Catchment-scale export of antibiotic resistance genes and bacteria from an agricultural watershed in central Iowa

Abstract

Antibiotics are administered to livestock in animal feeding operations (AFOs) for the control, prevention, and treatment of disease. Manure from antibiotic treated livestock contains unmetabolized antibiotics that provide selective pressure on bacteria, facilitating the expression of anti-microbial resistance (AMR). Manure application on row crops is an agronomic practice used by growers to meet crop nutrient needs; however, it can be a source of AMR to the soil and water environment. This study in central Iowa aims to directly compare AMR indicators in outlet runoff from two adjacent (221 to 229 ha) manured and non-manured catchments (manure comparison), and among three catchments (600 to 804 ha) with manure influence, no known manure application (control), and urban influences (mixed land use comparison). Monitored AMR indicators included antibiotic resistance genes (ARGs) ermB, ermF (macrolide), tetA, tetM, tetO, tetW (tetracycline), sul1, sul2 (sulfonamide), aadA2 (aminoglycoside), vgaA, and vgaB (pleuromutilin), and tylosin and tetracycline resistant enterococci bacteria. Results of the manure comparison showed significantly higher (p<0.05) tetracycline and tylosin resistant bacteria from the catchment with manure application in 2017, but no differences in 2018, possibly due to changes in antibiotic use resulting from the Veterinary Feed Directive. Moreover, the ARG analysis indicated a larger diversity of ARGs at the manure amended catchment. The mixed land use comparison showed the manure amended catchment had significantly higher (p<0.05) tetracycline resistant bacteria in 2017 and significantly higher tylosin resistant bacteria in 2017 and 2018 than the urban influenced catchment. The urban influenced catchment had significantly higher ermB concentrations in both sampling years, however the manure applied catchment runoff consisted of higher relative abundance of total ARGs. Additionally, both catchments showed higher AMR indicators compared to the control catchment. This study identifies four ARGs that might be specific to AMR as a result of agricultural sources (tetM, tetW, sul1, sul2) and optimal for use in watershed scale monitoring studies for tracking resistance in the environment.

Tracking Sources and Dissemination of Indicator Antibiotic Resistance Genes at a Watershed Scale

“…Tetracycline has been widely used in livestock production, and tet ARGs are often found in the water impacted by agricultural runoff. ,, Therefore, monitoring the tetracycline-resistance gene tetO as an indicator of ARG in this study reflected the abundance of ARGs from agricultural monitoring sites. Sources of fecal contamination from agricultural activities were identified using MST markers ( CowM2 for cows and Ovmito for sheep) at sites 7, 12, and 14.…”

Antimicrobial resistance in integrated agroecosystems: State of the science and future opportunities

“…The observation that AMR indicators are enriched after manure application into soils reveals a risk for its further transport into waterways; an example is the occurrence of ARB and ARGs in waters with proximity to swine (Sapkota et al., 2007; Baquero et al., 2008; Garder et al., 2014; Luby et al., 2016). In an agriculturally dominated watershed in Iowa, ARG concentrations were observed to be correlated with manure application timing (Neher et al., 2020b) and indicated catchment‐scale export of ARGs and tetracycline‐ and tylosin‐resistant bacteria from agricultural sources (Rieke et al., 2018; Neher et al., 2020a). Duff et al.…”

A geospatial approach to identify patterns of antibiotic susceptibility at a neighborhood level in Wisconsin, United States

“…Importantly, resistance rates can vary across a country or even within the same state, as reported by the CDC nationally and locally in Wisconsin 6 – 8 . Globally, differences in antibiotic susceptibility can be observed and linked with local antibiotic use in healthcare and agriculture 9 – 11 . Our prior research identified geospatial patterns across Wisconsin when analyzing AMR data at the health system level 12 .…”