bioRxivMicrobiology
Functional redundancy secures resilience of chain elongation communities upon pH shifts in closed bioreactor ecosystems
B. Liu, H. Straeuber, F. Centler, H. Harms, U. Nunes da Rocha, S. Kleinsteuber
Abstract
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.
- assay: metagenomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
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