Duplicability of self-interacting human genes

BACKGROUND There is increasing interest in the evolution of protein-protein interactions because this should ultimately be informative of the patterns of evolution of new protein functions within the cell. One model proposes that the evolution of new protein-protein interactions and protein complexes proceeds through the duplication of self-interacting genes. This model is supported by data from yeast. We examined the relationship between gene duplication and self-interaction in the human genome. RESULTS We investigated the patterns of self-interaction and duplication among 34808 interactions encoded by 8881 human genes, and show that self-interacting proteins are encoded by genes with higher duplicability than genes whose proteins lack this type of interaction. We show that this result is robust against the system used to define duplicate genes. Finally we compared the presence of self-interactions amongst proteins whose genes have duplicated either through whole-genome duplication (WGD) or small-scale duplication (SSD), and show that the former tend to have more interactions in general. After controlling for age differences between the two sets of duplicates this result can be explained by the time since the gene duplication. CONCLUSIONS Genes encoding self-interacting proteins tend to have higher duplicability than proteins lacking self-interactions. Moreover these duplicate genes have more often arisen through whole-genome rather than small-scale duplication. Finally, self-interacting WGD genes tend to have more interaction partners in general in the PIN, which can be explained by their overall greater age. This work adds to our growing knowledge of the importance of contextual factors in gene duplicability.At the time of publication the author Pérez-Bercoff was affiliated with Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin

A Conserved Mammalian Protein Interaction Network

Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.A˚.P.B. is supported by Ga˚lo¨stiftelsen Stipendium fo¨r ho¨gre utlandsstudier. C.M.H. is supported by a National Library of Medicine Biomedical and Health Informatics Training Fellowship [LM007089-19]. G.C.C. is supported by the Reproductive Biology Group of the Food for the 21st Century program at the University of Missouri. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Whole CMV proteome pattern recognition analysis after HSCT identifies unique epitope targets associated with the CMV status

Cytomegalovirus (CMV) infection represents a vital complication after Hematopoietic Stem Cell Transplantation (HSCT). We screened the entire CMV proteome to visualize the humoral target epitope-focus profile in serum after HSCT. IgG profiling from four patient groups (donor and/or recipient +/- for CMV) was performed at 6, 12 and 24 months after HSCT using microarray slides containing 17174 of 15mer-peptides overlapping by 4 aa covering 214 proteins from CMV. Data were analyzed using maSigPro, PAM and the 'exclusive recognition analysis (ERA)' to identify unique CMV epitope responses for each patient group. The 'exclusive recognition analysis' of serum epitope patterns segregated best 12 months after HSCT for the D+/R+ group (versus D-/R-). Epitopes were derived from UL123 (IE1), UL99 (pp28), UL32 (pp150), this changed at 24 months to 2 strongly recognized peptides provided from UL123 and UL100. Strongly (IgG) recognized CMV targets elicited also robust cytokine production in T-cells from patients after HSCT defined by intracellular cytokine staining (IL-2, TNF, IFN and IL-17). High-content peptide microarrays allow epitope profiling of entire viral proteomes; this approach can be useful to map relevant targets for diagnostics and therapy in patients with well defined clinical endpoints. Peptide microarray analysis visualizes the breadth of B-cell immune reconstitution after HSCT and provides a useful tool to gauge immune reconstitution.The work has been funded by ALF (Arbetslivfonden) to M.M. and P.L. funds from Karolinska Institutet and Vinnova, Sweden to M.M

SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/

A Conserved Mammalian Protein Interaction Network

<div><p>Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.</p> </div

PPI presence and absence at the different nodes in the rooted eutherian phylogenetic tree. A)

<p>At each node, we have shown the predicted percentage of human PPIs present at that node (necessarily 100% at the human tip). The percentages at the other seven tip nodes were inferred by the presence or absence of the orthologs of the two human proteins making up the PPI (<i>Methods</i>). We then inferred the states of the internal nodes under the assumption that a given PPI ortholog pair could appear only once in the phylogeny (<i>Methods</i>). The topology was visualized using FigTree <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052581#pone.0052581-Rambaut1" target="_blank">[61]</a>. Branch lengths are the mean K_s value (e.g., number of synonymous substitutions per synonymous site) found across the genes surveyed for that branch of the tree (See <i>Methods</i>). The five colored branches indicate potential origin points for a PPI under our limited parsimony model (<i>Methods</i>), while the two gray branches were used to estimate the rate of PPI <i>loss</i>. The dashed branches indicate the fact the K_s values could not be distinguished for these two branches because the models used produce unrooted trees. <b>B)</b> There is an association between the age of the branch along which a PPI appears (<i>x-</i>axis; estimated via K_s above) and the average interaction degree of the proteins that make up that interaction (<i>y</i>-axis). Note that the blue distance was estimated as one-half the K_s distance between the rodent-primate and horse-dog-cow clade in the unrooted topology of (<b>A</b>). See <i>Methods</i> for details.</p

Over- and under-represented GO terms of genes present at least once in a primate-specific PPI.

a<p>Observed instances of the GO term. 1675 genes present in primate PPIs vs 7201 genes never observed in primate PPIs.</p>b<p>Expected number of occurrences among an randomly-selected set of genes of the same size.</p>c<p><i>P</i>-values for the test of the hypothesis of no difference between the observed and expected number of occurrences of the term after a Bonferonni multiple-test correction.</p>d<p>Term was <i>under-represented</i> among the primate-specific PPIs.</p

Connectivity statistics of genes involved in primate PPIs vs genes part of nonprimate PPIs.

a<p>Set of genes involved <i>only</i> in primate-specific interactions.</p>b<p>All genes not in (<i>a</i>).</p>c<p>Wilcoxon test.</p

Paired cases of relaxed selective constraints for PPI pairs.

<p>For each clade in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052581#pone-0052581-g001" target="_blank">Figure 1</a>, we plot the number of cases where both members have either ρ>1.0 (<b>A</b>) or >0.5 (<b>B</b>). <i>P</i>-values are shown for the test of the hypothesis that there are more such shared cases of relaxed constraint than would be expected by chance (χ² test, <i>Methods</i>). Cases where no <i>P</i>-value is shown had too few observations of ρ>5 for valid statistical conclusions to be drawn.</p

Over- and under-represented GO terms of genes present in PPIs where proteins in the protein pair have ω>0.5 for both branches vs remaining 4506 genes.

a<p>Observed instances of the GO term. 524 genes with ω>0.5 for both branches vs remaining 4506 genes (of 5030 genes in total from 12472 PPIs for which mirrortrees could be constructed with reliable ML scores).</p>b<p>Expected number of occurrences among an randomly-selected set of genes of the same size.</p>c<p>Uncorrected <i>P</i>-value for the test of the hypothesis of no difference between the observed and expected number of occurrences of the term.</p