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Jill R. Stewart

Publications and source records attributed to Jill R. Stewart.

2 recordsLinked to original sources

Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework

Introduction: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined. Methods: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region. Results: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km. Discussion: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.

Wisconsin

Probabilistic analysis showing that a combination of bacteroides and methanobrevibacter source tracking markers is effective for identifying waters contaminated by human fecal pollution

Microbial source tracking assays to identify sources of waterborne contamination typically target genetic markers of host-specific microorganisms. However, no bacterial marker has been shown to be 100% host-specific, and cross-reactivity has been noted in studies evaluating known source samples. Using 485 challenge samples from 20 different human and animal fecal sources, this study evaluated microbial source tracking markers including the Bacteroides HF183 16S rRNA, M. smithii nifH, and Enterococcus esp gene targets that have been proposed as potential indicators of human fecal contamination. Bayes' Theorem was used to calculate the conditional probability that these markers or a combination of markers can correctly identify human sources of fecal pollution. All three human-associated markers were detected in 100% of the sewage samples analyzed. Bacteroides HF183 was the most effective marker for determining whether contamination was specifically from a human source, and greater than 98% certainty that contamination was from a human source was shown when both Bacteroides HF183 and M. smithii nifH markers were present. A high degree of certainty was attained even in cases where the prior probability of human fecal contamination was as low as 8.5%. The combination of Bacteroides HF183 and M. smithii nifH source tracking markers can help identify surface waters impacted by human fecal contamination, information useful for prioritizing restoration activities or assessing health risks from exposure to contaminated waters.

Environmental Science & Technology