Prediction of protein-protein interactions between Helicobacter pylori and a human host

A lack of information on protein-protein interactions at the host-pathogen interface is impeding the understanding of the pathogenesis process. A recently developed, homology search-based method to predict protein-protein interactions is applied to the gastric pathogen, Helicobacter pylori to predict the interactions between proteins of H. pylori and human proteins in vitro. Many of the predicted interactions could potentially occur between the pathogen and its human host during pathogenesis as we focused mainly on the H. pylori proteins that have a transmembrane region or are encoded in the pathogenic island and those which are known to be secreted into the human host. By applying the homology search approach to protein-protein interaction databases DIP and iPfam, we could predict in vitro interactions for a total of 623 H. pylori proteins with 6559 human proteins. The predicted interactions include 549 hypothetical proteins of as yet unknown function encoded in the H. pylori genome and 13 experimentally verified secreted proteins. We have recognized 833 interactions involving the extracellular domains of transmembrane proteins of H. pylori. Structural analysis of some of the examples reveals that the interaction predicted by us is consistent with the structural compatibility of binding partners. Examples of interactions with discernible biological relevance are discussed.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

Spatial ecology of food webs : herbivore-parasitoid communities on the pedunculate oak

Herbivorous insects, their host plants and natural enemies form the largest and most species-rich communities on earth. But what forces structure such communities? Do they represent random collections of species, or are they assembled by given rules? To address these questions, food webs offer excellent tools. As a result of their versatile information content, such webs have become the focus of intensive research over the last few decades. In this thesis, I study herbivore-parasitoid food webs from a new perspective: I construct multiple, quantitative food webs in a spatially explicit setting, at two different scales. Focusing on food webs consisting of specialist herbivores and their natural enemies on the pedunculate oak, Quercus robur, I examine consistency in food web structure across space and time, and how landscape context affects this structure. As an important methodological development, I use DNA barcoding to resolve potential cryptic species in the food webs, and to examine their effect on food web structure. I find that DNA barcoding changes our perception of species identity for as many as a third of the individuals, by reducing misidentifications and by resolving several cryptic species. In terms of the variation detected in food web structure, I find surprising consistency in both space and time. From a spatial perspective, landscape context leaves no detectable imprint on food web structure, while species richness declines significantly with decreasing connectivity. From a temporal perspective, food web structure remains predictable from year to year, despite considerable species turnover in local communities. The rate of such turnover varies between guilds and species within guilds. The factors best explaining these observations are abundant and common species, which have a quantitatively dominant imprint on overall structure, and suffer the lowest turnover. By contrast, rare species with little impact on food web structure exhibit the highest turnover rates. These patterns reveal important limitations of modern metrics of quantitative food web structure. While they accurately describe the overall topology of the web and its most significant interactions, they are disproportionately affected by species with given traits, and insensitive to the specific identity of species. As rare species have been shown to be important for food web stability, metrics depicting quantitative food web structure should then not be used as the sole descriptors of communities in a changing world. To detect and resolve the versatile imprint of global environmental change, one should rather use these metrics as one tool among several.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic bacteria

Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases. DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 - Escherichia coli and phage lambda - E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions. (C) 2011 Elsevier B.V. All rights reserved.

Salmonella enterica serovars Typhimurium and Typhi as model organisms Revealing paradigm of host-pathogen interactions

The lifestyle of intracellular pathogens has always questioned the skill of a microbiologist in the context of finding the permanent cure to the diseases caused by them. The best tool utilized by these pathogens is their ability to reside inside the host cell, which enables them to easily bypass the humoral immunity of the host, such as the complement system. They further escape from the intracellular immunity, such as lysosome and inflammasome, mostly by forming a protective vacuole-bound niche derived from the host itself. Some of the most dreadful diseases are caused by these vacuolar pathogens, for example, tuberculosis by Mycobacterium or typhoid fever by Salmonella. To deal with such successful pathogens therapeutically, the knowledge of a host-pathogen interaction system becomes primarily essential, which further depends on the use of a model system. A well characterized pathogen, namely Salmonella, suits the role of a model for this purpose, which can infect a wide array of hosts causing a variety of diseases. This review focuses on various such aspects of research on Salmonella which are useful for studying the pathogenesis of other intracellular pathogens.

From Workstations to Workbenches: Towards Predicting Physicochemically Viable Protein-Protein Interactions Across a Host and a Pathogen

The understanding of protein-protein interactions is indispensable in comprehending most of the biological processes in a cell. Small-scale experiments as well as large-scale high-throughput techniques over the past few decades have facilitated identification and analysis of protein-protein interactions which form the basis of much of our knowledge on functional and regulatory aspects of proteins. However, such rich catalog of interaction data should be used with caution when establishing protein-protein interactions in silico, as the high-throughput datasets are prone to false positives. Numerous computational means developed to pursue genome-wide studies on protein-protein interactions at times overlook the mechanistic and molecular details, thus questioning the reliability of predicted protein-protein interactions. We review the development, advantages, and shortcomings of varied approaches and demonstrate that by providing a structural viewpoint in terms of shape complementarity and interaction energies at protein-protein interfaces coupled with information on expression and localization of proteins homologous to an interacting pair, it is possible to assess the credibility of predicted interactions in biological context. With a focus on human pathogen Mycobacterium tuberculosis H37Rv, we show that such scrupulous use of details at the molecular level can predict physicochemically viable protein-protein interactions across host and pathogen. Such predicted interactions have the potential to provide molecular basis of probable mechanisms of pathogenesis and hence open up ways to explore their usefulness as targets in the light of drug discovery. (c) 2014 IUBMB Life, 66(11):759-774, 2014

A coat of many scents: Cuticular hydrocarbons in multitrophic interactions of fig wasps with ants

The fig fig wasp system of Ficus racemosa constitutes an assemblage of galler and parasitoid wasps in which tritrophic interactions occur. Since predatory ants (Oecophylla smaragdina and Technomyrmex albipes) or mostly trophobiont-tending ants (Myrmicaria brunnea) were previously shown to differentially use volatile organic compounds (VOCs) from figs as proximal cues for predation on fig wasps, we examined the response of these ants to the cuticular hydrocarbons (CHCs) of the wasps. CHC signatures of gallers were distinguished from those of parasitoids by the methyl-branched alkanes 5-methylpentacosane and 13-methylnonacosane which characterised trophic group membership. CHC profiles of wasp predator and wasp prey were congruent suggesting that parasitoids acquire CHCs from their prey; the CHC composition of the parasitoid Apocrypta sp 2 clustered with that of its galler host Apocryptophagus fusca, while the CHC profile of the parasitoid Apocryptophagus agraensis clustered with its galler prey, the fig pollinator Ceratosolen fusciceps. In behavioural assays with ants, parasitoid CHC extracts evoked greater response in all ant species compared to galler extracts, suggesting that parasitoid CHC extracts contain more elicitors of ant behaviour than those of plant feeders. CHCs of some wasp species did not elicit significant responses even in predatory ants, suggesting chemical camouflage. Contrary to earlier studies which demonstrated that predatory ants learned to associate wasp prey with specific fig VOCs, prior exposure to fig wasp CHCs did not affect the reaction of any ant species to these CHCs. (C) 2015 Elsevier Masson SAS. All rights reserved.

HOST-GUEST INTERACTIONS OF THIAMINE WITH ANIONS - CRYSTAL-STRUCTURE OF THIAMINE IODIDE SESQUIHYDRATE

The crystal structure of thiamine iodide sesquihydrate has been determined by X-ray diffraction methods as a host-guest model for coenzyme-substrate interactions. The asymmetric unit contains two chemical units. Both the thiamine molecules A and B, which are crystallographically independent, assume the usual F conformation and have a disordered hydroxyethyl side chain. An iodide anion (or a water molecule) bridges the pyrimidine and thiazolium rings of molecule A (or B) by forming a hydrogen bond with the amino group and an electrostatic contact with the thiazolium ring to stabilize the molecular conformation. In the crystal the thiamine molecules self-associate to form a pipe-like polymeric structure, in which four thiamine hosts surround an iodide guest and hold it through C(2)-H...I hydrogen bonds and thiazolium...I electrostatic interactions. Crystal data: C12H17N4OS+.I- . 1.5 H2O, monoclinic, P2(1)/c, a = 12.585(2), b = 25.303(5), c = 12.030(2) angstrom, beta = 115.15(1)degrees, V = 3468(1) angtrom3, Z = 8, D(c) = 1.606 g cm-3, R = 0.045 for 3328 observed reflections.

The impact of host-microbe interactions on murine colonic secretomotor function

The overall objective of this thesis was to gain further insight into the mechanisms underlying commensal microbial influences on intestinal ion transport. In this regard, I examined the impact of commensal host-microbe interactions on colonic secretomotor function in mouse. I first examined the influence of two different probiotic (microorganisms which, when given in adequate amounts, can confer health benefits upon the host) strains, Bifidobacterium infantis 35624 and L. salivarius UCC118 on active colonic ion transport in the mouse, using the Ussing Chamber. I found that both probiotics appear to have converging effects on ion transport at a functional level. However, L. salivarius UCC118 may preferentially inhibit neurally-evoked ion transport. Next I examined the impact of the host microbiota itself on both baseline and stimulated colonic secretomotor function as well as probiotic induced changes in ion transport. I provide further evidence that the microbiota is capable of mediating alterations in colonic ion transport, and specifically suggests that it can influence cAMP-mediated responses. Finally, it has been well documented that many probiotics elicit their effects via secreted bioactives, therefore, I studied the effects of microbially produced GABA, contained in supernatants from the commensal microbe Lactobacillus brevis DPC6108, on colonic secretomotor function. In conclusion, I believe that commensal microbes have an important and strain specific functional influence on colonic ion transport and secretomotor function and these effects can be mediated via extracellular bioactives. Moreover, I believe that functional ex-vivo studies such as those carried out in this thesis have a critical role to play in our future understanding of host-microbe interactions in the gut.

The relevance of bacterial isoprenoid biosynthetic pathways for host-microbe interactions

This thesis was undertaken to investigate the relevance of two bacterial isoprenoid biosynthetic pathways (Mevalonate (MVAL) and 2-C-methyl-D-erythritol 4-phosphate (MEP)) for host-microbe interactions. We determined a significant reduction in microbial diversity in the murine gut microbiota (by next generation sequencing) following oral administration of a common anti-cholesterol drug Rosuvastatin (RSV) that targets mammalian and bacterial HMG-CoA reductase (HMG-R) for inhibition of MVAL formation. In tandem we identified significant hepatic and intestinal off-target alterations to the murine metabolome indicating alterations in inflammation, bile acid profiles and antimicrobial peptide synthesis with implications on community structure of the gastrointestinal microbiota in statin-treated animals. However we found no effect on local Short Chain Fatty Acid biosynthesis (metabolic health marker in our model). We demonstrated direct inhibition of bacterial growth in-vitro by RSV which correlated with reductions in bacterial MVAL formation. However this was only at high doses of RSV. Our observations demonstrate a significant RSV-associated impact on the gut microbiota prompting similar human analysis. Successful deletion of another MVAL pathway enzyme (HMG-CoA synthase (mvaS)) involved in Listeria monocytogenes EGDe isoprenoid biosynthesis determined that the enzyme is non-essential for normal growth and in-vivo pathogenesis of this pathogen. We highlight potential evidence for alternative means of synthesis of the HMG-CoA substrate that could render mvaS activity redundant under our test conditions. Finally, we showed by global gene expression analysis (Massive Analysis of cDNA Ends (MACE RNA-seq) a significant role for the penultimate MEP pathway metabolite (E)-4-hydroxy-3-methyl-2-but-2-enyl pyrophosphate (HMBPP) in significant up regulation of genes of immunity and antigen presentation in THP-1 cells at nanomolar levels. We infected THP-1 cells with wild type or HMBPP under/over-producing L. monoctyogenes EGDe mutants and determined subtle effects of HMBPP upon overall host responses to Listeria infection. Overall our findings provide greater insights regarding bacterial isoprenoid biosynthetic pathways for host-microbe/microbe-host dialogue.

Cryptococcal cell morphology affects host cell interactions and pathogenicity.

Cryptococcus neoformans is a common life-threatening human fungal pathogen. The size of cryptococcal cells is typically 5 to 10 microm. Cell enlargement was observed in vivo, producing cells up to 100 microm. These morphological changes in cell size affected pathogenicity via reducing phagocytosis by host mononuclear cells, increasing resistance to oxidative and nitrosative stress, and correlated with reduced penetration of the central nervous system. Cell enlargement was stimulated by coinfection with strains of opposite mating type, and ste3aDelta pheromone receptor mutant strains had reduced cell enlargement. Finally, analysis of DNA content in this novel cell type revealed that these enlarged cells were polyploid, uninucleate, and produced daughter cells in vivo. These results describe a novel mechanism by which C. neoformans evades host phagocytosis to allow survival of a subset of the population at early stages of infection. Thus, morphological changes play unique and specialized roles during infection.

Live Imaging of Host-Parasite Interactions in a Zebrafish Infection Model Reveals Cryptococcal Determinants of Virulence and Central Nervous System Invasion.

UNLABELLED: The human fungal pathogen Cryptococcus neoformans is capable of infecting a broad range of hosts, from invertebrates like amoebas and nematodes to standard vertebrate models such as mice and rabbits. Here we have taken advantage of a zebrafish model to investigate host-pathogen interactions of Cryptococcus with the zebrafish innate immune system, which shares a highly conserved framework with that of mammals. Through live-imaging observations and genetic knockdown, we establish that macrophages are the primary immune cells responsible for responding to and containing acute cryptococcal infections. By interrogating survival and cryptococcal burden following infection with a panel of Cryptococcus mutants, we find that virulence factors initially identified as important in causing disease in mice are also necessary for pathogenesis in zebrafish larvae. Live imaging of the cranial blood vessels of infected larvae reveals that C. neoformans is able to penetrate the zebrafish brain following intravenous infection. By studying a C. neoformans FNX1 gene mutant, we find that blood-brain barrier invasion is dependent on a known cryptococcal invasion-promoting pathway previously identified in a murine model of central nervous system invasion. The zebrafish-C. neoformans platform provides a visually and genetically accessible vertebrate model system for cryptococcal pathogenesis with many of the advantages of small invertebrates. This model is well suited for higher-throughput screening of mutants, mechanistic dissection of cryptococcal pathogenesis in live animals, and use in the evaluation of therapeutic agents. IMPORTANCE: Cryptococcus neoformans is an important opportunistic pathogen that is estimated to be responsible for more than 600,000 deaths worldwide annually. Existing mammalian models of cryptococcal pathogenesis are costly, and the analysis of important pathogenic processes such as meningitis is laborious and remains a challenge to visualize. Conversely, although invertebrate models of cryptococcal infection allow high-throughput assays, they fail to replicate the anatomical complexity found in vertebrates and, specifically, cryptococcal stages of disease. Here we have utilized larval zebrafish as a platform that overcomes many of these limitations. We demonstrate that the pathogenesis of C. neoformans infection in zebrafish involves factors identical to those in mammalian and invertebrate infections. We then utilize the live-imaging capacity of zebrafish larvae to follow the progression of cryptococcal infection in real time and establish a relevant model of the critical central nervous system infection phase of disease in a nonmammalian model.

Fitting host-parasitoid models with CV² > 1 using hierarchical generalized linear models

The powerful general Pacala-Hassell host-parasitoid model for a patchy environment, which allows host density–dependent heterogeneity (HDD) to be distinguished from between-patch, host density–independent heterogeneity (HDI), is reformulated within the class of the generalized linear model (GLM) family. This improves accessibility through the provision of general software within well–known statistical systems, and allows a rich variety of models to be formulated. Covariates such as age class, host density and abiotic factors may be included easily. For the case where there is no HDI, the formulation is a simple GLM. When there is HDI in addition to HDD, the formulation is a hierarchical generalized linear model. Two forms of HDI model are considered, both with between-patch variability: one has binomial variation within patches and one has extra-binomial, overdispersed variation within patches. Examples are given demonstrating parameter estimation with standard errors, and hypothesis testing. For one example given, the extra-binomial component of the HDI heterogeneity in parasitism is itself shown to be strongly density dependent.