Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus which causes COVID-19, has spread rapidly across the globe infecting millions of people and causing significant health and economic impacts. Authorities are exploring complimentary approaches to monitor this infectious disease at the community level. Wastewater-based Epidemiological (WBE) approaches to detect SARS-CoV-2 RNA in municipal wastewater are being developed worldwide as an environmental surveillance approach to inform health authority decision-making. Owing to the extended excretion of SARS-CoV-2 RNA in stool, WBE can surveil large populated areas with a longer detection window providing unique information on the presence of pre-symptomatic and asymptomatic cases that are unlikely to be screened by clinical testing. Herein, we analysed SARS-CoV-2 RNA in 24-h composite wastewater samples (
n
= 63) from three wastewater treatment plants (WWTPs) in Brisbane, Queensland, Australia from 24
th
of February to 1
st
of May 2020. A total of 21 samples were positive for SARS-CoV-2, ranging from 135 to 11,992 gene copies (GC)/100 mL of wastewater. Detections were made in a Brisbane South WWTP in late February 2020, up to three weeks before the first cases were reported there. Wastewater samples were generally positive during the period with highest caseload data. The positive SARS-CoV-2 RNA detection in wastewater while there were limited clinical reported cases demonstrates the potential of WBE as an early warning system. When integrated into disease surveillance and monitoring systems, wastewater monitoring data may assist management efforts to identify hotspots and target localised public health responses, such as increased individual testing and the provision of health warnings.
Evolutionary trade-offs occur when selection on one trait has detrimental effects on other traits. In pathogenic microbes, it has been hypothesized that antibiotic resistance trades off with fitness in the absence of antibiotic. Although studies of single resistance mutations support this hypothesis, it is unclear whether trade-offs are maintained over time, due to compensatory evolution and broader effects of genetic background. Here, we leverage natural variation in 39 extraintestinal clinical isolates of Escherichia coli to assess trade-offs between growth rates and resistance to fluoroquinolone and cephalosporin antibiotics. Whole-genome sequencing identifies a broad range of clinically relevant resistance determinants in these strains. We find evidence for a negative correlation between growth rate and antibiotic resistance, consistent with a persistent trade-off between resistance and growth. However, this relationship is sometimes weak and depends on the environment in which growth rates are measured. Using in vitro selection experiments, we find that compensatory evolution in one environment does not guarantee compensation in other environments. Thus, even in the face of compensatory evolution and other genetic background effects, resistance may be broadly costly, supporting the use of drug restriction protocols to limit the spread of resistance. Furthermore, our study demonstrates the power of using natural variation to study evolutionary trade-offs in microbes.
The cost of antimicrobial resistance (AMR) is the reduction of fitness of a resistant mutant relative to a susceptible strain in the absence of drug. Costs of resistance are usually estimated in a single environment and on one genetic background; these fitness estimates may not be representative of what happens in nature. I measured the fitness of AMR E. coli strains in different environments, including medically and ecologically relevant ones. To do this, a collection of AMR strains of Escherichia coli bearing a single resistance mutation were competed against their ancestral strain in 10 different media. The results of this study indicate that laboratory media does not predict fitness in natural environments. We found environments in which resistance alleles suffered no cost, suggesting that these mutants may persist for long periods of time. Data on the fitness of AMR pathogens across environments will help manage their spread.
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