Real-time PCR methods for quantitative monitoring of streptomycin and tetracycline resistance genes in agricultural ecosystems

Simple absolute quantification method correcting for quantitative PCR efficiency variations for microbial community samples

Real-time quantitative PCR (qPCR) is a widely used technique in microbial community analysis, allowing the quantification of the number of target genes in a community sample. Currently, the standard-curve (SC) method of absolute quantification is widely employed for these kinds of analysis. However, the SC method assumes that the amplification efficiency (E) is the same for both the standard and the sample target template. We analyzed 19 bacterial strains and nine environmental samples in qPCR assays, targeting the nifH and 16S rRNA genes. The E values of the qPCRs differed significantly, depending on the template. This has major implications for the quantification. If the sample and standard differ in their E values, quantification errors of up to orders of magnitude are possible. To address this problem, we propose and test the one-point calibration (OPC) method for absolute quantification. The OPC method corrects for differences in E and was derived from the ΔΔC(T) method with correction for E, which is commonly used for relative quantification in gene expression studies. The SC and OPC methods were compared by quantifying artificial template mixtures from Geobacter sulfurreducens (DSM 12127) and Nostoc commune (Culture Collection of Algae and Protozoa [CCAP] 1453/33), which differ in their E values. While the SC method deviated from the expected nifH gene copy number by 3- to 5-fold, the OPC method quantified the template mixtures with high accuracy. Moreover, analyzing environmental samples, we show that even small differences in E between the standard and the sample can cause significant differences between the copy numbers calculated by the SC and the OPC methods.

Influence of soil use on prevalence of tetracycline, streptomycin, and erythromycin resistance and associated resistance genes

This study examined differences in antibiotic-resistant soil bacteria and the presence and quantity of resistance genes in soils with a range of management histories. We analyzed four soils from agricultural systems that were amended with manure from animals treated with erythromycin and exposed to streptomycin and/or oxytetracycline, as well as non-manure-amended compost and forest soil. Low concentrations of certain antibiotic resistance genes were detected using multiplex quantitative real-time PCR (qPCR), with tet(B), aad(A), and str(A) each present in only one soil and tet(M) and tet(W) detected in all soils. The most frequently detected resistance genes were tet(B), tet(D), tet(O), tet(T), and tet(W) for tetracycline resistance, str(A), str(B), and aac for streptomycin resistance, and erm(C), erm(V), erm(X), msr(A), ole(B), and vga for erythromycin resistance. Transposon genes specific for Tn916, Tn1549, TnB1230, Tn4451, and Tn5397 were detected in soil bacterial isolates. The MIC ranges of isolated bacteria for tetracycline, streptomycin, and erythromycin were 8 to >256 μg/ml, 6 to >1,024 μg/ml, and 0.094 to >256 μg/ml, respectively. Based on 16S rRNA gene similarity, isolated bacteria showed high sequence identity to genera typical of soil communities. Bacteria with the highest MICs were detected in manure-amended soils or soils from agricultural systems with a history of antibiotic use. Non-manure-amended soils yielded larger proportions of antibiotic-resistant bacteria, but these had lower MICs, carried fewer antibiotic resistance genes, and did not display multidrug resistance (MDR).