Moejes, F.W.; Succurro, A.; Popa, O.; Maguire, J.; Ebenhöh, O. Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures. Processes2017, 5, 77.
Moejes, F.W.; Succurro, A.; Popa, O.; Maguire, J.; Ebenhöh, O. Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures. Processes 2017, 5, 77.
Moejes, F.W.; Succurro, A.; Popa, O.; Maguire, J.; Ebenhöh, O. Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures. Processes2017, 5, 77.
Moejes, F.W.; Succurro, A.; Popa, O.; Maguire, J.; Ebenhöh, O. Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures. Processes 2017, 5, 77.
Abstract
The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures, prone to bio-contamination. However, little is known about the identity of the invading organisms. To reduce the complexity of natural systems, we systematically investigated the microbiome of non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results revealed a dynamic bacterial community that developed differently in “complete” and “minimal” media conditions. In complete media, we observed an accelerated “culture crash”, indicating a more stable culture in minimal media. The identification of only four bacterial families as major players within the microbiome suggests specific roles depending on environmental conditions. From our results we propose a network of putative interactions between P. tricornutum and these main bacterial factions. We demonstrate that, even with rather sparse data, a mathematical model can be reconstructed that qualitatively reproduces the observed population dynamics, thus indicating that our hypotheses regarding the molecular interactions are in agreement with experimental data. Whereas the model in its current state is only qualitative, we argue that it serves as a starting point to develop quantitative and predictive mathematical models, which may guide experimental efforts to synthetically construct and monitor stable communities required for robust upscaling strategies.
Biology and Life Sciences, Immunology and Microbiology
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