The genome sequence and effector complement of the flax rust pathogen Melampsora lini

Rust fungi cause serious yield reductions on crops, including wheat, barley, soybean, coffee, and represent real threats to global food security. Of these fungi, the flax rust pathogen Melampsora lini has been developed most extensively over the past 80 years as a model to understand the molecular mechanisms that underpin pathogenesis. During infection, M. lini secretes virulence effectors to promote disease. The number of these effectors, their function and their degree of conservation across rust fungal species is unknown. To assess this, we sequenced and assembled de novo the genome of M. lini isolate CH5 into 21,130 scaffolds spanning 189 Mbp (scaffold N50 of 31 kbp). Global analysis of the DNA sequence revealed that repetitive elements, primarily retrotransposons, make up at least 45% of the genome. Using ab initio predictions, transcriptome data and homology searches, we identified 16,271 putative protein-coding genes. An analysis pipeline was then implemented to predict the effector complement of M. lini and compare it to that of the poplar rust, wheat stem rust and wheat stripe rust pathogens to identify conserved and species-specific effector candidates. Previous knowledge of four cloned M. lini avirulence effector proteins and two basidiomycete effectors was used to optimize parameters of the effector prediction pipeline. Markov clustering based on sequence similarity was performed to group effector candidates from all four rust pathogens. Clusters containing at least one member from M. lini were further analyzed and prioritized based on features including expression in isolated haustoria and infected leaf tissue and conservation across rust species. Herein, we describe 200 of 940 clusters that ranked highest on our priority list, representing 725 flax rust candidate effectors. Our findings on this important model rust species provide insight into how effectors of rust fungi are conserved across species and how they may act to promote infection on their hosts.This work was funded by a grant from the CSIRO Transformational Biology Capability Platform to Adnane Nemri. Claire Anderson was supported by an ARC Discovery Grant (DP120104044) awarded to David A. Jones and Peter N. Dodds

Sequence-Based Prediction of Type III Secreted Proteins

The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of ∼71% and selectivity of ∼85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen–host interactions

Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs

<p>Abstract</p> <p>Background</p> <p>Host protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology favors excessive amounts of virus production in a stressed host cell network. Short linear peptide motifs common to both virus and host provide the basis for host network modification.</p> <p>Methods</p> <p>We focused our host-pathogen study on the binding and competing interactions of HIV-1 and human proteins. We showed that peptide motifs conserved across 70% of HIV-1 subtype B and C samples occurred in similar positions on HIV-1 proteins, and we documented protein domains that interact with these conserved motifs. We predicted which human proteins may be targeted by HIV-1 by taking pairs of human proteins that may interact via a motif conserved in HIV-1 and the corresponding interacting protein domain.</p> <p>Results</p> <p>Our predictions were enriched with host proteins known to interact with HIV-1 proteins ENV, NEF, and TAT (p-value < 4.26E-21). Cellular pathways statistically enriched for our predictions include the T cell receptor signaling, natural killer cell mediated cytotoxicity, cell cycle, and apoptosis pathways. Gene Ontology molecular function level 5 categories enriched with both predicted and confirmed HIV-1 targeted proteins included categories associated with phosphorylation events and adenyl ribonucleotide binding.</p> <p>Conclusion</p> <p>A list of host proteins highly enriched with those targeted by HIV-1 proteins can be obtained by searching for host protein motifs along virus protein sequences. The resulting set of host proteins predicted to be targeted by virus proteins will become more accurate with better annotations of motifs and domains. Nevertheless, our study validates the role of linear binding motifs shared by virus and host proteins as an important part of the crosstalk between virus and host.</p

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

Background: H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. Results: We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI. Conclusions: The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies

Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

<p>Abstract</p> <p>Background</p> <p>Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.</p> <p>Methods</p> <p>A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.</p> <p>Results</p> <p>An <it>in silico</it> system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from <it>Plasmodium falciparum</it>.</p> <p>Conclusion</p> <p>The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.</p

Modeling Virus-Host Networks

Virus-host interactions are being cataloged at an increasing rate using protein interaction assays and small interfering RNA screens for host factors necessary for infection. These interactions can be viewed as a network, where genes or proteins are nodes, and edges correspond to associations between them. Virus-host interac- tion networks will eventually support the study and treatment of infection, but first require more data and better analysis techniques. This dissertation targets these goals with three aims. The first aim tackles the lack of data by providing a method for the computational prediction of virus-host protein interactions. We show that HIV-human protein interactions can be predicted using documented human peptide motifs found to be conserved on HIV proteins from different subtypes. We find that human proteins predicted to bind to HIV proteins are enriched in both documented HIV targeted proteins and pathways known to be utilized by HIV. The second aim seeks to improve peptide motif annotation on virus proteins, starting with the dock- ing site for protein kinases ERK1 and ERK2, which phosphorylate HIV proteins during infection. We find that the docking site motif, in spite of being suggestive of phosphorylation, is not present on all HIV subtypes for some HIV proteins, and we provide evidence that two variations of the docking site motif could explain phos- phorylation. In the third aim, we analyze virus-host networks and build on the observation that viruses target host hub proteins. We show that of the two hub types, date and party, HIV and influenza virus proteins prefer to interact with the latter. The methods presented here for prediction and motif refinement, as well as the analysis of virus targeted hubs, provide a useful set of tools and hypotheses for the study of virus-host interactions

A novel bacterial l-arginine sensor controlling c-di-GMP levels in Pseudomonas aeruginosa

Nutrients such as amino acids play key roles in shaping the metabolism of microorganisms in natural environments and in host–pathogen interactions. Beyond taking part to cellular metabolism and to protein synthesis, amino acids are also signaling molecules able to influence group behavior in microorganisms, such as biofilm formation. This lifestyle switch involves complex metabolic reprogramming controlled by local variation of the second messenger 3′, 5′-cyclic diguanylic acid (c-di-GMP). The intracellular levels of this dinucleotide are finely tuned by the opposite activity of dedicated diguanylate cyclases (GGDEF signature) and phosphodiesterases (EAL and HD-GYP signatures), which are usually allosterically controlled by a plethora of environmental and metabolic clues. Among the genes putatively involved in controlling c-di-GMP levels in P. aeruginosa, we found that the multidomain transmembrane protein PA0575, bearing the tandem signature GGDEF-EAL, is an l-arginine sensor able to hydrolyse c-di-GMP. Here, we investigate the basis of arginine recognition by integrating bioinformatics, molecular biophysics and microbiology. Although the role of nutrients such as l-arginine in controlling the cellular fate in P. aeruginosa (including biofilm, pathogenicity and virulence) is already well established, we identified the first l-arginine sensor able to link environment sensing, c-di-GMP signaling and biofilm formation in this bacterium

Data integration for the analysis of uncharacterized proteins in Mycobacterium tuberculosis

Includes abstract.Includes bibliographical references (leaves 126-150).Mycobacterium tuberculosis is a bacterial pathogen that causes tuberculosis, a leading cause of human death worldwide from infectious diseases, especially in Africa. Despite enormous advances achieved in recent years in controlling the disease, tuberculosis remains a public health challenge. The contribution of existing drugs is of immense value, but the deadly synergy of the disease with Human Immunodeficiency Virus (HIV) or Acquired Immunodeficiency Syndrome (AIDS) and the emergence of drug resistant strains are threatening to compromise gains in tuberculosis control. In fact, the development of active tuberculosis is the outcome of the delicate balance between bacterial virulence and host resistance, which constitute two distinct and independent components. Significant progress has been made in understanding the evolution of the bacterial pathogen and its interaction with the host. The end point of these efforts is the identification of virulence factors and drug targets within the bacterium in order to develop new drugs and vaccines for the eradication of the disease