Evaluation of the long-term effects of anaerobic digestion on bovine manure resistomes and mobilomes and the molecular and microbial mechanisms involved
Bibliographic record
Abstract
Anaerobic digestion (AD) has shown the potential to reduce the abundance of antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) in animal manures. It stands as a promising option to reduce the risk of the spread of antimicrobial resistance (AMR) due to livestock production and manure applications. However, the underlying mechanisms driving these changes still need to be fully understood. This multidisciplinary study aimed to utilize metagenomics to investigate the molecular and microbial mechanisms associated with the evolution of ARGs and MGEs during the anaerobic digestion (AD) of bovine dairy manure. The research focused on three main aspects: 1) examining the long-term effects of mesophilic (MAD) and thermophilic (TAD) anaerobic digestion on the entire set of ARGs (resistome) and MGEs (mobilome) in manure; 2) comparing the impact of alternative manure treatments, such as storage and solid-liquid separation, on resistomes and mobilomes; 3) identifying microbial groups potentially associated with ARGs and MGEs and microbial shifts potentially driving changes in resistomes and mobilomes. A meta-analysis conducted early in this research informed the primary focus of this research. Two anaerobic digesters operating at mesophilic (36 °C) and the other at thermophilic (55 °C) temperatures were set up, operated, monitored, and studied for 4 and 2 years, respectively. Metagenomics analyses were used to evaluate resistomes, mobilomes, and microbiomes in the mesophilic and thermophilic digesters operating under steady state and in the bovine manure used as substrate. The results indicated that MAD and TAD lowered ARG levels in fresh cattle manure by over 50% and MGEs by over 65%. Surprisingly, TAD did not outperform MAD at reducing ARGs and MGEs. Co-occurrence analysis indicated a strong association between microbial groups from the phyla Bacillota (e.g., Jeotgalicoccus, Streptococcus, Enterococcus), Actinomycetota (e.g., Brevibacterium, Rhodococcus), and Pseudomonadota (e.g., Acinetobacter, Comamonas) with these AMR elements. The decline in the abundance of aerobic and facultative anaerobes likely linked to hydrolytic functions was suggested as one of the main drivers of the changes in resistomes and mobilomes. The proximity of toxin-antitoxin systems and transposon structures to specific ARGs (e.g., Erm, tet, Ant(6)-la) was discovered, which could explain the persistence of such ARGs in digestates. The study of the effects of other manure treatments, such as aerobic storage in an open tank and solid-liquid separation, revealed that they are less efficient in reducing ARGs and MGEs from manures than AD. In this study, the high levels of ARGs and genes conferring resistance to heavy metals in a farm operating in an antibiotic-free environment suggested that other antimicrobials, such as foot bathing solutions, may be causing the indirect selection of ARGs. Overall, this research made several contributions to understanding AMR in the context of anaerobic digestion of animal manure that could be extrapolated to other manure treatments. These contributions not only bridge existing gaps in the literature but also pave the way for future research, providing valuable insights that can be used to inform the development of more effective strategies to mitigate the dissemination of AMR associated with manure management, application, and disposal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".