Dataset Information

The Microbiome of the Maculinea-Myrmica Host-Parasite Interaction.

ABSTRACT: Maculinea (=Phengaris) are endangered butterflies that are characterized by a very complex biological cycle. Maculinea larvae behave as obligate parasites whose survival is strictly dependent on both particular food plants and species-specific Myrmica ants. In this interaction, Maculinea caterpillars induce Myrmica workers to retrieve and rear them in the nest by chemical and acoustic deception. Social insect symbiotic microorganisms play a key role in intraspecific and interspecific communication; therefore, it is possible that the Maculinea caterpillar microbiome might be involved in the chemical cross-talk by producing deceptive semiochemicals for host ants. To address this point, the microbiota of Maculinea alcon at different larval stages (phytophagous early larvae, intermediate larvae, carnivorous late larvae) was analyzed by using 16S rRNA-guided metabarcoding approach and compared to that of the host ant Myrmica scabrinodis. Structural and deduced functional profiles of the microbial communities were recorded, which were used to identify specific groups of microorganisms that may be involved in the chemical cross-talk. One of the most notable features was the presence in all larval stages and in the ants of two bacteria, Serratia marcescens and S. entomophila, which are involved in the chemical cross-talk between the microbes and their hosts.

SUBMITTER: Di Salvo M

PROVIDER: S-EPMC6541603 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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The Microbiome of the Maculinea-Myrmica Host-Parasite Interaction.

Di Salvo Marco M Calcagnile Matteo M Talà Adelfia A Tredici Salvatore Maurizio SM Maffei Massimo E ME Schönrogge Karsten K Barbero Francesca F Alifano Pietro P

Scientific reports 20190529 1

Maculinea (=Phengaris) are endangered butterflies that are characterized by a very complex biological cycle. Maculinea larvae behave as obligate parasites whose survival is strictly dependent on both particular food plants and species-specific Myrmica ants. In this interaction, Maculinea caterpillars induce Myrmica workers to retrieve and rear them in the nest by chemical and acoustic deception. Social insect symbiotic microorganisms play a key role in intraspecific and interspecific communicati ...[more]

PMID: 31142780

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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.

Dataset Information

The Microbiome of the Maculinea-Myrmica Host-Parasite Interaction.

Publications

The Microbiome of the Maculinea-Myrmica Host-Parasite Interaction.

Similar Datasets

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