Integrating independent microbial studies to build predictive models of anaerobic digestion inhibition by ammonia and phenol. (November 2020)

• Record Type: Journal Article
• Authors: Poirier, Simon; Déjean, Sébastien; Midoux, Cédric; Lê Cao, Kim-Anh; Chapleur, Olivier
• Is Part Of: Bioresource technology. Volume 316(2020)
• Publication Date: 2020-11
• Subjects: Methane -- Ammonia -- Phenol -- Microbial indicator -- Inhibition prediction; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2020.123952
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Robust biomarkers of AD inhibition by ammonia and phenol were tagged by integrating independent 16S studies. Multivariate model predicts ammonia inhibition with 90% accuracy in 2 external studies. Increase of the Clostridiales relative abundance is an early warning of AD inhibition. Presence of Cloacimonetes is associated with good performance of methane production. Abstract: Anaerobic digestion (AD) is a process that can efficiently degrade organic waste into renewable energies. AD failure is however common as the underpinning microbial mechanisms are highly vulnerable to a wide range of inhibitory compounds. Sequencing technologies enable the identification of microbial indicators of digesters inhibition, but existing studies are limited. They used different inocula, substrates, sites and types of reactors and reported different or contradictory indicators. Our aim was to identify a robust signature of microbial indicators of phenol and ammonia inhibitions across four independent AD microbial studies. To identify such signature, we applied an original multivariate integrative method on two in-house studies, then validated our approach by predicting the inhibitory status of samples from two other studies with more than 90% accuracy. Our approach shows how we can efficiently leverage on existing studies to extract reproducible microbial community patterns and predict AD inhibition to improve AD microbial management.