Defining an additivity framework for mixture research in inducible whole-cell biosensors

Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction

Environmental toxicants overwhelmingly occur together as mixtures. The variety of possible chemical interactions makes it difficult to predict the danger of the mixture. In this work, we propose the novel Reflected Generalized Concentration Addition (RGCA), a piece-wise, geometric technique for sigmoidal dose-responsed inverse functions that extends the use of generalized concentration addition (GCA) for 3+ parameter models. Since experimental tests of all relevant mixtures is costly and intractable, we rely only on the individual chemical dose responses. Additionally, RGCA enhances the classical two-step model for the cumulative effects of mixtures, which assumes a combination of GCA and independent action (IA). We explore how various clustering methods can dramatically improve predictions. We compare our technique to the IA, CA, and GCA models and show in a simulation study that the two-step approach performs well under a variety of true models. We then apply our method to a challenging data set of individual chemical and mixture responses where the target is an androgen receptor (Tox21 AR-luc). Our results show significantly improved predictions for larger mixtures. Our work complements ongoing efforts to predict environmental exposure to various chemicals and offers a starting point for combining different exposure predictions to quantify a total risk to health.

Acute toxicity of pesticide mixtures to honey bees is generally additive, and well predicted by Concentration Addition

Understanding the frequency of non-additive effects of pesticides (synergism and antagonism) is important in the context of risk assessment. The goal of this study was to investigate the prevalence of non-additive effects of pesticides to honey bees (Apis mellifera). We investigated a large set of mixtures including insecticides and fungicides of different chemical modes of action and classes. The mixtures included represent a relevant sample of pesticides that are currently used globally. We investigated whether the experimental toxicity of the mixtures could be predicted based on the Concentration Addition (CA) model for acute contact and oral adult bee toxicity tests. We measured the degree of deviation from the additivity predictions of the experimental toxicity based on the well-known Mixture Deviation Ratio (MDR). Further, we investigated the appropriate MDR thresholds that should be used for the identification of non-additive effects based on acceptable rates for false positive (alpha) and true positive (beta) findings. We found that a deviation factor of MDR = 5 is a sound reference for labeling potential non-additive effects in acute adult bee experimental designs when assuming a typical Coefficient of Variation (CV%) = 100 in the determination of the LD₅₀ of a pesticide (a factor of 2× deviation in the LD ₅₀ resulting from inter-experimental variability). We found that only 2.4 % and 9 % of the mixtures evaluated had an MDR > 5 and MDR < 0.2, respectively. The frequency and magnitude of deviation from additivity found for bees in this study are consistent with those of other terrestrial and aquatic taxa. Our findings suggest that additivity is a good baseline for predicting the toxicity of pesticide mixtures to bees, and that the rare cases of synergy of pesticide mixtures to bees are not random but have a mechanistic basis.

On the Progression of COVID-19 in Portugal: A Comparative Analysis of Active Cases Using Non-linear Regression

Portugal is often portrayed as a relatively successful case in the control of COVID-19's March 2020 outbreak in Europe due to timely confinement measures, commonly referred to as the "lockdown". As in other European Union member states, by late April, Portugal was preparing the phased loosening of such measures scheduled for the beginning of May. Despite a modest reduction in infection rates by that time, there was insufficient data to reliably forecast imminent scenarios. Using the South Korea data as scaffold, which became a paradigmatic case of recovery following a high number of infected people, we fitted the Portuguese data to biphasic models using non-linear regression and compared the two countries. The models, which yielded a good fit, showed that recovery would be slow, with over 50% active cases months after the lockdown. These findings acted at the time as a warning, showing that a high number of infected individuals, together with an unknown number of asymptomatic carriers, could increase the risk of a slow recovery, if not of new outbreaks. A month later, the models showed more favorable outcomes. However, shortly after, as the effects of leaving the lockdown became evident, the number of infections began rising again, leaving Portugal in a situation of inward and outward travel restrictions and baffling even the most conservative forecasts for the clearing of the pandemic.

Novel Segmented Concentration Addition Method to Predict Mixture Hormesis of Chlortetracycline Hydrochloride and Oxytetracycline Hydrochloride to Aliivibrio fischeri

Hormesis is a concentration-response phenomenon characterized by low-concentration stimulation and high-concentration inhibition, which typically has a nonmonotonic J-shaped concentration-response curve (J-CRC). The concentration addition (CA) model is the gold standard for studying mixture toxicity. However, the CA model had the predictive blind zone (PBZ) for mixture J-CRC. To solve the PBZ problem, we proposed a segmented concentration addition (SCA) method to predict mixture J-CRC, which was achieved through fitting the left and right segments of component J-CRC and performing CA prediction subsequently. We selected two model compounds including chlortetracycline hydrochloride (CTCC) and oxytetracycline hydrochloride (OTCC), both of which presented J-CRC to Aliivibrio fischeri (AVF). The seven binary mixtures (M1-M7) of CTCC and OTCC were designed according to their molar ratios of 12:1, 10:3, 8:5, 1:1, 5:8, 3:10, and 1:12 referring to the direct equipartition ray design. These seven mixtures all presented J-CRC to AVF. Based on the SCA method, we obtained mixture maximum stimulatory effect concentration (ECm) and maximum stimulatory effect (Em) predicted by SCA, both of which were not available for the CA model. The toxicity interactions of these mixtures were systematically evaluated by using a comprehensive approach, including the co-toxicity coefficient integrated with confidence interval method (CTCICI), CRC, and isobole analysis. The results showed that the interaction types were additive and antagonistic action, without synergistic action. In addition, we proposed the cross point (CP) hypothesis for toxic interactive mixtures presenting J-CRC, that there was generally a CP between mixture observed J-CRC and CA predicted J-CRC; the relative positions of observed and predicted CRCs on either side of the CP would exchange, but the toxic interaction type of mixtures remained unchanged. The CP hypothesis needs to be verified by more mixtures, especially those with synergism. In conclusion, the SCA method is expected to have important theoretical and practical significance for mixture hormesis.

Towards the development of a new generation of whole-cell bioreporters to sense iron bioavailability in oceanic systems-learning from the case of Synechococcus sp. PCC7002 iron bioreporter

Whole-cell bioreporters are genetically modified micro-organisms designed to sense bioavailable forms of nutrients or toxic compounds in aquatic systems. As they represent the most promising cost-efficient tools available for such purpose, engineering and use of bioreporters is rapidly growing in association with wide applicability. Bioreporters are urgently needed to determine phytoplankton iron (Fe) limitation, which has been reported in up to 30% of the ocean, with consequences affecting Earth's global carbon cycle and climate. This study presents a critical evaluation and optimization of the only Cyanobacteria bioreporter available to sense Fe limitation in marine systems (Synechococcus sp. PCC7002). The nonmonotonic biphasic dose-response curve between the bioreporters' signal and Fe bioavailability impairs an appropriate data interpretation, highlighting the need for new carefully designed bioreporters. Here, limitations under low Fe concentrations were related to cellular energy stress, nonlinear expression of the targeted promoter and siderophore expression. Furthermore, we provide critical standard criteria for the development of new Fe bioreporters. Finally, based on gene expression data under a range of marine Fe concentrations, we propose novel sensor genes for the development of new Cyanobacteria Fe bioreporters for distinct marine regions.

Dose-Response Analysis Using R

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.