Antimicrobial resistance (AMR) in the environment is driven by antibiotics released in the urine of humans and animals into sewage and ultimately the receiving rivers. AMR is also released from within the gut bacteria that are shed in faeces of both humans and animals. In both cases, antibiotics and AMR-containing gut bacteria are released into the environment through sewage.
Despite the continued release of both antibiotics and antibiotic-resistant bacteria into our rivers, we still don’t know the relative role that they play in explaining the amount of antibiotic resistance that we see in our environment.
This is a critically important knowledge gap as it prevents industry and policy makers from determining where to spend our time and resources so as to lower this ‘environmental reservoir of antimicrobial resistance’.
Sewage contains thousands of chemicals, many of which are at concentrations sufficient to inhibit or kill bacteria. Microbes defend themselves from these chemicals with a range of strategies, all of which have genes that are broadly classified as ‘resistance genes’. Hence, sewage is an excellent place to find bacteria rich in resistance genes. Many of these genes are known to be mobile, which allows for the genes to be shared, thereby increasing its abundance within the environment. This mobility of genes is key to why it is so difficult to know what is driving AMR in the environment—a bit like ‘which came first, the chicken or the egg.’
Are the concentrations of antibiotics present in sewage sufficiently high to select for resistance genes in the environment or are the genes for resistance simply spreading from the gut-derived bacteria into the native environmental microorganisms? Or is it equal parts of both? The research within CEH aims to examine this question.
A NERC-funded project within the Environmental Microbiology & Human Health programme aims to, for the first time, use cutting edge high throughput DNA sequencing technologies and computational analyses to increase our understanding of the human activities that drive increased levels of antibiotic resistant bacteria across the River Thames catchment. Abundance and identity of over 3000 different resistance genes will be determined at 69 sampling sites, in triplicate at three time points over one year, to capture impacts of seasonality and flow. We will also measure a range of antibiotic residues, metals and nutrients. We will use graphical information system data on waste water treatment plant type, size and location and land use throughout the catchment. Together this data will be used to produce a model which will reveal the main drivers of resistance gene abundance and diversity at the catchment scale. We will also identify novel molecular markers associated with different sources of pollution that can be used as source tracking targets. We aim to analyse the effects of specific mitigation strategies that are able to reduce levels of resistant bacteria, this will enable estimates of reduction in resistance levels that can inform policy and regulatory targets.
A translational tool will be developed for surveillance of the most important marker genes identified from the DNA sequence analyses and modelling work. This will be an affordable test that will help identify key factors for human health risk assessment.
All research will be conducted on sediment rather than the water column as bacterial numbers are far greater in sediment, are known to be more stable and are more likely to reflect associations with spatial and physico-chemical properties at a given sample site. Decay rates for E. coli are an order of magnitude lower in waste water contaminated sediment than in freshwater and several studies have reported growth of enteric bacteria including E. coli in sediments. A large number of publications have shown that re-suspension of sediment, rather than run-off from surrounding land can create elevated E. coli concentrations in water.
The 69 sampling locations can be found here. The sampling locations were selected within a sub-set of sub-catchments within the larger Thames Catchment. Specific sampling locations reflect several driving factors, such as sewage input, septic tank leakage, fish farm inputs, farmyard diffuse pollution, and urban runoff. The rivers include chalk catchments as well as clay catchments and include sub-catchments in all four corners of the Thames Catchment, including the main stem of the River Thames.