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Disentangling Community Structure of Ecological System in Activated Sludge: Core Communities, Functionality, and
Huibin Chen1, Meiying Wang1, Sheng Chang2
1School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada.
Microbial Ecology
|February 21, 2020
Summary
This study characterized activated sludge microbial communities using sequencing and analysis. The core genera identified were sufficient to predict ecosystem functions, revealing limited functional redundancy.
Area of Science:
- Environmental Microbiology
- Wastewater Treatment
- Microbial Ecology
Background:
- Activated sludge (AS) microbial communities are crucial for wastewater treatment efficiency.
- Understanding core microbial structures and functions in AS is vital for process optimization.
Purpose of the Study:
- To characterize the microbial community structure and functional pathways in activated sludge from extended aeration processes.
- To investigate functional redundancy within the AS ecosystem.
- To assess the predictive power of core microbial communities for AS functionality.
Main Methods:
- Illumina MiSeq sequencing was used to analyze the microbial community composition of AS samples.
- PICRUSt analysis was employed to predict functional pathways from 16S rDNA data.
- Operational Taxonomic Unit (OTU) distribution was used to identify core microbial genera.
Main Results:
- 125 core microbial genera were identified, with 3 highly abundant and 21 intermittently abundant.
- Key genera involved in nitrification, denitrification, phosphorus accumulation, and bulking were identified.
- High-abundance nitrogen metabolic pathways included nitrate reduction and denitrification; ammonia oxidation genes were scarce.
- A strong correlation between community composition and functional similarity indicated limited functional redundancy.
- The dominant core genera community was sufficient to characterize AS functionality.
Conclusions:
- The core microbial community structure in AS is a reliable indicator of its functional capacity.
- 16S rDNA sequencing combined with PICRUSt analysis can effectively predict AS ecosystem functions.
- Findings suggest potential for optimizing wastewater treatment processes by targeting specific core microbial communities.
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