A rationale for the high limits of quantification of antibiotic resistance genes in soil

2018 
Abstract The determination of values of abundance of antibiotic resistance genes (ARGs) per mass of soil is extremely useful to assess the potential impacts of relevant sources of antibiotic resistance, such as irrigation with treated wastewater or manure application. Culture-independent methods and, in particular, quantitative PCR (qPCR), have been regarded as suitable approaches for such a purpose. However, it is arguable if these methods are sensitive enough to measure ARGs abundance at levels that may represent a risk for environmental and human health. This study aimed at demonstrating the range of values of ARGs quantification that can be expected based on currently used procedures of DNA extraction and qPCR analyses. The demonstration was based on the use of soil samples spiked with known amounts of wastewater antibiotic resistant bacteria (ARB) ( Enterococcus faecalis , Escherichia coli , Acinetobacter johnsonii , or Pseudomonas aeruginosa ), harbouring known ARGs, and also on the calculation of expected values determined based on qPCR. The limits of quantification (LOQ) of the ARGs ( vanA , qnrS , bla TEM , bla OXA , bla IMP , bla VIM ) were observed to be approximately 4 log-units per gram of soil dry weight, irrespective of the type of soil tested. These values were close to the theoretical LOQ values calculated based on currently used DNA extraction methods and qPCR procedures. The observed LOQ values can be considered extremely high to perform an accurate assessment of the impacts of ARGs discharges in soils. A key message is that ARGs accumulation will be noticeable only at very high doses. The assessment of the impacts of ARGs discharges in soils, of associated risks of propagation and potential transmission to humans, must take into consideration this type of evidence, and avoid the simplistic assumption that no detection corresponds to risk absence.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    31
    References
    10
    Citations
    NaN
    KQI
    []