primary mouse lung macrophages infected with low pathogenic avian Influenza virus circulating in South East Asia

A) Identification of PMФ using ant-F4/80 antibody and ant-CD11b antibody. B) FACS analysis of anti-CD11b and anti-F4/80 stained PMФ. C) Overlap analysis of DEGs in three IAV viruses and RSV (the data for RSV was obtained from our previously published work (Ravi et al., 2013)). D) Top 20 significantly enriched pathways of DEGs in IAVs and RSV infected PMФ. E) Unsupervised hierarchical clustering of DEGs at 2 and 24hpi in H1N1/WSN and H5N2 infections. (PDF 230 kb

Additional file 2: of Systems-based approach to examine the cytokine responses in primary mouse lung macrophages infected with low pathogenic avian Influenza virus circulating in South East Asia

List of differentially expressed probe sets or genes A) List of all DEGs with logFC and adjusted p-values B) Expression levels of cytokines in H5N3 compared to H1N1/WSN and H5N2 C) Cytokine expression levels in H5N3 and RSV. (XLSX 233 kb

Mutation counts and time to resistance.

<p>For each set of target sequences, various sequence lengths ranging from 15 to 30 nucleotides were considered, and for simplicity, the length of all the target sequences in a set is identical. The median mutation counts and median time to resistance were obtained from 100,000 independent Monte Carlo simulations. <b>(A) Sets of effective <i>Duals</i>.</b> Only effective <i>Duals</i> from segments 1, 3 and 7 were used in the simulations as they are able to form complete graphs with <i>HF ≥ 2</i>. <b>(B) Sets of effective <i>Doubles</i> and sets of effective <i>Duals and effective Doubles combined</i>.</b> For <i>2 ≤ HF ≤ 5</i>, complete graphs formed by effective <i>Doubles</i> depicted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004663#pcbi.1004663.g004" target="_blank">Fig 4C</a> were used in the simulations. For <i>HF = 1</i>, effective <i>Doubles</i> targeting segments 1 and 2 were used. For <i>HF = 6</i>, the target sequence graphs that combine effective <i>Doubles</i> and effective <i>Duals</i> in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004663#pcbi.1004663.g004" target="_blank">Fig 4D</a> were used; 6(1) and 6(2) respectively denote the left and right graphs in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004663#pcbi.1004663.g004" target="_blank">Fig 4D</a>.</p

Hedge-factor of a set of target sequences.

<p>Each graph depicts a set of selected target sequences (shown as nodes) wherein an edge represents an effective <i>Dual</i> or <i>Double</i>. The nodes used to compute the hedge-factor (given in brackets) are either shaded or bordered grey. <b>(A) Representative values of hedge-factor, <i>HF</i>.</b><i>HF</i> of all possible 2-, 3- and 4-vertices graphs are shown. Complete graphs are demarcated by a border. <b>(B) Maximum <i>HF</i> of effective <i>Duals</i> clusters.</b> The topologies of the <i>Duals</i> clusters from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004663#pcbi.1004663.g002" target="_blank">Fig 2</a> and their maximal <i>HFs</i> were presented. <b>(C) Maximum <i>HF</i> of segment partner graphs by effective <i>Doubles</i>.</b> As each internal segment can be used to form complete graphs exhibiting a range of <i>HF</i> from 2 to 5, only representative complete graphs are shown. <b>(D) Representative target sequence graphs with <i>HF = 6</i> constructed by combining effective <i>Duals</i> and effective <i>Doubles</i>.</b> Effective <i>Duals</i> and effective <i>Doubles</i> are denoted by broken and full edges, respectively. The running number in the round bracket of a target sequence’s label is purely schematic to denote different target sequences.</p

Clusters of effective <i>Duals</i>.

<p>Each vertex denotes a cluster of overlapping single target sequences whose first and last positions were given within the enclosing square brackets. An edge connecting two vertices signifies that target sequences between the two clusters form one or more effective <i>Duals</i>. Note: overlapping clusters in S1 (3-S) are not merged as a cluster because they can form effective <i>Duals</i>.</p

<i>NSP</i> of single target sequences in an effective <i>Double and</i> segment partner graphs.

<p><b>(A) Schematic to determine the <i>NSP</i></b>. An effective <i>Double</i> is represented by a grey line connecting a pair of nodes denoting single target sequences from two target segments. For illustration, the <i>NSP</i> of a single target sequence in segment 1 (depicted as an enlarged grey node) is unity in the left panel (as it forms an effective <i>Double</i> only with segment 2 target sequences) and five in the right panel (as it forms an effective <i>Double</i> with target sequences from each of the other five segments). <b>(B) <i>NSP</i> frequency distributions</b>. Number of single target sequences against <i>NSP</i> by target segment in 5-S (left) and 3-S (right) sets plotted as 100% stacked bar charts. <b>(C) 6-vertices <i>(NSP = 5)</i> segment partner graphs.</b> The size (number of effective <i>Doubles</i>) distribution of all permutations of 6-vertices segment partner graphs constructed by single target sequences with <i>NSP = 5</i> from the six internal segments were plotted in absolute number of graphs for 5-S (left) and 3-S (right).</p

Overlap between miRNA-regulated signaling pathways affected in myopic retina.

<p>Diagram depicts miRNA contributions to the 9 miRNA-mRNA signaling cascades associated with form-deprivation myopia in mice.</p

Overlap between miRNAs differentially expressed in the myopic retina and miRNAs differentially expressed in the retina versus sclera.

<p>Venn diagram shows overlap between 53 miRNAs, which were differentially expressed in the myopic retina, 136 miRNAs, which were up-regulated in the retina versus sclera, and 109 miRNAs, which were up-regulated in the sclera versus retina. Eighteen differential miRNAs were equally expressed in both retina and sclera, 20 differential miRNAs were up-regulated in the retina versus sclera and 15 differential miRNAs were down-regulated in the retina versus sclera.</p

Gene ontology categories affected in myopic retina.

<p>Graph shows top 18 biological processes which were modified in the myopic retina.</p