Organic fertilization co-selects genetically linked antibiotic and metal(loid) resistance genes in global soil microbiome.
Zi-Teng LiuRui-Ao MaDong ZhuKonstantinos T KonstantinidisYong-Guan ZhuSi-Yu ZhangPublished in: Nature communications (2024)
Antibiotic resistance genes (ARGs) and metal(loid) resistance genes (MRGs) coexist in organic fertilized agroecosystems based on their correlations in abundance, yet evidence for the genetic linkage of ARG-MRGs co-selected by organic fertilization remains elusive. Here, an analysis of 511 global agricultural soil metagenomes reveals that organic fertilization correlates with a threefold increase in the number of diverse types of ARG-MRG-carrying contigs (AMCCs) in the microbiome (63 types) compared to non-organic fertilized soils (22 types). Metatranscriptomic data indicates increased expression of AMCCs under higher arsenic stress, with co-regulation of the ARG-MRG pairs. Organic fertilization heightens the coexistence of ARG-MRG in genomic elements through impacting soil properties and ARG and MRG abundances. Accordingly, a comprehensive global map was constructed to delineate the distribution of coexistent ARG-MRGs with virulence factors and mobile genes in metagenome-assembled genomes from agricultural lands. The map unveils a heightened relative abundance and potential pathogenicity risks (range of 4-6) for the spread of coexistent ARG-MRGs in Central North America, Eastern Europe, Western Asia, and Northeast China compared to other regions, which acquire a risk range of 1-3. Our findings highlight that organic fertilization co-selects genetically linked ARGs and MRGs in the global soil microbiome, and underscore the need to mitigate the spread of these co-resistant genes to safeguard public health.
Keyphrases
- antibiotic resistance genes
- genome wide
- public health
- water soluble
- wastewater treatment
- heavy metals
- microbial community
- risk assessment
- climate change
- staphylococcus aureus
- escherichia coli
- poor prognosis
- bioinformatics analysis
- dna methylation
- machine learning
- south africa
- genome wide identification
- copy number
- anaerobic digestion
- long non coding rna
- high density
- drinking water
- data analysis