The global groundwater resistome: core ARGs and their dynamics - an in silico re-analysis of publicly available groundwater metagenomes
Kampouris, I. D.; Bengtsson-Palme, J.; Berendonk, T. U.; Klumper, U.
Show abstract
Despite the importance of groundwater as a drinking water resource, currently, no comprehensive picture regarding the global levels of antibiotic resistance genes (ARGs) in groundwater environments exists. Moreover, the biotic and abiotic factors that shape the groundwater resistome on the global scale remain to be explored. Herein, we attempted to fill this knowledge gap through in silico re-analysis of publicly available global groundwater metagenomes. First, nine ARGs encoding resistance to aminoglycosides (aadA, aph(3), and ant(3)), sulfonamides (sul1 and sul2), {beta}-lactams (blaOXA and blaTEM), tetracyclines (tet(C)) and macrolides (msr(E)) were identified to constitute the core groundwater resistome with high detection and abundance levels. Second, the global drivers of groundwater resistome composition were identified by applying a structural equation model with mixed effects to disentangle the individual contributions of each abiotic and biotic factor. Most notably, global effects of the origin of groundwater samples on the resistome were detected with samples from high-income countries (HICs) constantly displaying lower ARG and mobile genetic element (MGE) abundances than those from low-and-middle-income countries (LMICs). While these effects were consistent across antibiotic classes, biotic factors such as interactions of the groundwater microbiome with fungal or bacterial natural producers of antibiotics, or the co-occurrence of ARGs on mobile genetic elements (MGEs) played significant roles in shaping abundance patterns of resistance towards individual antibiotic classes. Only few ARGs correlated to individual bacterial genera, with microbial community composition in general weakly associated with resistome composition. In conclusion, we provide a first global picture of the resistome of low-anthropogenic impacted groundwater environments and the underlying anthropogenic and biotic drivers shaping it, which can be used as a baseline in future surveillance of antibiotic resistance.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Characterisation of the bacterial microbiota of a landfill-contaminated confined aquifer undergoing intrinsic remediation 97%
- Direct and indirect effects of the increase in atmospheric CO2 and temperature on groundwater organisms 96%
- Life on the edge: microbial diversity, resistome, and virulome in soils from the Union Glacier cold desert 95%
Similar papers in this journal
Similar papers in this journal
- Spatial and Temporal Dynamics at an Actively Silicifying Hydrothermal System 96%
- Single-cell genomics of single soil aggregates: methodological assessment and potential implications with a focus on nitrogen metabolism 96%
- A resistome survey across hundreds of freshwater bacterial communities reveals the impacts of veterinary and human antibiotics use 96%
Similar papers in this journal
- Microbial communities from weathered outcrops of a sulfide-rich ultramafic intrusion, and implications for mine waste management 95%
- Time series metagenomic sampling of the Thermopyles, Greece, geothermal springs reveals stable microbial communities dominated by novel sulfur-oxidizing chemoautotrophs 95%
- Metabolic potential and survival strategies of microbial communities across extreme temperature gradients on Deception Island volcano, Antarctica 95%
Similar papers in this journal
- Multi-compartment impact of micropollutants and particularly antibiotics on bacterial communities using environmental DNA at river basin-level 96%
- Stable isotopes and nanoSIMS single-cell imaging reveals soil plastisphere colonizers able to assimilate sulfamethoxazole 94%
- Plastic Leachate Exposure Drives Antibiotic Resistance and Virulence in Marine Bacterial Communities 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.