Correlating methane production to microbiota in anaerobic digesters fed synthetic wastewater

Autor: Daniel Zitomer, Michael T. Johnson, Kaushik Venkiteshwaran, Kim Milferstedt, Jérôme Hamelin, Masanori Fujimoto
Přispěvatelé: Department of Civil, Construction and Environmental Engineering, Marquette University, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign [Urbana], University of Illinois System-University of Illinois System, Marquette University [Milwaukee], Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)
Jazyk: angličtina
Rok vydání: 2017
Předmět:
0301 basic medicine
anaerobic digestion
multiple linear regression
digesteur anaérobie
Environmental Engineering
[SDV]Life Sciences [q-bio]
030106 microbiology
Biomass
digestion anaérobie
microbial community composition
quantitative structure activity relationship
Wastewater
010501 environmental sciences
Biology
01 natural sciences
03 medical and health sciences
Bioreactors
microbiote
RNA
Ribosomal
16S
bioindicator
Anaerobiosis
Waste Management and Disposal
Relative species abundance
0105 earth and related environmental sciences
Water Science and Technology
Civil and Structural Engineering
bioindicateur
communauté microbienne
amplicon sequencing
Microbiota
Ecological Modeling
régression linéaire multiple
Environmental engineering
structure activity relationships
Pulp and paper industry
Pollution
6. Clean water
relation structure activité
Anaerobic digestion
Microbial population biology
production de méthane
Volatile suspended solids
Species evenness
microbial community
Methane
Anaerobic exercise
Zdroj: Water Research
Water Research, IWA Publishing, 2017, 110, pp.161-169. ⟨10.1016/j.watres.2016.12.010⟩
ISSN: 0043-1354
DOI: 10.1016/j.watres.2016.12.010⟩
Popis: A quantitative structure activity relationship (QSAR) between relative abundance values and digester methane production rate was developed. For this, 50 triplicate anaerobic digester sets (150 total digesters) were each seeded with different methanogenic biomass samples obtained from full-scale, engineered methanogenic systems. Although all digesters were operated identically for at least 5 solids retention times (SRTs), their quasi steady-state function varied significantly, with average daily methane production rates ranging from 0.09 ± 0.004 to 1 ± 0.05 L-CH4/LR-day (LR = Liter of reactor volume) (average ± standard deviation). Digester microbial community structure was analyzed using more than 4.1 million partial 16S rRNA gene sequences of Archaea and Bacteria. At the genus level, 1300 operational taxonomic units (OTUs) were observed across all digesters, whereas each digester contained 158 ± 27 OTUs. Digester function did not correlate with typical biomass descriptors such as volatile suspended solids (VSS) concentration, microbial richness, diversity or evenness indices. However, methane production rate did correlate notably with relative abundances of one Archaeal and nine Bacterial OTUs. These relative abundances were used as descriptors to develop a multiple linear regression (MLR) QSAR equation to predict methane production rates solely based on microbial community data. The model explained over 66% of the variance in the experimental data set based on 149 anaerobic digesters with a standard error of 0.12 L-CH4/LR-day. This study provides a framework to relate engineered process function and microbial community composition which can be further expanded to include different feed stocks and digester operating conditions in order to develop a more robust QSAR model.
Databáze: OpenAIRE
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