Time-course analysis of metabolomic and microbial responses in anaerobic digesters exposed to ammonia. (November 2021)

• Record Type: Journal Article
• Authors: Chapleur, Olivier; Poirier, Simon; Guenne, Angéline; Lê Cao, Kim-Anh
• Is Part Of: Chemosphere. Volume 283(2021)
• Publication Date: 2021-11
• Subjects: Metabolomics -- Inhibition -- Statistics -- Longitudinal data -- 16S; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2021.131309
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Omics longitudinal studies are effective experimental designs to inform on the stability and dynamics of microbial communities in response to perturbations, but time-course analytical frameworks are required to fully exploit the temporal information acquired in this context. In this study we investigate the influence of ammonia on the stability of anaerobic digestion (AD) microbiome with a new statistical framework. Ammonia can severely reduce AD performance. Understanding how it affects microbial communities development and the degradation progress is a key operational issue to propose more stable processes. Thirty batch digesters were set-up with different levels of ammonia. Microbial community structure and metabolomic profiles were monitored with 16 S-metabarcoding and GCMS (gas-chromatography-mass-spectrometry). Digesters were first grouped according to similar degradation performances. Within each group, time profiles of OTUs and metabolites were modelled, then clustered into similar time trajectories, evidencing for example a syntrophic interaction between Syntrophomonas and Methanoculleus that was maintained up to 387 mg FAN/L. Metabolites resulting from organic matter fermentation, such as dehydroabietic or phytanic acid, decreased with increasing ammonia levels. Our analytical framework enabled to fully account for time variability and integrate this parameter in data analysis. Graphical abstract: Image 1 Highlights: A novel time-course statistical framework was used for omics longitudinal data. Similar temporal dynamics between microorganisms and metabolites were identified. Successive growth of guilds of microorganisms across time was revealed. Microbial dynamics under different ammonia levels were compared. The influence of ammonia on degradation rate differs between identified metabolites.