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Centler, F.

Publications and source records attributed to Centler, F..

2 recordsLinked to original sources

Functional redundancy secures resilience of chain elongation communities upon pH shifts in closed bioreactor ecosystems

For anaerobic mixed cultures performing microbial chain elongation, it is unclear how pH alterations affect the abundance of key players, microbial interactions and community functioning in terms of medium-chain carboxylate yields. We explored pH effects on mixed cultures enriched in continuous anaerobic bioreactors representing closed model ecosystems. Increasing the pH from 5.5 to 6.0 caused fluctuations in community composition and yields of n-butyrate, n-caproate and n-caprylate. Further pH increase to 6.5 restored previous yield values while the community entered a new state characterized by lower diversity and evenness but apparently higher richness, indicating the presence of species below the detection threshold in the previous state. We applied Aitchison PCA clustering, linear mixed-effects models and random forest classification on our datasets. Different pH preferences of two key chain elongation species - one Clostridium IV species related to Ruminococcaceae bacterium CPB6, and one Clostridium sensu stricto species related to Clostridium luticellarii - were determined. Based on relative abundances measured by 16S rRNA amplicon sequencing, network analysis revealed positive correlations of Clostridium IV with lactic acid bacteria, which switched from Olsenella to Lactobacillus along the pH increase, illustrating the plasticity of the food web in chain elongation communities. The pH increase induced dramatic shifts in community composition whereas process performance (in terms of product range and yields) returned to the previous state. Despite long-term cultivation in closed systems over the pH shift experiment, the communities retained functional redundancy in fermentation pathways, reflected by the emergence of rare species and concomitant recovery of chain elongation functions.

microbiology↗

{micro}bialSim: constraint-based dynamic simulation of complex microbiomes

Microbial communities are pervasive in the natural environment, associated with many animal hosts, and of increasing importance in biotechnological applications. The complexity of these microbial systems makes the underlying mechanisms driving their dynamics difficult to identify. While experimental meta-OMICS techniques are routinely applied to record the inventory and activity of microbiomes over time, it remains difficult to obtain quantitative predictions based on such data. Mechanistic, quantitative mathematical modeling approaches hold the promise to both provide predictive power and shed light on cause-effect relationships driving these dynamic systems. We introduce {micro}bialSim (pronounced \"microbialsim\"), a dynamic Flux-Balance-Analysis-based (dFBA) numerical simulator which is able to predict the time course in terms of composition and activity of microbiomes containing 100s of species in batch or chemostat mode. Activity of individual species is simulated by using separate FBA models which have access to a common pool of compounds, allowing for metabolite exchange. A novel augmented forward Euler method ensures numerically accuracy by temporarily reducing the time step size when compound concentrations decrease rapidly due to high compound affinities and/or the presence of many consuming species. We present three exemplary applications of {micro}bialSim: a batch culture of a hydrogenotrophic archaeon, a syntrophic methanogenic biculture, and a 773-species human gut microbiome which exhibits a complex and dynamic pattern of metabolite exchange.\n\nFocussing on metabolite exchange as the main interaction type, {micro}bialSim allows for the mechanistic simulation of microbiomes at their natural complexity. Simulated trajectories can be used to contextualize experimental meta-OMICS data, and hypotheses on cause-effect relationships driving community dynamics can be derived based on scenario simulations.\n\n{micro}bialSim is implemented in Matlab and relies on the COBRA Toolbox or CellNetAnalyzer for FBA calculations. The source code is available under the GNU General Public License v3.0 at https://git.ufz.de/UMBSysBio/microbialsim.

systems biology↗