Applying PICRUSt and 16S rRNA functional characterisation to predicting co-digestion strategies of various animal manures for biogas production

Abstract

AbstractAn estimated 25 million tons of animal manure is produced globally every year, causing considerable impact to the environment. These impacts can be managed through the use of anaerobic digestion (AD) This process achieves waste degradation through enzymatic activity, the efficiency of the AD process is directly related to microorganisms that produce these enzymes. Biomethane potential (BMP) assays remain the standard theoretical framework to pre-determine biogas yield and have been used to determine the feasibility of substrates or their combination for biogas production. However, an integrated approach that combines substrate choice and co-digestion would provide an improvement to the current predictive models. PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) addresses the limitations of assays in this regard. In this paper, the biochemical functions of horse, cow, and pig manures are predicted. A total of 135 predicted KEGG Orthologies (KOs) showed amino acids, carbohydrate, energy, lipid, and xenobiotic metabolisms in all the samples. Linear discriminant analysis (LDA) combined with the effect size measurements (LEfSe), showed that fructose, mannose, amino acid and nucleotide sugar, phosphotransferase (PST) as well as starch and sucrose metabolisms were significantly higher in horse manure samples. 36 of the KOs were related to the acidogenesis and/or acetogenesis AD stages. Extended bar plots showed that 11 significant predictions were observed for horse-cow, while 5 were predicted for horse-pig and for cow-pig manures. Based on these predictions, the AD process can be enhanced through co-digestion strategies that takes into account the predicted metabolic contributions of the manure samples. The results supported the BMP calculations for the samples in this study. Biogas yields can be improved if this combined approach is employed in routine analysis before co-digesting different substrates.