|
|
Marin, O., Gonzalez, B., & Poupin, M. J. (2021). From Microbial Dynamics to Functionality in the Rhizosphere: A Systematic Review of the Opportunities With Synthetic Microbial Communities. Front. Plant Sci., 12, 650609.
Abstract: Synthetic microbial communities (SynComs) are a useful tool for a more realistic understanding of the outcomes of multiple biotic interactions where microbes, plants, and the environment are players in time and space of a multidimensional and complex system. Toward a more in-depth overview of the knowledge that has been achieved using SynComs in the rhizosphere, a systematic review of the literature on SynComs was performed to identify the overall rationale, design criteria, experimental procedures, and outcomes of in vitro or in planta tests using this strategy. After an extensive bibliography search and a specific selection process, a total of 30 articles were chosen for further analysis, grouping them by their reported SynCom size. The reported SynComs were constituted with a highly variable number of members, ranging from 3 to 190 strains, with a total of 1,393 bacterial isolates, where the three most represented phyla were Proteobacteria, Actinobacteria, and Firmicutes. Only four articles did not reference experiments with SynCom on plants, as they considered only microbial in vitro studies, whereas the others chose different plant models and plant-growth systems; some of them are described and reviewed in this article. Besides, a discussion on different approaches (bottom-up and top-down) to study the microbiome role in the rhizosphere is provided, highlighting how SynComs are an effective system to connect and fill some knowledge gaps and to have a better understanding of the mechanisms governing these multiple interactions. Although the SynCom approach is already helpful and has a promising future, more systematic and standardized studies are needed to harness its full potential.
|
|
|
|
Orellana, D., Machuca, D., Ibeas, M. A., Estevez, J. M., & Poupin, M. J. (2022). Plant-growth promotion by proteobacterial strains depends on the availability of phosphorus and iron in Arabidopsis thaliana plants. Front. Microbiol., 13, 1083270.
Abstract: Phosphorus (as phosphate, Pi) and iron (Fe) are critical nutrients in plants that are often poorly available in the soil and can be microbially affected. This work aimed to evaluate how plant-rhizobacteria interaction changes due to different Pi or Fe nutritional scenarios and to study the underlying molecular mechanisms of the microbial modulation of these nutrients in plants. Thus, three proteobacteria (Paraburkholderia phytofirmans PsJN, Azospirillum brasilense Sp7, and Pseudomonas putida KT2440) were used to inoculate Arabidopsis seeds. Additionally, the seeds were exposed to a nutritional factor with the following levels for each nutrient: sufficient (control) or low concentrations of a highly soluble source or sufficient concentrations of a low solubility source. Then, the effects of the combinatorial factors were assessed in plant growth, nutrition, and genetic regulation. Interestingly, some bacterial effects in plants depended on the nutrient source (e.g., increased aerial zones induced by the strains), and others (e.g., decreased primary roots induced by Sp7 or KT2440) occurred regardless of the nutritional treatment. In the short-term, PsJN had detrimental effects on plant growth in the presence of the low-solubility Fe compound, but this was not observed in later stages of plant development. A thorough regulation of the phosphorus content was detected in plants independent of the nutritional treatment. Nevertheless, inoculation with KT2440 increased P content by 29% Pi-deficiency exposed plants. Conversely, the inoculation tended to decrease the Fe content in plants, suggesting a competition for this nutrient in the rhizosphere. The P-source also affected the effects of the PsJN strain in a double mutant of the phosphate starvation response (PSR). Furthermore, depending on the nutrient source, PsJN and Sp7 strains differentially regulated PSR and IAA- associated genes, indicating a role of these pathways in the observed differential phenotypical responses. In the case of iron, PsJN and SP7 regulated iron uptake-related genes regardless of the iron source, which may explain the lower Fe content in inoculated plants. Overall, the plant responses to these proteobacteria were not only influenced by the nutrient concentrations but also by their availabilities, the elapsed time of the interaction, and the specific identities of the beneficial bacteria.
|
|
|
|
Travisany, D., Goles, E., Latorre, M., Cort?s, M. P., & Maass, A. (2020). Generation and robustness of Boolean networks to model Clostridium difficile infection. Nat. Comput., 19(1), 111–134.
Abstract: One of the more common healthcare associated infection is Chronic diarrhea. This disease is caused by the bacterium Clostridium difficile which alters the normal composition of the human gut flora. The most successful therapy against this infection is the fecal microbial transplant (FMT). They displace C. difficile and contribute to gut microbiome resilience, stability and prevent further episodes of diarrhea. The microorganisms in the FMT their interactions and inner dynamics reshape the gut microbiome to a healthy state. Even though microbial interactions play a key role in the development of the disease, currently, little is known about their dynamics and properties. In this context, a Boolean network model for C. difficile infection (CDI) describing one set of possible interactions was recently presented. To further explore the space of possible microbial interactions, we propose the construction of a neutral space conformed by a set of models that differ in their interactions, but share the final community states of the gut microbiome under antibiotic perturbation and CDI. To begin with the analysis, we use the previously described Boolean network model and we demonstrate that this model is in fact a threshold Boolean network (TBN). Once the TBN model is set, we generate and use an evolutionary algorithm to explore to identify alternative TBNs. We organize the resulting TBNs into clusters that share similar dynamic behaviors. For each cluster, the associated neutral graph is constructed and the most relevant interactions are identified. Finally, we discuss how these interactions can either affect or prevent CDI.
|
|
