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

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

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

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/

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

Cryptococcus gattii in North American Pacific Northwest: whole-population genome analysis provides insights into species evolution and dispersal

The emergence of distinct populations of Cryptococcus gattii in the temperate North American Pacific Northwest (PNW) was surprising, as this species was previously thought to be confined to tropical and semitropical regions. Beyond a new habitat niche, the dominant emergent population displayed increased virulence and caused primary pulmonary disease, as opposed to the predominantly neurologic disease seen previously elsewhere. Whole-genome sequencing was performed on 118 C. gattii isolates, including the PNW subtypes and the global diversity of molecular type VGII, to better ascertain the natural source and genomic adaptations leading to the emergence of infection in the PNW. Overall, the VGII population was highly diverse, demonstrating large numbers of mutational and recombinational events; however, the three dominant subtypes from the PNW were of low diversity and were completely clonal. Although strains of VGII were found on at least five continents, all genetic subpopulations were represented or were most closely related to strains from South America. The phylogenetic data are consistent with multiple dispersal events from South America to North America and elsewhere. Numerous gene content differences were identified between the emergent clones and other VGII lineages, including genes potentially related to habitat adaptation, virulence, and pathology. Evidence was also found for possible gene introgression from Cryptococcus neoformans var. grubii that is rarely seen in global C. gattii but that was present in all PNW populations. These findings provide greater understanding of C. gattii evolution in North America and support extensive evolution in, and dispersal from, South America. Importance: Cryptococcus gattii emerged in the temperate North American Pacific Northwest (PNW) in the late 1990s. Beyond a new environmental niche, these emergent populations displayed increased virulence and resulted in a different pattern of clinical disease. In particular, severe pulmonary infections predominated in contrast to presentation with neurologic disease as seen previously elsewhere. We employed population-level whole-genome sequencing and analysis to explore the genetic relationships and gene content of the PNW C. gattii populations. We provide evidence that the PNW strains originated from South America and identified numerous genes potentially related to habitat adaptation, virulence expression, and clinical presentation. Characterization of these genetic features may lead to improved diagnostics and therapies for such fungal infections. The data indicate that there were multiple recent introductions of C. gattii into the PNW. Public health vigilance is warranted for emergence in regions where C. gattii is not thought to be endemic

Recent advances in understanding the roles of whole genome duplications in evolution

Ancient whole-genome duplications (WGDs)—paleopolyploidy events—are key to solving Darwin’s ‘abominable mystery’ of how flowering plants evolved and radiated into a rich variety of species. The vertebrates also emerged from their invertebrate ancestors via two WGDs, and genomes of diverse gymnosperm trees, unicellular eukaryotes, invertebrates, fishes, amphibians and even a rodent carry evidence of lineage-specific WGDs. Modern polyploidy is common in eukaryotes, and it can be induced, enabling mechanisms and short-term cost-benefit assessments of polyploidy to be studied experimentally. However, the ancient WGDs can be reconstructed only by comparative genomics: these studies are difficult because the DNA duplicates have been through tens or hundreds of millions of years of gene losses, mutations, and chromosomal rearrangements that culminate in resolution of the polyploid genomes back into diploid ones (rediploidisation). Intriguing asymmetries in patterns of post-WGD gene loss and retention between duplicated sets of chromosomes have been discovered recently, and elaborations of signal transduction systems are lasting legacies from several WGDs. The data imply that simpler signalling pathways in the pre-WGD ancestors were converted via WGDs into multi-stranded parallelised networks. Genetic and biochemical studies in plants, yeasts and vertebrates suggest a paradigm in which different combinations of sister paralogues in the post-WGD regulatory networks are co-regulated under different conditions. In principle, such networks can respond to a wide array of environmental, sensory and hormonal stimuli and integrate them to generate phenotypic variety in cell types and behaviours. Patterns are also being discerned in how the post-WGD signalling networks are reconfigured in human cancers and neurological conditions. It is fascinating to unpick how ancient genomic events impact on complexity, variety and disease in modern life

Enzymatic Mechanisms Involved in Evasion of Fungi to the Oxidative Stress: Focus on Scedosporium apiospermum

The airways of patients with cystic fibrosis (CF) are frequently colonized by various filamentous fungi, mainly Aspergillus fumigatus and Scedosporium species. To establish within the respiratory tract and cause an infection, these opportunistic fungi express pathogenic factors allowing adherence to the host tissues, uptake of extracellular iron, or evasion to the host immune response. During the colonization process, inhaled conidia and the subsequent hyphae are exposed to reactive oxygen species (ROS) and reactive nitrogen species (RNS) released by phagocytic cells, which cause in the fungal cells an oxidative stress and a nitrosative stress, respectively. To cope with these constraints, fungal pathogens have developed various mechanisms that protect the fungus against ROS and RNS, including enzymatic antioxidant systems. In this review, we summarize the different works performed on ROS- and RNS-detoxifying enzymes in fungi commonly encountered in the airways of CF patients and highlight their role in pathogenesis of the airway colonization or respiratory infections. The potential of these enzymes as serodiagnostic tools is also emphasized. In addition, taking advantage of the recent availability of the whole genome sequence of S. apiospermum, we identified the various genes encoding ROS- and RNS-detoxifying enzymes, which pave the way for future investigations on the role of these enzymes in pathogenesis of these emerging species since they may constitute new therapeutics targets