The use of machine learning methodologies to analyse antibiotic and biocide susceptibility in Staphylococcus aureus

An exploration of descriptive machine learning approaches for antimicrobial resistance: Multidrug resistance patterns in Salmonella enterica

Salmonellosis is one of the most common foodborne diseases worldwide, with the ability to infect humans and animals. Antimicrobial resistance (AMR) and, particularly, multidrug resistance (MDR) among Salmonella enterica poses a risk to human health. Antimicrobial use (AMU) regulations in livestock have been implemented to reduce AMR and MDR in foodborne pathogens. In this study, we used an integrated machine learning approach to investigate Salmonella AMR and MDR patterns before and after the implementation of AMU restrictions in agriculture in the United States. For this purpose, Salmonella isolates from cattle in the National Antimicrobial Resistance Monitoring System (NARMS) dataset were analysed using three descriptive models consisting of hierarchical clustering, network analysis, and association rule mining. The analysis showed the impact of the United States' 2012 extra-label cephalosporin regulations on AMR trends and revealed a distinctive MDR pattern in the Dublin serotype. The results also indicated that each descriptive model provides insights on a specific aspect of resistance patterns and, therefore, combining these approaches make it possible to gain a deeper understanding of AMR.

Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility

Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the "Genome Feature Extractor Pipeline" that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.

Disinfectants and antiseptics: mechanisms of action and resistance

Chemical biocides are used for the prevention and control of infection in health care, targeted home hygiene or controlling microbial contamination for various industrial processes including but not limited to food, water and petroleum. However, their use has substantially increased since the implementation of programmes to control outbreaks of methicillin-resistant Staphylococcus aureus, Clostridioides difficile and severe acute respiratory syndrome coronavirus 2. Biocides interact with multiple targets on the bacterial cells. The number of targets affected and the severity of damage will result in an irreversible bactericidal effect or a reversible bacteriostatic one. Most biocides primarily target the cytoplasmic membrane and enzymes, although the specific bactericidal mechanisms vary among different biocide chemistries. Inappropriate usage or low concentrations of a biocide may act as a stressor while not killing bacterial pathogens, potentially leading to antimicrobial resistance. Biocides can also promote the transfer of antimicrobial resistance genes. In this Review, we explore our current understanding of the mechanisms of action of biocides, the bacterial resistance mechanisms encompassing both intrinsic and acquired resistance and the influence of bacterial biofilms on resistance. We also consider the impact of bacteria that survive biocide exposure in environmental and clinical contexts.

Current Understanding of Potential Linkages between Biocide Tolerance and Antibiotic Cross-Resistance

Antimicrobials (e.g., antibiotics and biocides) are invaluable chemicals used to control microbes in numerous contexts. Because of the simultaneous use of antibiotics and biocides, questions have arisen as to whether environments commonly treated with biocides (e.g., hospitals, food processing, wastewater, agriculture, etc.) could act as a reservoir for the development of antibiotic cross-resistance. Theoretically, cross-resistance could occur if the mechanism of bacterial tolerance to biocides also resulted in antibiotic resistance. On the other hand, biocides would likely present a higher evolutionary barrier to the development of resistance given the different modes of action between biocides and antibiotics and the broad-based physicochemical effects associated with most biocides. Published studies have shown that the induction of biocide tolerance in a laboratory can result in cross-resistance to some antibiotics, most commonly hypothesized to be due to efflux pump upregulation. However, testing of environmental isolates for biocide tolerance and antibiotic cross-resistance has yielded conflicting results, potentially due to the lack of standardized testing. In this review, we aim to describe the state of the science on the potential linkage between biocide tolerance and antibiotic cross-resistance. Questions still remain about whether the directed evolution of biocide tolerance and the associated antibiotic cross-resistance in a laboratory are or are not representative of real-world settings. Thus, research should continue to generate informative data to guide policies and preserve these tools' utility and availability.

eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning

Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70-0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.

Impact of benzalkonium chloride, benzethonium chloride and chloroxylenol on bacterial antimicrobial resistance

This review examined 3655 articles on benzalkonium chloride (BKC), benzethonium chloride (BZT) and chloroxylenol (CHO) aiming to understand their impact on antimicrobial resistance. Following the application of inclusion/exclusion criteria, only 230 articles were retained for analysis; 212 concerned BKC, with only 18 for CHO and BZT. Seventy-eight percent of studies used MIC to measure BKC efficacy. Very few studies defined the term 'resistance' and 85% of studies defined 'resistance' as <10-fold increase (40% as low as 2-fold) in MIC. Only a few in vitro studies reported on formulated products and when they did, products performed better. In vitro studies looking at the impact of BKC exposure on bacterial resistance used either a stepwise training protocol or exposure to constant BKC concentrations. In these, BKC exposure resulted in elevated MIC or/and MBC, often associated with efflux, and at time, a change in antibiotic susceptibility profile. The clinical relevance of these findings was, however, neither reported nor addressed. Of note, several studies reported that bacterial strains with an elevated MIC or MBC remained susceptible to the in-use BKC concentration. BKC exposure was shown to reduce bacterial diversity in complex microbial microcosms, although the clinical significance of such a change has not been established. The impact of BKC exposure on the dissemination of resistant genes (notably efflux) remains speculative, although it manifests that clinical, veterinary and food isolates with elevated BKC MIC carried multiple efflux pump genes. The correlation between BKC usage and gene carriage, maintenance and dissemination has also not been established. The lack of clinical interpretation and significance in these studies does not allow to establish with certainty the role of BKC on AMR in practice. The limited literature and BZT and CHO do not allow to conclude that these will impact negatively on emerging bacterial resistance in practice.

Paving the way for precise diagnostics of antimicrobial resistant bacteria

The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.

Towards a robust out-of-the-box neural network model for genomic data

Background

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity.

Results

Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study.

Conclusions

While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Analysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram

Multidrug resistance (MDR) surveillance consists of reporting MDR prevalence and MDR phenotypes. Detailed knowledge of the specific associations underlying MDR patterns can allow antimicrobial stewardship programs to accurately identify clinically relevant resistance patterns. We applied machine learning and graphical networks to quantify and visualize associations between resistance traits in a set of 1,091 Staphylococcus aureus isolates collected from one New York hospital between 2008 and 2018. Antimicrobial susceptibility testing was performed using reference broth microdilution. The isolates were analyzed by year, methicillin susceptibility, and infection site. Association mining was used to identify resistance patterns that consisted of two or more individual antimicrobial resistance (AMR) traits and quantify the association among the individual resistance traits in each pattern. The resistance patterns captured the majority of the most common MDR phenotypes and reflected previously identified pairwise relationships between AMR traits in S. aureus Associations between β-lactams and other antimicrobial classes (macrolides, lincosamides, and fluoroquinolones) were common, although the strength of the association among these antimicrobial classes varied by infection site and by methicillin susceptibility. Association mining identified associations between clinically important AMR traits, which could be further investigated for evidence of resistance coselection. For example, in skin and skin structure infections, clindamycin and tetracycline resistance occurred together 1.5 times more often than would be expected if they were independent from one another. Association mining efficiently discovered and quantified associations among resistance traits, allowing these associations to be compared between relevant subsets of isolates to identify and track clinically relevant MDR.

Minimum Inhibitory Concentration of Glyphosate and a Glyphosate-Containing Herbicide in Salmonella enterica Isolates Originating from Different Time Periods, Hosts, and Serovars

Glyphosate, the active compound of Roundup, is one of the most used pesticides in the world. Its residues are often detected in animal feed, but the impact on the animal gut microbiota and on pathogens of the intestine has not intensively been investigated. In this study, we analyzed the minimum inhibitory concentration (MIC) of glyphosate isopropylamine salt and a common glyphosate-containing herbicide formulation in 225 Salmonella enterica isolates by broth microdilution. A bacteriostatic effect of glyphosate on Salmonella growth was detected at the concentration range of 10 to 80 mg/mL for both the active ingredient and the ready-to-use formulation. Time/year of isolation, host species, and serovars revealed a statistically significant influence on MIC values. Recently collected Salmonella isolates had significantly higher MIC values for glyphosate and the glyphosate-containing product compared with isolates collected between 1981 and 1990. Isolates from pigs showed significantly higher MIC values compared with isolates from poultry, and isolates of the Salmonella serovar Typhimurium had significantly higher MIC values than Salmonella Enteritidis and Infantis isolates.

Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 14,418 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.

Antibiotic ResistanceCan Be Enhanced in Gram-Positive Species by Some Biocidal Agents Used for Disinfection

Some biocidal agents used for disinfection have been described to enhance antibiotic resistance in Gram-negative species. The aim of this review was therefore to evaluate the effect of 13 biocidal agents at sublethal concentrations on antibiotic resistance in Gram-positive species. A MEDLINE search was performed for each biocidal agent on antibiotic tolerance, antibiotic resistance, horizontal gene transfer, and efflux pump. Most data were reported with food-associated bacterial species. In cells adapted to benzalkonium chloride, a new resistance was most frequently found to ampicillin (seven species), cefotaxime and sulfamethoxazole (six species each), and ceftazidime (five species), some of them with relevance for healthcare-associated infections such as Enterococcus faecium and Enterococcus faecalis. With chlorhexidine, a new resistance was often found to imipenem (ten species) as well as cefotaxime, ceftazidime, and tetracycline (seven species each). Cross-resistance was also found with triclosan and ceftazidime (eight species), whereas it was very uncommon for didecyldimethylammonium chloride or hydrogen peroxide. No cross-resistance to antibiotics has been described after low level exposure to glutaraldehyde, ethanol, propanol, peracetic acid, octenidine, povidone iodine, sodium hypochlorite, and polyhexanide. Preference should be given to disinfectant formulations based on biocidal agents with a low or no selection pressure potential.

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, was used to generate extreme gradient boosting (XGBoost)-based machine learning models for predicting MICs for 15 antibiotics. The MIC prediction models had an overall average accuracy of 95% within ±1 2-fold dilution step (confidence interval, 95% to 95%), an average very major error rate of 2.7% (confidence interval, 2.4% to 3.0%), and an average major error rate of 0.1% (confidence interval, 0.1% to 0.2%). The model predicted MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less than 500 genomes. We also show that our approach for predicting MICs is stable over time, despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole-genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

An assay for determining the susceptibility of Salmonella isolates to commercial and household biocides

Poultry and meat products contaminated with Salmonella enterica are a major cause of foodborne illness in the United States. The food industries use a wide variety of antimicrobial interventions to reduce bacterial contamination. However, little is known about Salmonella susceptibility to these compounds and some studies have shown a concerning link between biocide resistance and antibiotic resistance. To investigate this, a 96 well panel of 17 common household and commercially used biocides was designed to determine the minimum inhibitory concentrations (MIC) of these compounds for Salmonella. The panel contained two-fold serial dilutions of chemicals including Dodecyltrimethylammonium chloride (DC), Benzalkonium chloride (BKC), Cetylpyridinium chloride (CPC), Hexadecyltrimethylammonium bromide (HB), Hexadecyltrimethylammonium chloride (HC), Acetic acid (AA), Lactic acid (LA), Citric acid (CA), Peroxyacetic acid (PXA), Acidified sodium chlorite (ASC), Sodium hypochlorite (SHB), 1,3 dibromo, 5,5 dimethylhydantoin (DBH), Chlorhexidine (CHX), Sodium metasilicate (SM), Trisodium phosphate (TSP), Arsenite (ARI), and Arsenate (ARA). The assay was used to test the susceptibility of 88 multidrug resistant (MDR) Salmonella isolates from animal sources. Bacteria are defined as multidrug resistant (MDR) if it exhibited non-susceptibility to at least one agent in three or more antimicrobial categories. The concentration of biocide at which ≥50% of the isolates could not grow was designated as the minimum inhibitory concentration or MIC50 and was used as the breakpoint in this study. The MIC50 (μg ml-1) for the tested MDR Salmonella was 256 for DC, 40 for BKC, 80 for CPC. HB and HC, 1,640 for AA, 5664 for LA, 3,156 for CA, 880 for PXA, 320 for ASC, 3.0 for CHX, 1,248 for DBH, 3,152 (6%) for SHB, 60,320 for SM, 37,712 for TSP, 56 for ARI and 832 for ARA. A few isolates were not susceptible at the MIC50 breakpoint to some chemicals indicating possible resistance. Isolates with MICs of two 2-fold dilutions above the MIC50 were considered resistant. Biocides for which resistant isolates were detected included CPC (n = 1 isolate), HB (1), CA (18), ASC (7), CHX (22), ARA (16), and ARI (4). There was no correlation detected between the biocide susceptibility of Salmonella isolates and antibiotic resistance. This assay can determine the MICs of bacteria to 17 biocides in a single test and will be useful in evaluating the efficacy of biocides and to detect the development of resistance to them.

Data Science Priorities for a University Hospital-Based Institute of Infectious Diseases: A Viewpoint

Automation of laboratory tests, bioinformatic analysis of biological sequences, and professional data management are used routinely in a modern university hospital-based infectious diseases institute. This dates back to at least the 1980s. However, the scientific methods of this 21st century are changing with the increased power and speed of computers, with the "big data" revolution having already happened in genomics and environment, and eventually arriving in medical informatics. The research will be increasingly "data driven," and the powerful machine learning methods whose efficiency is demonstrated in daily life will also revolutionize medical research. A university-based institute of infectious diseases must therefore not only gather excellent computer scientists and statisticians (as in the past, and as in any medical discipline), but also fully integrate the biologists and clinicians with these computer scientists, statisticians, and mathematical modelers having a broad culture in machine learning, knowledge representation, and knowledge discovery.

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.

Dissemination of Novel Antimicrobial Resistance Mechanisms through the Insertion Sequence Mediated Spread of Metabolic Genes

The widely used biocide triclosan selectively targets FabI, the NADH-dependent trans-2-enoyl-acyl carrier protein (ACP) reductase, which is also an important target for the development of narrow spectrum antibiotics. The analysis of triclosan resistant Staphylococcus aureus isolates had previously shown that in about half of the strains, the mechanism of triclosan resistance consists on the heterologous duplication of the triclosan target gene due to the acquisition of an additional fabI allele derived from Staphylococcus haemolyticus (sh-fabI). In the current work, the genomic sequencing of 10 of these strains allowed the characterization of two novel composite transposons TnSha1 and TnSha2 involved in the spread of sh-fabI. TnSha1 harbors one copy of IS1272, whereas TnSha2 is a 11.7 kb plasmid carrying TnSha1 present either as plasmid or in an integrated form generally flanked by two IS1272 elements. The target and mechanism of integration for IS1272 and TnSha1 are novel and include targeting of DNA secondary structures, generation of blunt-end deletions of the stem-loop and absence of target duplication. Database analyses showed widespread occurrence of these two elements in chromosomes and plasmids, with TnSha1 mainly in S. aureus and with TnSha2 mainly in S. haemolyticus and S. epidermidis. The acquisition of resistance by means of an insertion sequence-based mobilization and consequent duplication of drug-target metabolic genes, as observed here for sh-fabI, is highly reminiscent of the situation with the ileS2 gene conferring mupirocin resistance, and the dfrA and dfrG genes conferring trimethoprim resistance both of which are mobilized by IS257. These three examples, which show similar mechanisms and levels of spread of metabolic genes linked to IS elements, highlight the importance of this genetic strategy for recruitment and rapid distribution of novel resistance mechanisms in staphylococci.

Use of a predictive protocol to measure the antimicrobial resistance risks associated with biocidal product usage

BACKGROUND

In this study we assessed the propensity of biocide exposure in the development of antimicrobial resistance in bacteria.

METHODS

Our protocol is based on reporting changes in established antimicrobial susceptibility profiles in biocides and antibiotics after during use exposure to a product. The during use exposure reflects worse conditions of product use during application. It differs from the term low concentration, which usually reflects a concentration below the minimal inhibitory concentration, but not necessarily a concentration that occurs in practice.

RESULTS

Our results showed that exposure to triclosan (0.0004%) was associated with a high risk of developing resistance and cross-resistance in Staphylococcus aureus and Escherichia coli. This was not observed with exposure to chlorhexidine (0.00005%) or a hydrogen peroxide-based biocidal product (in during use conditions). Interestingly, exposure to a low concentration of hydrogen peroxide (0.001%) carried a risk of emerging resistance to antibiotics if the presence of the oxidizing agent was maintained. We observed a number of unstable clinical resistances to antibiotics after exposure to the cationic biocide and oxidizing agent, notably to tobramycin and ticarcillin-clavulanic acid.

CONCLUSIONS

Using a decision tree based on the change in antimicrobial susceptibility test results, we were able to provide information on the effect of biocide exposure on the development of bacterial resistance to antimicrobials. Such information should address the call from the U.S. Food and Drug Administration and European Union Biocidal Products Regulation for manufacturers to provide information on antimicrobial resistance and cross-resistance in bacteria after the use of their product.

Bacterial Multidrug Efflux Pumps: Much More Than Antibiotic Resistance Determinants

Bacterial multidrug efflux pumps are antibiotic resistance determinants present in all microorganisms. With few exceptions, they are chromosomally encoded and present a conserved organization both at the genetic and at the protein levels. In addition, most, if not all, strains of a given bacterial species present the same chromosomally-encoded efflux pumps. Altogether this indicates that multidrug efflux pumps are ancient elements encoded in bacterial genomes long before the recent use of antibiotics for human and animal therapy. In this regard, it is worth mentioning that efflux pumps can extrude a wide range of substrates that include, besides antibiotics, heavy metals, organic pollutants, plant-produced compounds, quorum sensing signals or bacterial metabolites, among others. In the current review, we present information on the different functions that multidrug efflux pumps may have for the bacterial behaviour in different habitats as well as on their regulation by specific signals. Since, in addition to their function in non-clinical ecosystems, multidrug efflux pumps contribute to intrinsic, acquired, and phenotypic resistance of bacterial pathogens, the review also presents information on the search for inhibitors of multidrug efflux pumps, which are currently under development, in the aim of increasing the susceptibility of bacterial pathogens to antibiotics.

Significant Differences Characterise the Correlation Coefficients between Biocide and Antibiotic Susceptibility Profiles in Staphylococcus aureus

There is a growing concern by regulatory authorities for the selection of antibiotic resistance caused by the use of biocidal products. We aimed to complete the detailed information on large surveys by investigating the relationship between biocide and antibiotic susceptibility profiles of a large number of Staphylococcus aureus isolates using four biocides and antibiotics commonly used in clinical practice. The minimal inhibitory concentration (MIC) for most clinically-relevant antibiotics was determined according to the standardized methodology for over 1600 clinical S. aureus isolates and compared to susceptibility profiles of benzalkonium chloride, chlorhexidine, triclosan, and sodium hypochlorite. The relationship between antibiotic and biocide susceptibility profiles was evaluated using non-linear correlations. The main outcome evidenced was an absence of any strong or moderate statistically significant correlation when susceptibilities of either triclosan or sodium hypochlorite were compared for any of the tested antibiotics. On the other hand, correlation coefficients for MICs of benzalkonium chloride and chlorhexidine were calculated above 0.4 for susceptibility to quinolones, beta-lactams, and also macrolides. Our data do not support any selective pressure for association between biocides and antibiotics resistance and furthermore do not allow for a defined risk evaluation for some of the compounds. Importantly, our data clearly indicate that there does not involve any risk of selection for antibiotic resistance for the compounds triclosan and sodium hypochlorite. These data hence infer that biocide selection for antibiotic resistance has had so far a less significant impact than feared.

High Prevalence of Biocide Resistance Determinants in Staphylococcus aureus Isolates from Three African Countries

We assessed the prevalence of six biocide resistance genes among 82 methicillin-resistant Staphylococcus aureus (MRSA) and 219 methicillin-susceptible S. aureus (MSSA) isolates from three African countries; the prevalence was very high for sepA (95.3%), mepA (89.4%), and norA (86.4%), intermediate for lmrS (60.8%) and qacAB (40.5%), and low for smr (3.7%). A significant association between biocide resistance genes and antibiotic resistance was observed, and a new cutoff MIC of ≥1 mg/liter for chlorhexidine nonsusceptibility was defined.

Antibacterial activity of Pinus elliottii and its major compound, dehydroabietic acid, against multidrug-resistant strains

Antibiotic-resistant bacteria have emerged from the widespread use of antibiotics worldwide and have prompted the search for new sources of antimicrobial substances. Pinus spp. contain several bioactive compounds consisting mainly of terpenes, terpenoids and some other aromatic and aliphatic constituents. These compounds exert important biological effects, and pine oils have found wide application in the industry. In the present study, we have evaluated the potential activity of the resin-oil of Pinus elliottii and its major compound dehydroabietic acid (DA) against multiresistant bacteria by MIC, minimum bactericidal concentration and time-kill assays. The MIC of the resin-oil of P. elliottii varied between 25 and 100 µg ml−1. As for DA, the MIC and minimum bactericidal concentration varied between 6.25 and 50 and between 6.25 and 100 µg ml−1, respectively. The time-kill assay conducted with DA at 6.25 µg ml−1 evidenced bactericidal activity against Staphylococcus epidermidis (American Type Culture Collection 14990) within 24 h. On the basis of these results, the resin-oil of P. elliottii and its major compound DA play an important part in the search for novel sources of agents that can act against multiresistant bacteria.

Recent advances in the potential interconnection between antimicrobial resistance to biocides and antibiotics

Interconnection between microbial resistance to biocides and antibiotics is a topic of increasing interest given the recent changes in European legislation and claims of a risk of biocide use on bacterial resistance. In the second International Conference on Antimicrobial Research held in Lisbon in November 2012, a workshop specifically addressed this topic, presentations included approaches to risk assessment and investigations into the molecular mechanisms of biocide resistance and co- and cross-resistance to antibiotics. The overall conclusion was that, even if each biocide represents a specific case, there is scientific evidence that biocides select for biocide resistance, but that there is, so far, no conclusive evidence that this also determined or will determine an increase in antibiotic resistance.

Evaluation of reduced susceptibility to quaternary ammonium compounds and bisbiguanides in clinical isolates and laboratory-generated mutants of Staphylococcus aureus

The MICs and minimum bactericidal concentrations (MBCs) for the biocides benzalkonium chloride and chlorhexidine were determined against 1,602 clinical isolates of Staphylococcus aureus. Both compounds showed unimodal MIC and MBC distributions (2 and 4 or 8 mg/liter, respectively) with no apparent subpopulation with reduced susceptibility. To investigate further, all isolates were screened for qac genes, and 39 of these also had the promoter region of the NorA multidrug-resistant (MDR) efflux pump sequenced. The presence of qacA, qacB, qacC, and qacG genes increased the mode MIC, but not MBC, to benzalkonium chloride, while only qacA and qacB increased the chlorhexidine mode MIC. Isolates with a wild-type norA promoter or mutations in the norA promoter had similar biocide MIC distributions; notably, not all clinical isolates with norA mutations were resistant to fluoroquinolones. In vitro efflux mutants could be readily selected with ethidium bromide and acriflavine. Multiple passages were necessary to select mutants with biocides, but these mutants showed phenotypes comparable to those of mutants selected by dyes. All mutants showed changes in the promoter region of norA, but these were distinct from this region of the clinical isolates. Still, none of the in vitro mutants displayed fitness defects in a killing assay in Galleria mellonella larvae. In conclusion, our data provide an in-depth comparative overview on efflux in S. aureus mutants and clinical isolates, showing also that plasmid-encoded efflux pumps did not affect bactericidal activity of biocides. In addition, current in vitro tests appear not to be suitable for predicting levels of resistance that are clinically relevant.