| Title | Metagenomic and HT-qPCR analysis reveal the microbiome and resistome in pig slurry under storage, composting, and anaerobic digestion |
|---|---|
| ID_Doc | 12851 |
| Authors | Do, TT; Nolan, S; Hayes, N; O'Flaherty, V; Burgess, C; Brennan, F; Walsh, F |
| Title | Metagenomic and HT-qPCR analysis reveal the microbiome and resistome in pig slurry under storage, composting, and anaerobic digestion |
| Year | 2022 |
| Published | |
| Abstract | Direct application of pig slurry to agricultural land, as a means of nutrient recycling, introduces pathogens, antibiotic resistant bacteria, or genes, to the environment. With global environmental sustainability policies mandating a reduction in synthetic fertilisation and a commitment to a circular economy it is imperative to find effective on-farm treatments of slurry that maximises its fertilisation value and minimises risk to health and the environment. We assessed and compared the effect of storage, composting, and anaerobic digestion (AD) on pig slurry microbiome, resistome and nutrient content. Shotgun metagenomic sequencing and HT-qPCR arrays were implemented to understand the dynamics across the treatments. Our results identified that each treatment methods have advantages and disadvantages in removal pollutants or increasing nutrients. The data suggests that storage and composting are optimal for the removal of human pathogens and anaerobic digestion for the reduction in antibiotic resistance (AMR) genes and mobile genetic elements. The nitrogen content is increased in storage and AD, while reduced in composting. Thus, depending on the requirement for increased or reduced nitrogen the optimum treatment varies. Combining the results indicates that composting provides the greatest gain by reducing risk to human health and the environment. Network analysis revealed reducing Proteobacteria and Bacteroidetes while increasing Firmicutes will reduce the AMR content. KEGG analysis identified no significant change in the pathways across all treatments. This novel study provides a data driven decision tree to determine the optimal treatment for best practice to minimise pathogen, AMR and excess or increasing nutrient transfer from slurry to environment. |
| https://doi.org/10.1016/j.envpol.2022.119271 |
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