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Transcriptome profiling during a natural host-parasite interaction.

ABSTRACT: BACKGROUND:Infection outcome in some coevolving host-pathogens is characterised by host-pathogen genetic interactions, where particular host genotypes are susceptible only to a subset of pathogen genotypes. To identify candidate genes responsible for the infection status of the host, we exposed a Daphnia magna host genotype to two bacterial strains of Pasteuria ramosa, one of which results in infection, while the other does not. At three time points (four, eight and 12 h) post pathogen exposure, we sequenced the complete transcriptome of the hosts using RNA-Seq (Illumina). RESULTS:We observed a rapid and transient response to pathogen treatment. Specifically, at the four-hour time point, eight genes were differentially expressed. At the eight-hour time point, a single gene was differentially expressed in the resistant combination only, and no genes were differentially expressed at the 12-h time point. CONCLUSIONS:We found that pathogen-associated transcriptional activity is greatest soon after exposure. Genome-wide resistant combinations were more likely to show upregulation of genes, while susceptible combinations were more likely to be downregulated, relative to controls. Our results also provide several novel candidate genes that may play a pivotal role in determining infection outcomes.

SUBMITTER: McTaggart SJ

PROVIDER: S-EPMC4551569 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Transcriptome profiling during a natural host-parasite interaction.

McTaggart Seanna J SJ Cézard Timothée T Garbutt Jennie S JS Wilson Phil J PJ Little Tom J TJ

BMC genomics 20150828

<h4>Background</h4>Infection outcome in some coevolving host-pathogens is characterised by host-pathogen genetic interactions, where particular host genotypes are susceptible only to a subset of pathogen genotypes. To identify candidate genes responsible for the infection status of the host, we exposed a Daphnia magna host genotype to two bacterial strains of Pasteuria ramosa, one of which results in infection, while the other does not. At three time points (four, eight and 12 h) post pathogen e ...[more]

PMID: 26311167

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Project description:BackgroundHost-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases.ObjectiveMany studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose.MethodsThe various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed.ResultWe discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information.ConclusionThis review presents useful knowledge and future directions on the subject matter.

Project description:BackgroundAmong more than 20,000 species of hermaphroditic trematodes, Schistosomatidae are unusual since they have evolved gonochorism. In schistosomes, sex is determined by a female heterogametic system, but phenotypic sexual dimorphism appears only after infection of the vertebrate definitive host. The completion of gonad maturation occurs even later, after pairing. To date, the molecular mechanisms that trigger the sexual differentiation in these species remain unknown, and in vivo studies on the developing schistosomulum stages are lacking. To study the molecular basis of sex determination and sexual differentiation in schistosomes, we investigated the whole transcriptome of the human parasite Schistosoma mansoni in a stage- and sex-comparative manner.Methodology/ principal findingsWe performed a RNA-seq on males and females for five developmental stages: cercariae larvae, three in vivo schistosomulum stages and adults. We detected 7,168 genes differentially expressed between sexes in at least one of the developmental stages, and 4,065 of them were functionally annotated. Transcriptome data were completed with H3K27me3 histone modification analysis using ChIP-Seq before (in cercariae) and after (in adults) the phenotypic sexual dimorphism appearance. In this paper we present (i) candidate determinants of the sexual differentiation, (ii) sex-biased players of the interaction with the vertebrate host, and (iii) different dynamic of the H3K27me3 histone mark between sexes as an illustration of sex-biased epigenetic landscapes.Conclusions/ significanceOur work presents evidence that sexual differentiation in S. mansoni is accompanied by distinct male and female transcriptional landscapes of known players of the host-parasite crosstalk, genetic determinants and epigenetic regulators. Our results suggest that such combination could lead to the optimized sexual dimorphism of this parasitic species. As S. mansoni is pathogenic for humans, this study represents a promising source of therapeutic targets, providing not only data on the parasite development in interaction with its vertebrate host, but also new insights on its reproductive function.

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Transcriptome profiling during a natural host-parasite interaction.

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Transcriptome profiling during a natural host-parasite interaction.

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