Metal Ions and their Effects on Antimicrobial Resistance Development in Wastewater.

Antimicrobial resistance (AMR) is a global health challenge and there is increasing recognition of the role of the environment, particularly wastewater, in the development and spread of AMR. Although trace metals are common contaminants in wastewater, the quantitative effects of trace metals on AMR in wastewater settings remain understudied. We experimentally determined the interactions between common antibiotic residues and metal ions found in wastewater and investigated their effects on the development of antibiotic resistance in Escherichia coli over time. These data were then used to expand on a previously developed computational model of antibiotic resistance development in continuous flow settings to incorporate the effects of trace metals acting in combination with multiple antibiotic residues. We found that the common metal ions, copper and iron, interact with both ciprofloxacin and doxycycline at wastewater relevant concentrations. This can significantly affect resistance development due to antibiotic chelation of the metal ions causing a reduction in the antibiotics' bioactivity. Furthermore, modeling the effect of these interactions in wastewater systems showed the potential for metal ions in wastewater to significantly increase the development of antibiotic resistant E. coli populations. These results demonstrate the need to quantitatively understand the effects of trace metal-antibiotic interactions on AMR development in wastewater.

Effects of antibiotic interaction on antimicrobial resistance development in wastewater.

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the effects of antibiotic interaction. We found that populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on resistance development. Furthermore E. coli populations grown with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments.

Effects of Antibiotic Interaction on Antimicrobial Resistance Development in Wastewater.

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the complex effects of antibiotic interaction. We found that while E. coli populations grown in additively interacting antibiotic combinations grew predictably according to the previously developed model, those populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown in the condition with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on antimicrobial resistance development. Furthermore E. coli populations grown in the condition with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments. Importance: Antimicrobial resistance (AMR) is a growing global threat to public health expected to impact 10 million people by 2050, driving mortality rates globally and with a disproportionate effect on low- and middle-income countries. Communities in proximity to wastewater settings and environmentally contaminated surroundings are at particular risk due to resistance stemming from antibiotic residues from industrial and agricultural runoff. Currently, there is a limited quantitative and mechanistic understanding of the evolution of AMR in response to multiple interacting antibiotic residues in constant flow environments. Using an integrated computational and experimental methods, we find that interactions between antibiotic residues significantly affect the development of resistant bacterial populations.

Isolating Specific vs. Non-Specific Binding Responses in Conducting Polymer Biosensors for Bio-Fingerprinting.

A longstanding challenge for accurate sensing of biomolecules such as proteins concerns specifically detecting a target analyte in a complex sample (e.g., food) without suffering from nonspecific binding or interactions from the target itself or other analytes present in the sample. Every sensor suffers from this fundamental drawback, which limits its sensitivity, specificity, and longevity. Existing efforts to improve signal-to-noise ratio involve introducing additional steps to reduce nonspecific binding, which increases the cost of the sensor. Conducting polymer-based chemiresistive biosensors can be mechanically flexible, are inexpensive, label-free, and capable of detecting specific biomolecules in complex samples without purification steps, making them very versatile. In this paper, a poly (3,4-ethylenedioxyphene) (PEDOT) and poly (3-thiopheneethanol) (3TE) interpenetrating network on polypropylene-cellulose fabric is used as a platform for a chemiresistive biosensor, and the specific and nonspecific binding events are studied using the Biotin/Avidin and Gliadin/G12-specific complementary binding pairs. We observed that specific binding between these pairs results in a negative ΔR with the addition of the analyte and this response increases with increasing analyte concentration. Nonspecific binding was found to have the opposite response, a positive ΔR upon the addition of analyte was seen in nonspecific binding cases. We further demonstrate the ability of the sensor to detect a targeted protein in a dual-protein analyte solution. The machine-learning classifier, random forest, predicted the presence of Biotin with 75% accuracy in dual-analyte solutions. This capability of distinguishing between specific and nonspecific binding can be a step towards solving the problem of false positives or false negatives to which all biosensors are susceptible.

Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater.

Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of the development of antibiotic-resistant infections in wastewater, we have developed a mathematical model synthesizing many known drivers of antibiotic resistance in these settings to help predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug-resistant infection outbreak, including antibiotic residue concentration, antibiotic interaction, chromosomal mutation, and horizontal gene transfer, have not previously been integrated into a single computational model. We validated the outputs of the model with quantitative studies conducted on the eVOLVER continuous culture platform. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to increased development of resistant populations and that the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than acquisition of chromosomal mutations. Additionally, we found that synergistic antibiotics at low concentrations lead to increased resistant population growth. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic-resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low-income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts. IMPORTANCE The rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and according to the Review on Antimicrobial Resistance in 2014, it is suggested that the current rate will lead to AMR-related deaths of several million people by 2050 (Review on Antimicrobial Resistance, Tackling a Crisis for the Health and Wealth of Nations, 2014). One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug-resistant bacterial infections but is not well understood is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.

Evaluation of the effect of temperature on the stability and antimicrobial activity of rifampicin quinone.

Rifampicin is an antibiotic used as a first line treatment for tuberculosis, as well as in the treatment of other infectious diseases. Drug quality is essential for drug efficacy. Determining the stability and activity of Rifampicin Quinone in solution is important in its role as a standard against which to determine Rifampicin quality and in its effect on treatment and AMR development. Poor quality medicines, such as antimicrobials not only increase mortality and morbidity, but can also contribute to the development of antimicrobial resistance (AMR). One common marker of poor quality in Rifampicin samples is the presence of the degradation product Rifampicin Quinone. In this study we have found that Rifampicin Quinone in solution undergoes a chemical conversion to Rifampicin that is temperature dependent. This conversion occurs in physiologically relevant temperatures (30-50 °C) and time scales (24-120 h) and was verified using HPLC and LC-MS methods. Additionally, the conversion of Rifampicin Quinone to Rifampicin results in an increase in antimicrobial activity. We believe that ours is the first study reporting the instability of Rifampicin Quinone, and this instability in solution at these temperatures and time scales raises concerns for its use as a standard in quality testing using liquid chromatography methods and in studies of the effect of Rifampicin Quinone on AMR. Due to the use of Rifampicin Quinone as a standard in determining Rifampicin quality, the instability of Rifampicin Quinone also poses public health concerns, as the incorrect determination of medicine quality risks patient health and may promote the development of AMR.

Benchmarking national action plans on antimicrobial resistance in eight selected LMICs: Focus on the veterinary sector strategies.

BACKGROUND: The WHO Global Action Plan on antimicrobial resistance (GAP) provides a global strategy for combating antimicrobial resistance. Context-specific national action plans (NAP) translate GAP to reflect local priorities. However, the process by which countries translate GAP into NAPs, and the resultant concordance, is not well-known. The aim of the paper is to evaluate the NAPs of eight selected low- and lower-middle income countries (LMICs) against GAP and each other to identify best practices with a focus on the veterinary sector. METHODS: Using the WHO GAP, and the WHO Manual for designing NAPs, we performed a policy content evaluation for: Afghanistan, Bangladesh, Ethiopia, Ghana, Nepal, Nigeria, Pakistan and Uganda. NAPs were assessed as concordant with GAP if they contained ≥80% of the recommendations. Operational and monitoring and evaluation (M&E) plans were assessed as: Specific, Measurable, Assignable, and Time-bound (or SMAT). Financing, targets and legislation for antimicrobial use reduction, and medicine quality assurance mechanisms were assessed using a constructed framework. Countries were then ranked using a scoring system to identify best practices. RESULTS: All NAPs contained ≥80% of GAP's recommendations. Whereas Nepal's NAP was strategic, the rest were operational and uniformly SMAT; except Afghanistan's. The M&E plans were not all SMAT. Detailed costing and funding sources were included for only Ghana and Uganda. Quantitative target for antimicrobial use reduction was found only in Nepal's NAP and legislation only for Bangladesh. Ghana's and Uganda's medicine quality assurance mechanisms were the most robust. CONCLUSIONS: All NAPs were concordant with GAP. However, gaps exist in relation to M&E, diminishing the countries' capacity to be accountable and implement corrective action if necessary. Most lacked financing plans and targets for antimicrobial use reduction. The antimicrobial quality assurances strategies are limited in most of the NAPs assessed. A mechanism by which countries can benchmark their NAP would allow identification of specific limitations and areas of best practice.

Bacterial antibiotic resistance development and mutagenesis following exposure to subinhibitory concentrations of fluoroquinolones in vitro: a systematic review of the literature.

BACKGROUND: Understanding social and scientific drivers of antibiotic resistance is critical to help preserve antibiotic efficacy. These drivers include exposure to subinhibitory antibiotic concentrations in the environment and clinic. OBJECTIVES: To summarize and quantify the relationship between subinhibitory fluoroquinolone exposure and antibiotic resistance and mutagenesis to better understand resistance patterns and mechanisms. METHODS: Following PRISMA guidelines, PubMed, Web of Science and Embase were searched for primary in vitro experimental studies on subinhibitory fluoroquinolone exposure and bacterial antibiotic resistance and mutagenesis, from earliest available dates through to 2018 without language limitation. A specifically developed non-weighted tool was used to assess risk of bias. RESULTS: Evidence from 62 eligible studies showed that subinhibitory fluoroquinolone exposure results in increased resistance to the selecting fluoroquinolone. Most increases in MIC were low (median minimum of 3.7-fold and median maximum of 32-fold) and may not be considered clinically relevant. Mechanistically, resistance is partly explained by target mutations but also changes in drug efflux. Collaterally, resistance to other fluoroquinolones and unrelated antibiotic classes also develops. The mean ± SD quality score for all studies was 2.6 ± 1.8 with a range of 0 (highest score) to 7 (lowest score). CONCLUSIONS: Low and moderate levels of resistance and efflux changes can create an opportunity for higher-level resistance or MDR. Future studies, to elucidate the genetic regulation of specific resistance mechanisms, and increased policies, including surveillance of low-level resistance changes or genomic surveillance of efflux pump genes and regulators, could serve as a predictor of MDR development.