Back

Phytoplankton consortia as a blueprint for mutually beneficial eukaryote-bacteria ecosystems: Biocoenosis of Botryococcus consortia

Blifernez-Klassen, O.; Klassen, V.; Wibberg, D.; Cebeci, E.; Henke, C.; Rueckert, C.; Chaudhari, S.; Rupp, O.; Blom, J.; Winkler, A.; Al-Dilaimi, A.; Goesmann, A.; Sczyrba, A.; Kalinowski, J.; Braeutigam, A.; Kruse, O.

2019-12-24 ecology
10.1101/476887 bioRxiv
Show abstract

Bacteria occupy all major ecosystems and maintain an intensive relationship to the eukaryotes, developing together into complex biomes (i.e., phycosphere and rhizosphere). Interactions between eukaryotes and bacteria range from cooperative to competitive, with the associated microorganisms affecting their hosts development, growth, health and disease. Since the advent of non-culture dependent analytical techniques such as metagenome sequencing, consortia have been described but owing to the complex interactions rarely functionally dissected. Multifaceted analysis of the microbial consortium of the ancient phytoplankton Botryococcus as an attractive model food web revealed that its all abundant bacterial members belong to a distinct niche of biotin auxotrophs, essentially depending on the microalga. In addition, hydrocarbonoclastic bacteria without vitamin auxotrophies, which adversely affect the algal cell morphology, appear evidently decimated. Synthetic rearrangement of a minimal community consisting of alga, mutualistic and parasitic bacteria underpins the model of a eukaryote that domesticates its own mutualistic bacterial "zoo" to manipulate and control its surrounding biosphere. This model of domestication of mutualistic bacteria for the defense against destruents by a eukaryotic host could represent ecologically relevant interactions that cross species boundaries. Metabolic and system reconstruction disentangles the relationships and provide a blueprint for the construction of mutually beneficial synthetic ecosystems.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.