Exploring correlations between antibiotic resistance and antibiotic selective pressure in the aquatic environment

Abstract

Antibiotics resistance (AR) in human and animal pathogens is increasingly leading to failures in treating infectious disease by means of antibiotics currently in use. Large scale antibiotics consumption in human health care and agriculture is seen as the primary driver behind this. Scientific research bears out that the environment has an important role in the emergence and spread of AR. However, a limited understanding of the mechanisms that drive AR in the environment complicates the identification of effective strategies. Many knowledge and data gaps still exist on the mechanisms that drive AR in the environment and how this eventually relates to human health risk.
This thesis aimed to investigate the correlation between antibiotic selective pressure and antibiotic resistance in the aquatic environment, by performing a meta-analysis of data retrieved from experimental research literature. Sample data extracted from 9 studies was used containing measurements of both antibiotic concentrations and antibiotic resistance genes (ARGs) abundance per 16s in surface water and sediments. Antibiotic concentrations were translated into selective pressures using the PNEC (predicted no-effect concentration) for each antibiotic type. Total selective pressure of each antibiotic class (TASP) was matched to the total resistance gene abundance (TARG) of associated gene types, resulting in 738 data points. A linear mixed effect model (LMEM) was constructed with TARG as the response and TASP as the explanatory variable. Antibiotic class (Class) was added as an categorical explanatory variable and environmental matrix (Matrix), season
(Season), and antibiotic class nested in study (Study/Class) were added as covariates.
Results from data analysis showed no correlation between TARG and TASP. However, both Class, Season and Study/Class were significant factors influencing the relationship between TASP and TARG in the data set, together explaining 81% of the variance in the data. This indicates that the relationship between TASP and TARG is complex and non-linear, but temporal influences and antibiotic class might significantly affect the variance seen in the relationship between selective pressure and associated resistance gene abundance.
Additionally, Pearson correlations showed a strong and positive correlation between Tetracyclines and tetW in sediments. A number of relatively strong and positive correlations were seen in both matrices between antibiotic classes and unrelated gene types. Overall, selective pressure was highest from Quinolones in both matrices, followed by Cephalosporins and Tetracyclines and lowest from Sulphonamides. Highest ARG-abundances were found for mobile genetic element intl1 and for the resistance genes blaTEM and tetZ, conferring resistance to respectively Cephalosporins and Tetracyclines.

Date of Award	21 Oct 2021
Original language	English
Supervisor	Ad Ragas (Examiner) & Stefan Dekker (Co-assessor)