Login / Signup

Parasite-microbiota interactions potentially affect intestinal communities in wild mammals.

Tuomas AiveloAnna Norberg
Published in: The Journal of animal ecology (2017)
Detecting interaction between species is notoriously difficult, and disentangling species associations in host-related gut communities is especially challenging. Nevertheless, due to contemporary methods, including metabarcoding and 16S sequencing, collecting observational data on community composition has become easier and much more common. We studied the previously collected datasets of intestinal bacterial microbiota and parasite compositions within longitudinally followed mouse lemurs by analysing the potential interactions with diversity metrics and novel joint species distribution modelling. Both methods showed statistical association between certain parasite species and bacterial microbiota composition. Unicellular Eimeria sp. had an effect on diversity of gut microbiota. The cestode Hymenolepis diminuta had negative associations with several bacterial orders, whereas closely related species Hymenolepis nana had positive associations with several bacterial orders. Our results reveal potential interactions between some, but not all, intestinal parasites and gut bacterial microbiota. Host variables contributed over half of the total variation explained with the model, and sex was the most important single host variable; especially with microbiota, there were sex-related differences in the community composition. This study shows how joint species distribution modelling can incorporate both within-host dynamics of several taxa and host characteristics to model potential interactions in intestinal community. These results provide new hypothesis for interactions between and among parasites and bacterial microbiota to be tested further with experimental studies.
Keyphrases
  • plasmodium falciparum
  • healthcare
  • mental health
  • genetic diversity
  • genome wide
  • human health
  • dna methylation
  • climate change
  • life cycle
  • data analysis