A QSAR-based mechanistic study on the combined toxicity of antibiotics and quorum sensing inhibitors against Escherichia coli

A new parameter for quantitatively characterizing antibiotic hormesis: QSAR construction and joint toxic action judgment

Antibiotics usually induce the hormetic effects on bacteria, featured by low-dose stimulation and high-dose inhibition, which challenges the central belief in toxicity assessment and environmental risk assessment of antibiotics. However, there are currently no ideal parameters to quantitatively characterize hormesis. In this study, an effective area in hormesis (AH) was developed to quantify the biphasic dose-responses of single antibiotics (sulfonamides (SAs), sulfonamides potentiators (SAPs), and tetracyclines (TCs)) and binary mixtures (SAs-SAPs, SAs-TCs, and SAs-SAs) to the bioluminescence of Aliivibrio fischeri. Using Ebind (the lowest interaction energy between antibiotic and target protein) and Kow (octanol-water partition coefficient) as the structural descriptors, the reliable quantitative structure-activity relationship (QSAR) models were constructed for the AH values of test antibiotics and mixtures. Furthermore, a novel method based on AH was established to judge the joint toxic actions of binary antibiotics, which mainly exhibited synergism. The results also indicated that SAPs (or TCs) contributed more than SAs in the hormetic effects of antibiotic mixtures. This study proposes a new quantitative parameter for characterizing and predicting antibiotic hormesis, and considers hormesis as an integrated whole to reveal the combined effects of antibiotics, which will promote the development of risk evaluation for antibiotics and their mixtures.

Classification, characteristics, harmless treatment and safety assessment of antibiotic pharmaceutical wastewater (APWW): A comprehensive review

The issues related to the spread of antibiotics and antibiotic resistance genes (ARGs) have garnered significant attention from researchers and governments. The production of antibiotics can lead to the emission of high-concentration pharmaceutical wastewater, which contains antibiotic residues and various other pollutants. This review compiles the classification and characteristics of antibiotic pharmaceutical wastewater (APWW), offers an overview of the development, advantages, and disadvantages of diverse harmless treatment processes, and presents a strategy for selecting appropriate treatment approaches. Biological treatment remains the predominant approach for treating APWW. In addition, several alternative methods can be employed to address the challenges associated with APWW treatment. On the other hand, the present safety assessment of the effluent resulting from APWW treatment is inadequate, necessitating more comprehensive research in this domain. It is recommended that researches in this area consider the issue of toxicity and antibiotic resistance as well. The PNECR model (similar to ecotoxicological PNECs but used to specifically refer to endpoints related to antimicrobial resistance) (Murray et al., 2024) is an emerging tool used for evaluating the antimicrobial resistance (AMR) issue. This model is, characterized by its simplicity and effectiveness, is a promising tool for assessing the safety of treated APWW.

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Acute toxicity of binary mixtures for quorum sensing inhibitors and sulfonamides against Aliivibrio fischeri: QSAR investigations and joint toxic actions

Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure-activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures.

Triple-Emission Single Sensing Element-Enabled Ratiometric Fluorescent Array Identification of Multiple Antibiotics

Given that intricate toxicological profiles exist among different antibiotics and pose serious threats to the environment and human health, synchronous analysis of multiple residues becomes crucial. Sensor arrays show potential to achieve the above purpose, but it is challenging to develop easy-to-use and high-sensitivity tools because the state-of-the-art arrays often require more than one recognition unit and are monosignal dependent. Here we exquisitely designed a fluorescent nanoprobe (2-aminoterephthalic acid-anchored CdTe quantum dots with Eu3+ coordination, CdTe-ATPA-Eu3+) featuring triple emissions at the same excitation as the only element to fabricate a luminescent sensor array with ratiometric calculations for identifying multiple antibiotics. By taking tetracycline, chlortetracycline, doxycycline, oxytetracycline, penicillin G, and sulfamethoxazole as models, the six species exhibited distinguishable motivation or/and quenching impacts on the three emissions of CdTe-ATPA-Eu3+, which were employed as indicators to perform the ratiometric logical operation and further combined with pattern recognition analysis for multitarget determination. Evidently, such a design exhibits two advances: (1) with the triple-emission probe as the sole receptor requiring neither internal nor external adjustments, the fabricated array acts as an extremely facile tool for multianalyte detection; (2) the ratiometric calculations offer excellent sensitivity and reliability for high-performance determination. Consequently, accurate identification and quantification of individual antibiotics and their combinations at various levels were verified in both laboratory and practical matrices. Our work provides a new tool for simultaneously detecting multiple antibiotics, and it will inspire the development of advanced sensor arrays for multitarget analysis.

Interactions between sulfonamide homologues and glycosyltransferase induced metabolic disorders in rice (Oryza sativa L.)

Sulfadiazine and its derivatives (sulfonamides, SAs) could induce distinct biotoxic, metabolic and physiological abnormalities, potentially due to their subtle structural differences. This study conducted an in-depth investigation on the interactions between SA homologues, i.e. sulfadiazine (SD), sulfamerazine (SD1), and sulfamethazine (SD2), and the key metabolic enzyme (glycosyltransferase, GT) in rice (Oryza sativa L.). Untargeted screening of SA metabolites revealed that GT-catalyzed glycosylation was the primary transformation pathway of SAs in rice. Molecular docking identified that the binding sites of SAs on GT (D0TZD6) were responsible for transferring sugar moiety to synthesize polysaccharides and detoxify SAs. Specifically, amino acids in the GT-binding cavity (e.g., GLY487 and CYS486) formed stable hydrogen bonds with SAs (e.g., the sulfonamide group of SD). Molecular dynamics simulations revealed that SAs induced conformational changes in GT ligand binding domain, which was supported by the significantly decreased GT activity and gene expression level. As evidenced by proteomics and metabolomics, SAs inhibited the transfer and synthesis of sugar but stimulated sugar decomposition in rice leaves, leading to the accumulation of mono- and disaccharides in rice leaves. While the differences in the increased sugar content by SD (24.3%, compared with control), SD1 (11.1%), and SD2 (6.24%) can be attributed to their number of methyl groups (0, 1, 2, respectively), which determined the steric hindrance and hydrogen bonds formation with GT. This study suggested that the disturbances on crop sugar metabolism by homologues contaminants are determined by the interaction between the contaminants and the target enzyme, and are greatly dependent on the steric hindrance effects contributed by their side chains. The results are of importance to identify priority pollutants and ensure crop quality in contaminated fields.

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Surface modification of titanium substrate via combining photothermal therapy and quorum-sensing-inhibition strategy for improving osseointegration and treating biofilm-associated bacterial infection

Insufficient osseointegration and biofilm-associated bacterial infection are important challenges for clinical application of titanium (Ti)-based implants. Here, we constructed mesoporous polydopamine (MPDA) nanoparticles (NPs) loaded with luteolin (LUT, a quorum sensing inhibitor), which were further coated with the shell of calcium phosphate (CaP) to construct MPDA-LUT@CaP nanosystem. Then, MPDA-LUT@CaP NPs were immobilized on the surface of Ti implants. Under acidic environment of bacterial biofilm-infection, the CaP shell of MPDA-LUT@CaP NPs was rapidly degraded and released LUT, Ca2+ and PO4 3- from the surface of Ti implant. LUT could effectively inhibit and disperse biofilm. Furthermore, under near-infrared irradiation (NIR), the thermotherapy induced by the photothermal conversion effect of MPDA destroyed the integrity of the bacterial membrane, and synergistically led to protein leakage and a decrease in ATP levels. Combined with photothermal therapy (PTT) and quorum-sensing-inhibition strategy, the surface-functionalized Ti substrate had an antibacterial rate of over 95.59% against Staphylococcus aureus and the elimination rate of the formed biofilm was as high as 90.3%, so as to achieve low temperature and efficient treatment of bacterial biofilm infection. More importantly, the modified Ti implant accelerated the growth of cell and the healing process of bone tissue due to the released Ca2+ and PO4 3-. In summary, this work combined PTT with quorum-sensing-inhibition strategy provides a new idea for surface functionalization of implant for achieving effective antibacterial and osseointegration capabilities.

Bioengineering Approaches to Fight against Orthopedic Biomaterials Related-Infections

One of the most serious complications following the implantation of orthopedic biomaterials is the development of infection. Orthopedic implant-related infections do not only entail clinical problems and patient suffering, but also cause a burden on healthcare care systems. Additionally, the ageing of the world population, in particular in developed countries, has led to an increase in the population above 60 years. This is a significantly vulnerable population segment insofar as biomaterials use is concerned. Implanted materials are highly susceptible to bacterial and fungal colonization and the consequent infection. These microorganisms are often opportunistic, taking advantage of the weakening of the body defenses at the implant surface–tissue interface to attach to tissues or implant surfaces, instigating biofilm formation and subsequent development of infection. The establishment of biofilm leads to tissue destruction, systemic dissemination of the pathogen, and dysfunction of the implant/bone joint, leading to implant failure. Moreover, the contaminated implant can be a reservoir for infection of the surrounding tissue where microorganisms are protected. Therefore, the biofilm increases the pathogenesis of infection since that structure offers protection against host defenses and antimicrobial therapies. Additionally, the rapid emergence of bacterial strains resistant to antibiotics prompted the development of new alternative approaches to prevent and control implant-related infections. Several concepts and approaches have been developed to obtain biomaterials endowed with anti-infective properties. In this review, several anti-infective strategies based on biomaterial engineering are described and discussed in terms of design and fabrication, mechanisms of action, benefits, and drawbacks for preventing and treating orthopaedic biomaterials-related infections.

The investigation of influencing factors on the degradation of sulfonamide antibiotics in iron-impregnated biochar-activated urea-hydrogen peroxide system: A QSAR study

Iron-impregnated biochar-activated urea-hydrogen peroxide (FB-activated UHP) is a potential in-situ technology for simultaneously reducing soil sulfonamide antibiotic contaminants and improving soil fertility. To better understand the degradation of sulfonamide antibiotics by FB-activated UHP, a two-dimensional quantitative structure-activity relationship (2D-QSAR) model based on quantum chemical parameters and a three-dimensional QSAR (3D-QSAR) model based on molecular force field were developed to investigate the factors influencing the removal efficiencies (Re%). The optimal 2D-QSAR model was Re%= 0.858-8.930 E-5 E_B3LYP-0.175 f(+)_x with the evaluation indices of R²= 0.732, q²= 0.571, and Q_ext²= 0.673. The given 2D-QSAR model indicated that the molecular size (E_B3LYP) and Fukui index with respect to nucleophilic attack (f(+)) were intrinsic factors influencing Re%. Three degradation pathways were subsequently proposed based on the f(+) distribution. Compared to the 2D-QSAR model, the developed 3D-QSAR model exhibited a better predictive ability, with the evaluation indices of R²= 0.989, q²= 0.696, and SEE= 0.001. The analysis of field contribution rates suggested that electrostatic field (48.2%), hydrophobic field (25.3%), and hydrogen-bond acceptor field (12.7%) were the main factors influencing Re%. These findings generated critical information for evaluating the degradation mechanisms/rules and provided theoretical bases for initially estimating the Re% of sulfonamide antibiotics undergoing FB-activated UHP process.

Joint effects of antibiotics and quorum sensing inhibitors on resistance development in bacteria

Quorum sensing inhibitors (QSIs) are promising alternatives to antibiotics. While QSIs have great application potential in a variety of fields, their joint effects with antibiotics on bacteria, especially on antibiotic resistance mutations, remain largely unexplored. Herein, we report the joint effects of four commonly used antibiotics and two QSIs on bacterial growth and resistance mutations in E. coli. It was found that QSIs presented antagonistic or additive effects with antibiotics on bacterial growth, and more importantly, QSIs exhibited an attenuating effect on antibiotic-induced resistance mutations. Further analysis demonstrated that antibiotics might enhance resistance mutations by promoting the expressions of rpoS, lexA and recA, while QSIs attenuated the mutations by promoting the expressions of mutS and uvrD. The present research provides a comprehensive understanding of the joint effects of antibiotics and QSIs on bacteria, which may benefit the risk assessment of their combined exposure.

Complexity of the Nano-Bio Interface and the Tortuous Path of Metal Oxides in Biological Systems

Metal oxide nanoparticles (NPs) have received a great deal of attention as potential theranostic agents. Despite extensive work on a wide variety of metal oxide NPs, few chemically active metal oxide NPs have received Food and Drug Administration (FDA) clearance. The clinical translation of metal oxide NP activity, which often looks so promising in preclinical studies, has not progressed as rapidly as one might expect. The lack of FDA approval for metal oxide NPs appears to be a consequence of the complex transformation of NP chemistry as any given NP passes through multiple extra- and intracellular environments and interacts with a variety of proteins and transport processes that may degrade or transform the chemical properties of the metal oxide NP. Moreover, the translational models frequently used to study these materials do not represent the final therapeutic environment well, and studies in reduced preparations have, all too frequently, predicted fundamentally different physico-chemical properties from the biological activity observed in intact organisms. Understanding the evolving pharmacology of metal oxide NPs as they interact with biological systems is critical to establish translational test systems that effectively predict future theranostic activity.

Identification of structural properties influencing the metabolism of polycyclic aromatic hydrocarbons by cytochrome P450 1A1

Cytochrome P450 1A1 (CYP1A1) has served as a known metabolic enzyme that mediates the carcinogenesis of polycyclic aromatic hydrocarbons (PAHs). However, the structural mechanism involved in the metabolic capacity remains unclear. In this study, thirty-three calculated properties representing the physicochemical and electronic properties of PAH and PAH-CYP1A1 interactions were utilized to identify the key structural properties that affect metabolic processes, including binding ability, metabolic clearance, and mutagenicity, using a quantitative structure-activity relationship (QSAR) strategy combined with docking methods, QM/MM calculations and ab initio calculations. van der Waals interactions (glide vdw) appeared to be important for PAH binding to CYP1A1 and were mainly affected by the molecular weight and hydrophobic structures of PAHs. Interaction features between PAHs and heme, including the distance between iron and carbons of PAHs (Fe_C_min) and heme vdw, coordinately influence the metabolic clearance of PAHs. Furthermore, the electronic properties (ESP neg variance) appeared to be critical for the mutagenicity of PAHs by CYP1A1 through influencing epoxide metabolite formation. The QSAR models with these key properties provide a new perspective on the structural mechanism of PAH metabolism and provide a useful in silico tool for screening, classifying and predicting PAHs for their metabolism-related toxicities and risk assessment in the environment.

Antioxidant, anti-quorum sensing, biofilm inhibitory activities and chemical composition of Patchouli essential oil: in vitro and in silico approach

The interest in naturally occurring essential oils from medicinal plants has increased extremely over the last decade markedly because they possess antimicrobial and antioxidant protective properties against different chronic diseases. Extensive survival of drug-resistant infectious bacteria depends on quorum sensing (QS) signaling network which raises the need for alternative antibacterial compounds. The aim of this study was to examine the phytochemical compounds of patchouli essential oil (PEO) and to assess its antioxidant activity. Antioxidant studies estimated by 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging method showed that the PEO has effective antioxidant activity (IC₅₀ 19.53 µg/mL). QS inhibitory activity of PEO was examined by employing the biosensor strain, Chromobacterium violaceum CV12472. At sub-lethal concentrations, PEO potentially reduced the QS regulated violacein synthesis in CV12472 without inhibiting its cell proliferation. Moreover, it also effectively reduced the production of some QS regulated virulence factors and biofilm development in P. aeruginosa PAO1 without hindering its growth. Phytochemical analysis of PEO was done by GC/MS technique. Molecular docking of PEO major compounds with QS (LasR and FabI) and biofilm regulator proteins (MvfR and Sialidase) of PAO1 was evaluated. These phytocompounds showed potential hydrogen binding interactions with these proteins. The overall results, in vitro and in silico, suggest that PEO could be applied as biocontrol agent against antibiotic resistance pathogens. Communicated by Ramaswamy H. Sarma.

A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling

Acute toxicity of the fathead minnow (Pimephales promelas) is an important indicator to evaluate the hazards and risks of compounds in aquatic environments. The aim of our study is to explore the predictive power of the quantitative structure-activity relationship (QSAR) model based on a radial basis function (RBF) neural network with the joint optimization method to study the acute toxicity mechanism, and to develop a potential acute toxicity prediction model, for fathead minnow. To ensure the symmetry and fairness of the data splitting and to generate multiple chemically diverse training and validation sets, we used a self-organizing mapping (SOM) neural network to split the modeling dataset (containing 955 compounds) characterized by PaDEL-descriptors. After preliminary selection of descriptors via the mean decrease impurity method, a hybrid quantum particle swarm optimization (HQPSO) algorithm was used to jointly optimize the parameters of RBF and select the key descriptors. We established 20 RBF-based QSAR models, and the statistical results showed that the 10-fold cross-validation results (R cv10 2) and the adjusted coefficients of determination (R adj 2) were all great than 0.7 and 0.8, respectively. The Q ext 2 of these models was between 0.6480 and 0.7317, and the R ext 2 was between 0.6563 and 0.7318. Combined with the frequency and importance of the descriptors used in RBF-based models, and the correlation between the descriptors and acute toxicity, we concluded that the water distribution coefficient, molar refractivity, and first ionization potential are important factors affecting the acute toxicity of fathead minnow. A consensus QSAR model with RBF-based models was established; this model showed good performance with R 2 = 0.9118, R cv10 2 = 0.7632, and Q ext 2 = 0.7430. A frequency weighted and distance (FWD)-based application domain (AD) definition method was proposed, and the outliers were analyzed carefully. Compared with previous studies the method proposed in this paper has obvious advantages and its robustness and external predictive power are also better than Xgboost-based model. It is an effective QSAR modeling method.

DNA pyrimidine bases in water: Insights into relative reactivity, byproducts formation and combined toxicity during chlorination

Soluble microbial products (SMPs), as precursors of disinfection byproducts (DBPs) in water treatment, are composed of polysaccharides, humic acid, proteins and DNA, and have caused widespread concerned. Pyrimidine bases (cytosine and thymine) are significant nitrogenous constituents of DNA, which could pose an adverse impact on water quality during chlorination. This study focused on the correlation between relative reactivity, formation of DBPs and combined toxicity in the chlorination of a binary pyrimidine base mixture. The relative reactivities of cytosine and thymine were quite different at a low disinfectant concentration; cytosine reacted more actively with chlorine than thymine did, at the chlorine/total pyrimidine bases molar ratio = 10. The chlorination of binary pyrimidine bases can produce both carbonous DBPs (C-DBPs) and nitrogenous DBPs (N-DBPs). In particular, the total yields of trichloromethane (TCM) and trichloronitromethane (TCNM) were lower than the additive yields of monadic cytosine and monadic thymine ("monadic" refers to "separate"), whereas the total yields of haloacetic acids (HAAs) and haloacetonitriles (HANs) were promoted evidently. High reactivity of cytosine with chlorine, greater potential of cytosine to produce specific DBPs and the alkylation of transformation products of thymine may synthetically account for the diversity in total DBPs yields, especially the increased formation of HAAs and HANs. In our toxicity trial, even though the antagonistic effect predominated at f_a > 0.4 (f_a refers to the affected fraction), the synergism at low concentration levels could enhance the combined toxicity by promoting the yields of N-DBPs.

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

The protein corona affects the clinical applications, organ targeting, and safety assessment of nanomaterials, and prediction of the protein corona would be valuable for the design of ideal nanomaterials. However, no methods to predict the protein corona are available. Overcoming the numerous quantitative and qualitative factors influencing corona formation, the present work builds models that precisely predict the functional composition of the protein corona and the cell recognition of nanoparticles (NPs) integrating machine learning and meta-analysis. This workflow provides an effective method to predict the functional composition of the protein corona that determines cell recognition to guide the synthesis and applications of NPs.

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R² (less than 0.40). Here, the performance with R² over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R² (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

Rosuvastatin as forthcoming antibiotic or as adjuvant additive agent: In vitro novel antibacterial study

INTRODUCTION:

Rosuvastatin is a lipid-lowering agent that inhibits 3-hydroxy-3-methylglutaryl coenzyme A reductase leading to a reduction of cholesterol biosynthesis. Many studies have shown an association between statins use and the reduction of sepsis. The aim of the present study was to evaluate the in vitro combined antibacterial activity of rosuvastatin and cefixime.

MATERIALS AND METHODS:

Five pathogenic bacteria isolates (Gram positive and Gram negative) were used for testing the antibacterial activity of rosuvastatin alone and in combination with cefixime.

RESULTS:

Rosuvastatin mainly inhibited Klebsiella pneumoniae and Escherichia coli where it caused zones of inhibition of (17.9 ± 0.6 mm) and (16.9 ± 0.3 mm), respectively; however, it moderately inhibited the growth of Staphylococcus epidermidis (12.9 ± 0.2 mm) and Staphylococcus aureus (12.76 ± 0.2) and produced less inhibition for Pseudomonas aeruginosa growth where it led to a zone of inhibition equal to (9.1 ± 0.5 mm). Minimal inhibitory concentration (μg/mL) of rosuvastatin was high compared to cefixime. Fractional inhibitory concentration (FIC) of rosuvastatin was low for E. coli and K. pneumoniae compared to the other types of bacterial strains. Rosuvastatin exhibited additive effects with cefixime against E. coli and K. pneumoniae. ΣFIC index was 0.536 and 0.734 for E. coli and K. pneumoniae, respectively.

CONCLUSION:

Rosuvastatin has a significant antibacterial activity against both Gram-negative and Gram-positive bacteria with a potential additive effect when used in combination with cefixime.

Individual and combined mechanistic toxicity of sulfonamides and their implications for ecological risk assessment in the Three Gorges Reservoir Area (TGRA), China

Sulfonamides (SAs) are conventional veterinary antibiotics that pose ecological risks in the aquatic environment. This study aims to evaluate the environmental concerns of SAs in the Three Gorges Reservoir Area (TGRA) and their toxicogenetic implications. Here, we employed various in vitro and in vivo bioassays to determine the combine toxicogenetic effects of SAs, which were further confirmed through applying Combination Index (CI) and Independent Action (IA) models. Among the investigated SAs, sulfamethoxazole (SMX) appeared as the individual chemical with relatively high environmental effects and elevated in vitro and in vivo toxicity. Importantly, exposure to the binary mixtures of SAs induced higher developmental toxicity and significantly perturbed the detoxification pathway in zebrafish, compared to that of individual compound exposure. Moreover, the CI and IA models indicated greater synergistic effects of SAs binary mixtures as SMX-SMR, SMX-ST, and SPY-ST on the Acinetobacter sp. Tox2 at Fa = 0.5. Contrarily, IA model predicted the additive, antagonistic and synergistic effects of SAs mixtures on the transcriptional responses of detoxification pathways in zebrafish, implying the different mode of actions (MoAs) for SAs to induce mixture toxicity in vivo. Thus, the nature of toxicological interactions of SAs should be considered while performing their ecological risk assessment.

Combination of sulfonamides, silver antimicrobial agents and quorum sensing inhibitors as a preferred approach for improving antimicrobial efficacy against Bacillus subtilis

More and more antibacterial agents are used together to treat bacterial infections in diverse fields, but the overuse of antibacterial agents may cause the environmental pollution of antibiotic resistance genes (ARGs). In order to reduce the use of antimicrobial agents, the potential joint effects of quorum-sensing inhibitors (QSIs) and traditional antimicrobial agents have been proposed to be effective. In this study, the joint effects of traditional antimicrobial agents, represented by sulfonamides (SAs) and silver antibacterial agents (silver nitrate (AgNO₃) and nanosilver (AgNP, 5 nm)), and five potential QSIs, were investigated using B. subtilis. It was found that AgNP showed higher toxicity than AgNO₃, whereas the joint effects on B. subtilis showed no difference between AgNO₃ and AgNP when they combined with SAs or QSIs, respectively. In general, AgNO₃ and AgNP presented synergetic and additive effects with QSIs, but additive and antagonistic effects with SAs; SAs exhibited synergetic, additive and antagonistic effects with different QSIs whether in binary or ternary mixed system. Moreover, it was found that the use of antimicrobials was reduced and the synergistic combined toxicity of antimicrobial agents on B. subtilis was increased through the addition of the QSIs. This study can offer a valuable reference for the combined medication of the different antimicrobial agents, which will benefit the environmental and human health.

QSAR-based investigation on antibiotics facilitating emergence and dissemination of antibiotic resistance genes: A case study of sulfonamides against mutation and conjugative transfer in Escherichia coli

Antibiotic resistance genes (ARGs), which are emerging environmental contaminants, have posed great threats to global public health. Although extensive efforts have been undertaken to investigate ARG pollution, little attention has been paid to the structural information of antibiotics when exploring their impact on the emergence and dissemination of ARGs. In this study, setting Escherichia coli (E. coli) as the test organism, the effects of sulfonamides (SAs) on growth, mutation frequency and conjugative transfer frequency were tested, and quantitative structure-activity relationship (QSAR) was used to quantitatively analyze the promotion of SAs on these biological effects and explore their possible mechanism. The constructed QSAR models reveal that SAs may increase expression of the FtsZ protein and pili in E. coli via binding to the SdiA protein, ultimately leading to SAs facilitation of growth, mutation frequency and conjugative transfer frequency. The results indicate that SAs can produce selective pressure on E. coli to promote the emergence and dissemination of ARGs. This study provides reference data for further investigation of the emergence and dissemination of ARGs under antibiotic exposure and a new perspective for the mechanistic exploration of ARG pollution.

Screening for Improved Nerve Agent Simulants and Insights into Organophosphate Hydrolysis Reactions from DFT and QSAR Modeling

The effective detoxification of chemical warfare agents, specifically nerve agents, is a pressing issue in the modern world. Due to the high toxicity of these molecules, simulants are often used in experiments as substitutes for the agents. However, there is little reason to believe that the current simulants used in the literature are optimal predictors of nerve agent reactivity. Density functional theory calculations were performed on the alkaline hydrolysis of over 100 organophosphate molecules to identify improved simulants for the G-series nerve agents soman and sarin, based on low toxicity and similarity to nerve agent hydrolysis energetics and degradation mechanism. This screening highlighted 5 molecules that have nearly identical reaction barriers to the actual agents, while being far less toxic. Quantitative structure-activity relationship (QSAR) models were also derived to determine the most significant molecular descriptors for describing the hydrolysis free energy barriers of these reactions. The optimal QSAR model was subjected to a thorough statistical analysis and validation procedure to confirm its predictive capacity, showing excellent quantitative and ranking accuracy. It was further shown that the model trained on G-series agents can reliably predict energetics for other organophosphate classes as well, including VX. Through these computational insights, experimentalists may be aided in accurately and safely studying these reactions with less toxic simulants.

Combined Toxicity of Nitro-Substituted Benzenes and Zinc to Photobacterium Phosphoreum: Evaluation and QSAR Analysis

The single toxicity (IC₅₀) of zinc (Zn) and 11 nitro-substituted benzenes to Photobacterium phosphoreum were determined, respectively. On basis of single toxicity, the joint toxicity of binary mixtures of Zn and 11 nitro-substituted benzenes at different Zn concentrations of 0.2 IC₅₀, 0.5 IC₅₀, and 0.8 IC₅₀ were measured. The joint toxicity was evaluated by toxic unit (TU) and additive index (AI) methods. The results indicated that the joint toxicity was not only depending on the Zn concentrations but also on the substituted groups of nitro-substituted benzenes. The quantitative structure-activity relation (QSAR) equations were developed and the results showed that the toxicity of nitro-substituted benzenes has different joint effect at the different Zn concentrations. At the Zn concentration of 0.2 IC₅₀, the binary joint effects were mainly antagonism and the joint toxicity was negatively related to descriptors called VE2_B(p) and TIC3. At the Zn concentration of 0.5 IC₅₀ and 0.8 IC₅₀, the binary joint effects were mainly antagonism and simple addition, and the joint toxicity was related to the same descriptor Eig06_ AEA(dm). It indicated that the joint toxic actions were similar when combined at the medium and high concentrations of Zn.

Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study

Extensive commercial use of aromatic hydrocarbons results with significant amounts of these chemicals and related by-products in waters, causing a severe ecological and health threat, thus requiring an increased attention. This study was aimed at developing models for prediction of the initial toxicity of the aromatic water-pollutants (expressed as EC₅₀ and TU₀) as well as the toxicity of their intermediates at half-life of the parent pollutant (TU_1/2). For that purpose, toxicity toward Vibrio fischery was determined for 36 single-benzene ring compounds (S-BRCs), diversified by the type, number and position of substituents. Quantitative structure-activity relationship (QSAR) methodology paired with genetic algorithm optimization tool and multiple linear regression was applied to obtain the models predicting the targeted toxicity, which are based on pure structural characteristics of the tested pollutants, avoiding thus additional experimentation. Upon derivation of the models and extensive analysis on training and test sets, 4-, 4- and 5-variable models (for EC₅₀ and TU₀, TU_1/2, respectively) were selected as the most predictive possessing 0.839<R²< 0.901 and 0.789<Q²< 0.859. The analysis of the selected descriptors indicated three major structural characteristics influencing the toxicity: electronegativity, geometry and electrotopological states of the molecule. Degradation kinetics determining as well the pathways of intermediates formation, reflected over ionization potential, was found to be an important parameter determining the toxicity in half-life.

The combined toxicity of UV/chlorinated products from binary ibuprofen (IBP) and tyrosine (Tyr) on Escherichia coli: Emphasis on their occurrence and underlying mechanism

In this study, the combined toxicity of UV/chlorinated products on Escherichia coli (E. coli) was investigated when ibuprofen (IBP) and tyrosine (Tyr) were used as two precursors. The median-effect equation and combined index (CI)-isobologram equation were used to evaluate the combined toxicity of UV/chlorinated products. Results revealed that the UV/chlorinated products originated from binary Tyr and IBP showed a synergism in toxicity on Escherichia coli at low concentration level while it turned into a clear antagonism effect above a f_a value of 0.2 in the toxicity trial. The combined toxic effects on E. coli were determined by both the potential toxicity mode of specific disinfection byproducts (DBPs) and the complicated interaction caused by Tyr and IBP. The addition of IBP decreased the yield of N-DBPs generated from Tyr, which dominated the effect of combined toxicity. Even though the antagonism predominated in toxicity effect on E. coli, the synergistic toxicity at low dose levels should be getting attention, which was more close to the natural concentration of N-DBPs in waters.

Screening Priority Factors Determining and Predicting the Reproductive Toxicity of Various Nanoparticles

Due to the numerous factors (e.g., nanoparticle [NP] properties and experimental conditions) influencing nanotoxicity, it is difficult to identify the priority factors dominating nanotoxicity. Herein, by integrating data from the literature and a random forest model, the priority factors determining reproductive toxicity were successfully screened from highly heterogeneous data. Among 10 factors from more than 18 different NPs, the NP type and the exposure pathway were found to dominantly determine NP accumulation. The reproductive toxicity of various NPs primarily depended on the NP type and the toxicity indicators. Nanoparticles containing major elements (e.g., Zn and Fe) tended to accumulate in rats but induced lower toxicity than NPs containing noble elements. Compared with other exposure pathways, i.p. injection posed significantly higher risks for NP accumulation. By combining similarity network analysis and hierarchical clustering, the sources of highly heterogeneous data were identified, the factor-toxicity dependencies were extracted and visualized, and the prediction of nanotoxicity was then achieved based on the screened priority factors. The present work provides insights for the design of animal experiments and the illustration and prediction of nanotoxicity.

Regulation of Nicotine Tolerance by Quorum Sensing and High Efficiency of Quorum Quenching Under Nicotine Stress in Pseudomonas aeruginosa PAO1

Quorum sensing (QS) regulates the behavior of bacterial populations and promotes their adaptation and survival under stress. As QS is responsible for the virulence of vast majority of bacteria, quorum quenching (QQ), the interruption of QS, has become an attractive therapeutic strategy. However, the role of QS in stress tolerance and the efficiency of QQ under stress in bacteria are seldom explored. In this study, we demonstrated that QS-regulated catalase (CAT) expression and biofilm formation help Pseudomonas aeruginosa PAO1 resist nicotine stress. CAT activity and biofilm formation in wild type (WT) and ΔrhlR strains are significantly higher than those in the ΔlasR strain. Supplementation of ΔlasI strain with 3OC12-HSL showed similar CAT activity and biofilm formation as those of the WT strain. LasIR circuit rather than RhlIR circuit is vital to nicotine tolerance. Acylase I significantly decreased the production of virulence factors, namely elastase, pyocyanin, and pyoverdine under nicotine stress compared to the levels observed in the absence of nicotine stress. Thus, QQ is more efficient under stress. To our knowledge, this is the first study to report that QS contributes to nicotine tolerance in P. aeruginosa. This work facilitates a better application of QQ for the treatment of bacterial infections, especially under stress.