Mapping the scarcity of data on antibiotics in natural and engineered water environments across India

Antimicrobial resistance is a growing public health concern, increasingly recognized as a silent pandemic across the globe. Therefore, it is important to monitor all factors that could contribute to the emergence, maintenance and spread of antimicrobial resistance. Environmental antibiotic pollution is thought to be one of the contributing factors. India is one of the world’s largest consumers and producers of antibiotics. Hence, antibiotics have been detected in different environments across India, sometimes at very high concentrations due to their extensive use in humans and agriculture or due to manufacturing. We summarize the current state of knowledge on the occurrence and transport pathways of antibiotics in Indian water environments, including sewage or wastewater and treatment plants, surface waters such as rivers, lakes, and reservoirs as well as groundwater and drinking water. The factors influencing the distribution of antibiotics in the water environment, such as rainfall, population density and variations in sewage treatment are discussed, followed by existing regulations and policies aimed at the mitigation of environmental antimicrobial resistance in India, which will have global benefits. Then, we recommend directions for future research, development of standardized methods for monitoring antibiotics in water, ecological risk assessment, and exploration of strategies to prevent antibiotics from entering the environment. Finally, we provide an evaluation of how scarce the data is, and how a systematic understanding of the occurrence and concentrations of antibiotics in the water environment in India could be achieved. Overall, we highlight the urgent need for sustainable solutions to monitor and mitigate the impact of antibiotics on environmental, animal, and public health

A review on occurrence and spread of antibiotic resistance in wastewaters and in wastewater treatment plants: Mechanisms and perspectives

This paper reviews current knowledge on sources, spread and removal mechanisms of antibiotic resistance genes (ARGs) in microbial communities of wastewaters, treatment plants and downstream recipients. Antibiotic is the most important tool to cure bacterial infections in humans and animals. The over- and misuse of antibiotics have played a major role in the development, spread, and prevalence of antibiotic resistance (AR) in the microbiomes of humans and animals, and microbial ecosystems worldwide. AR can be transferred and spread amongst bacteria via intra- and interspecies horizontal gene transfer (HGT). Wastewater treatment plants (WWTPs) receive wastewater containing an enormous variety of pollutants, including antibiotics, and chemicals from different sources. They contain large and diverse communities of microorganisms and provide a favorable environment for the spread and reproduction of AR. Existing WWTPs are not designed to remove micropollutants, antibiotic resistant bacteria (ARB) and ARGs, which therefore remain present in the effluent. Studies have shown that raw and treated wastewaters carry a higher amount of ARB in comparison to surface water, and such reports have led to further studies on more advanced treatment processes. This review summarizes what is known about AR removal efficiencies of different wastewater treatment methods, and it shows the variations among different methods. Results vary, but the trend is that conventional activated sludge treatment, with aerobic and/or anaerobic reactors alone or in series, followed by advanced post treatment methods like UV, ozonation, and oxidation removes considerably more ARGs and ARB than activated sludge treatment alone. In addition to AR levels in treated wastewater, it examines AR levels in biosolids, settled by-product from wastewater treatment, and discusses AR removal efficiency of different biosolids treatment procedures. Finally, it puts forward key-points and suggestions for dealing with and preventing further increase of AR in WWTPs and other aquatic environments, together with a discussion on the use of mathematical models to quantify and simulate the spread of ARGs in WWTPs. Mathematical models already play a role in the analysis and development of WWTPs, but they do not consider AR and challenges remain before models can be used to reliably study the dynamics and reduction of AR in such systems.publishedVersio

Stochastic simulations as a tool for assessing signal fidelity in gene expression in synthetic promoter design

The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics.publishedVersio

Stochastic simulations as a tool for assessing signal fidelity in gene expression in synthetic promoter design

The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics

Quantifying dynamic mechanisms of auto-regulation in Escherichia coli with synthetic promoter in response to varying external phosphate levels

14 p.-6 fig.-1 tab.Escherichia coli have developed one of the most efficient regulatory response mechanisms to phosphate starvation. The machinery involves a cascade with a two-component system (TCS) that relays the external signal to the genetic circuit, resulting in a feedback response. Achieving a quantitative understanding of this system has implications in synthetic biology and biotechnology, for example, in applications for wastewater treatment. To this aim, we present a computational model and experimental results with a detailed description of the TCS, consisting of PhoR and PhoB, together with the mechanisms of gene expression. The model is parameterised within the feasible range, and fitted to the dynamic response of our experimental data on PhoB as well as PhoA, the product of this network that is used in alkaline phosphatase production. Deterministic and stochastic simulations with our model predict the regulation dynamics in higher external phosphate concentrations while reproducing the experimental observations. In a cycle of simulations and experimental verification, our model predicts and explores phenotypes with various synthetic promoter designs that can optimise the inorganic phosphate intake in E. coli. Sensitivity analysis demonstrates that the Pho-controlled genes have a significant influence over the phosphate response. Together with experimental findings, our model should thus provide insights for the investigations on engineering new sensors and regulators for living technologies.This work has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 686585 - LIAR, Living Architecture.Peer reviewe

The effect of disinfectants and antiseptics on co- and cross-selection of resistance to antibiotics in aquatic environments and wastewater treatment plants

The outbreak of the SARS-CoV-2 pandemic led to increased use of disinfectants and antiseptics (DAs), resulting in higher concentrations of these compounds in wastewaters, wastewater treatment plant (WWTP) effluents and receiving water bodies. Their constant presence in water bodies may lead to development and acquisition of resistance against the DAs. In addition, they may also promote antibiotic resistance (AR) due to cross- and co-selection of AR among bacteria that are exposed to the DAs, which is a highly important issue with regards to human and environmental health. This review addresses this issue and provides an overview of DAs structure together with their modes of action against microorganisms. Relevant examples of the most effective treatment techniques to increase the DAs removal efficiency from wastewater are discussed. Moreover, insight on the resistance mechanisms to DAs and the mechanism of DAs enhancement of cross- and co-selection of ARs are presented. Furthermore, this review discusses the impact of DAs on resistance against antibiotics, the occurrence of DAs in aquatic systems, and DA removal mechanisms in WWTPs, which in principle serve as the final barrier before releasing these compounds into the receiving environment. By recognition of important research gaps, research needs to determine the impact of the majority of DAs in WWTPs and the consequences of their presence and spread of antibiotic resistance were identified

Model dynamics at the whole body level.

<p>Each plot represents one variable dynamics. The normal glucose regulation (NGR) and T2DM conditions are shown in green and black, respectively. The red and blue lines delimit the physiological lower and upper ranges of variables (see also Table B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190627#pone.0190627.s001" target="_blank">S1 File</a>).</p

Graphical representation of the model describing the insulin signaling in adipocytes at the cellular level, according to the notation introduced in [62].

<p>Solid arrows represent state modification, while dashed arrows indicate reaction stimulation. Protein complexes are colored in yellow, green ovals represent the active and inactive feedback protein, while the orange rectangles represent all the other components of the cellular model. The plasma membrane of the adipose cell is represented in yellow and it separates the cytosol (light yellow horizontal lines) from the interstitial fluid (blue and white vertical lines). The variables I and G indicate insulin and glucose concentration in plasma (compartment not represented), which regulate the amount of interstitial insulin (INS_A) and glucose (Gt_A), respectively. For the sake of simplicity, we highlighted the five variables linking the cellular level to the whole body description (namely plasma insulin, interstitial insulin, plasma glucose, interstitial glucose and intra-adipocitary glucose) by adding the corresponding names in parenthesis.</p

Graphical representation of the whole-body glucose metabolism as considered in our model, according to the notation introduced in [62].

<p>Only the organs/tissues for which a variable has been explicitly included in the model are depicted in the figure (other key organs/tissues of glucose metabolism, like pancreas and brain, are not displayed in the figure even if their effect has been indirectly taken into account in model equations, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190627#sec006" target="_blank">Results</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190627#pone.0190627.s001" target="_blank">S1 File</a> for details). Adipose tissue is colored in yellow to highlight that it is the part for which a model at the cellular level is also provided (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190627#pone.0190627.g002" target="_blank">Fig 2</a>). Green ovals (hormones) and orange rectangles represent model variables; arrows represent mass transfer (white head), stimulation (black head) and inhibition (T head).</p

Model simulation and data fitting, T2DM condition.

<p>Each plot represents the corresponding time courses for the indicated insulin signaling intermediaries. The experimental data are taken from Nyman <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190627#pone.0190627.ref026" target="_blank">26</a>] and are represented with circles and error bars (a.u. indicates arbitrary units). The time course represents the model simulation.</p