Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli

Reviewing the complexities of bacterial biocide susceptibility and in vitro biocide adaptation methodologies

Decreased bacterial susceptibility to biocides raises concerns due to their influences on antibiotic resistance. The lack of standardized breakpoints, established methods, and consistent terminology complicates this research. This review summarizes techniques for studying biocide resistance mechanisms, susceptibility testing, and in-vitro adaptation methods, highlighting their benefits and limitations. Here, the challenges in studying biocide susceptibility and the need for standardized approaches in biocide research are emphasized for commonly studied biocide classes.

Targeting efflux pumps prevents the multi-step evolution of high-level resistance to fluoroquinolone in Pseudomonas aeruginosa

Antibiotic resistance is emerging as a significant global health crisis, necessitating the urgent development of novel antibiotics or alternative therapies. Although it is recognized that bacteria require multiple mutations to develop resistance levels exceeding the mutant prevention concentration, the specific mutation combinations conferring high resistance have been largely undefined. Here, we investigated the multi-step evolution of fluoroquinolone resistance in Pseudomonas aeruginosa through experimental evolution and whole-genome sequencing coupled with proteomic approaches. We discovered that in low-dose and high-dose experimental evolution scenarios, combinations of mutations in the negative regulators of efflux pumps (nfxB/mexR) and DNA gyrases (gyrA/gyrB) contributed to the high-level resistance and some of these combinations were also prevalent in clinical isolates of P. aeruginosa. Notably, the selected nfxB mutation, which resulted in the overexpression of the MexCD-OprJ efflux pump, also exhibited collateral sensitivity to aminoglycosides and enhanced antibiotic tolerance. It was further revealed that the efflux pump inhibitor phenylalanine-arginine β-naphthylamide (PAβN) could effectively prevent evolution to high-level resistance for both laboratory and clinical P. aeruginosa strains. Our work highlights the critical role of efflux pump repressor-related mutations in the evolution of high-level antibiotic resistance and demonstrates the potential of targeting these mutations to impede the evolution toward high-level resistance.IMPORTANCEIn this study, we examined the stepwise evolution of fluoroquinolone resistance in Pseudomonas aeruginosa using experimental evolution, whole-genome sequencing, and proteomic analyses. Our findings revealed that under both low-dose and high-dose conditions, mutations in efflux pump regulators (nfxB/mexR) and DNA gyrase genes (gyrA/gyrB) synergistically contributed to high-level resistance. These mutation combinations were not only observed in experimental settings but also detected in clinical isolates of P. aeruginosa. This work underscores the pivotal role of efflux pump repressor-related mutations in the progression to high-level antibiotic resistance. It also highlights the promise of targeting efflux pumps as a strategy to prevent the multi-step evolution of resistance in P. aeruginosa.

Quantitative analysis of relationship between mutation rate and speed of adaptation under antibiotic exposure in Escherichia coli

Mutations are the ultimate source of biological evolution that creates genetic variation in populations. Mutations can create new advantageous traits but can also potentially interfere with pre-existing organismal functions. Therefore, organisms may have evolved mutation rates to appropriate levels to maintain or improve their fitness. In this study, we aimed to experimentally quantify the relationship between the mutation rate and evolution of antibiotic resistance. We conducted an evolution experiment using 12 Escherichia coli mutator strains with increased mutation rates and five antibiotics. Our results demonstrated that the rate of adaptation generally increased with higher mutation rates, except in a single mutator strain with the highest mutation rate, which exhibited a significant decline in evolutionary speed. To further elucidate these findings, we developed a simple population dynamics model that successfully recapitulated the observed dependence of adaptation speed on mutation rate. These findings provide important insights into the evolution of mutation rate accompanied by the evolution.

A systematic survey of TF function in E. coli suggests RNAP stabilization is a prevalent strategy for both repressors and activators

Transcription factors (TFs) are often classified as activators or repressors, yet these context-dependent labels are inadequate to predict quantitative profiles that emerge across different promoters. A mechanistic understanding of how different regulatory sequences shape TF function is challenging due to the lack of systematic genetic control in endogenous genes. To address this, we use a library of Escherichia coli strains with precise control of TF copy number, measuring the quantitative regulatory input-output function of 90 TFs on synthetic promoters that isolate the contributions of TF binding sequence, location, and basal promoter strength to gene expression. We interpret the measured regulation of these TFs using a thermodynamic model of gene expression and uncover stabilization of RNA polymerase as a pervasive regulatory mechanism, common to both activating and repressing TFs. This property suggests ways to tune the dynamic range of gene expression through the interplay of stabilizing TF function and RNA polymerase basal occupancy, a phenomenon we confirm by measuring fold change for stabilizing TFs across synthetic promoter sequences spanning over 100-fold basal expression. Our work deconstructs TF function at a mechanistic level, providing foundational principles on how gene expression is realized across different promoter contexts, with implications for decoding the relationship between sequence and gene expression.

Genomic Pipeline for Analysis of Mutational Events in Bacteria

Unveiling the strategies of bacterial adaptation to stress constitute a challenging area of research. The understanding of mechanisms governing emergence of resistance to antimicrobials is of particular importance regarding the increasing threat of antibiotic resistance on public health worldwide. In the last decades, the fast democratization of sequencing technologies along with the development of dedicated bioinformatical tools to process data offered new opportunities to characterize genomic variations underlying bacterial adaptation. Thereby, research teams have now the possibility to dive deeper in the deciphering of bacterial adaptive mechanisms through the identification of specific genetic targets mediating survival to stress. In this chapter, we proposed a step-by-step bioinformatical pipeline enabling the identification of mutational events underlying biocidal stress adaptation associated with antimicrobial resistance development using Escherichia marmotae as an illustrative model.

Impact of untreated tannery wastewater in the evolution of multidrug-resistant bacteria in Bangladesh

The tannery industry produces one of the worst contaminants, and unsafe disposal in nearby waterbodies and landfills has become an imminent threat to public health, especially when the resulting multidrug-resistant bacteria and heavy metals enter community settings and animal food chains. In this study, we have collected 10 tannery wastewater (TWW) samples and 10 additional non-tannery wastewater (NTW) samples to compare the chemical oxygen demand (COD), pH, biological oxygen demand (BOD), dissolved oxygen (DO), total dissolved solids (TDS), chromium concentration, bacterial load, and antibiotic resistance profiles. While COD, pH, and chromium concentration data were previously published from our lab, this part of the study uncovers that TWW samples had a significantly higher bacterial load, compared to the non-tannery wastewater samples (5.89 × 104 and 9.38 × 103 cfu/mL, respectively), higher BOD and TDS values, and significantly lower DO values. The results showed that 53.4, 46.7, 40.0, and 40.0% of the TWW isolates were resistant to ceftriaxone, erythromycin, nalidixic acid, and azithromycin, respectively. On the other hand, 20.0, 30.0, 50.0, and 40.0% of the NTW isolates were resistant to the same antibiotics, respectively. These findings suggest that the TWW isolates were more resistant to antibiotics than the NTW isolates. Moreover, the TWW isolates exhibited higher multidrug resistance than the NTW isolates, 33.33, and 20.00%, respectively. Furthermore, spearman correlation analysis depicts that there is a negative correlation between BOD and bacterial load up to a certain level (r = - 0.7749, p = 0.0085). In addition, there is also a consistent negative correlation between COD and bacterial load (r = - 0.7112, p = 0.0252) and TDS and bacterial load (r = - 0.7621, p = 0.0104). These findings suggest that TWW could pose a significant risk to public health and the environment and highlight the importance of proper wastewater treatment in tannery industries.

Manure application amplified the co-selection of quaternary ammonium disinfectant and antibiotic on soil antibiotic resistome

Disinfectants and antibiotics are widely used for the prevention and control of bacterial infectious diseases. Frequent disinfection is thought to exacerbate antibiotic resistance. However, little is known about how disinfectants and antibiotics co-induce changes in the soil antibiotic resistance genes (ARGs). This study determined the ARG profiles and bacterial community dynamics between unamended soil and manure-amended soil exposed to benzalkonium chloride (C12) (BC, 10 mg kg-1) disinfectant and sulfamethazine (SMZ, 1 mg kg-1), using high-throughput quantitative PCR and 16 S rRNA gene sequencing. Manure application enriched the soil in terms of ARGs abundance and diversity, which synergistically amplified the co-selection effect of BC and SMZ on soil antibiotic resistome. Compared with the control treatment, BC and SMZ exposure had a smaller impact on the bacterial infectious diseases and antimicrobial resistance-related functions in manure-amended soil, in which bacterial communities with greater tolerance to antimicrobial substances were constructed. Manure application increased the proportion of rank I ARGs and potential human pathogenic bacteria, while BC and SMZ exposure increased the drug-resistant pathogens transmission risk. This study validated that BC and SMZ aggravated the antimicrobial resistance under manure application, providing a reference for managing the spread risk of antimicrobial resistance in agricultural activities.

Adaptive laboratory evolution of Salmonella enterica in acid stress

Introduction

Adaptive laboratory evolution (ALE) studies play a crucial role in understanding the adaptation and evolution of different bacterial species. In this study, we have investigated the adaptation and evolution of Salmonella enterica serovar Enteritidis to acetic acid using ALE.

Materials and methods

Acetic acid concentrations below the minimum inhibitory concentration (sub-MIC) were used. Four evolutionary lineages (EL), namely, EL1, EL2, EL3, and EL4, of S. Enteritidis were developed, each demonstrating varying levels of resistance to acetic acid.

Results

The acetic acid MIC of EL1 remained constant at 27 mM throughout 70 days, while the MIC of EL2, EL3, and EL4 increased throughout the 70 days. EL4 was adapted to the highest concentration of acetic acid (30 mM) and demonstrated the highest increase in its MIC against acetic acid throughout the study, reaching an MIC of 35 mM on day 70. The growth rates of the evolved lineages increased over time and were dependent on the concentration of acetic acid used during the evolutionary process. EL4 had the greatest increase in growth rate, reaching 0.33 (h-1) after 70 days in the presence of 30 mM acetic acid as compared to EL1, which had a growth rate of 0.2 (h-1) after 70 days with no exposure to acetic acid. Long-term exposure to acetic acid led to an increased MIC of human antibiotics such as ciprofloxacin and meropenem against the S. enterica evolutionary lineages. The MIC of ciprofloxacin for EL1 stayed constant at 0.016 throughout the 70 days while that of EL4 increased to 0.047. Bacterial whole genome sequencing revealed single-nucleotide polymorphisms in the ELs in various genes known to be involved in S. enterica virulence, pathogenesis, and stress response including phoP, phoQ, and fhuA. We also observed genome deletions in some of the ELs as compared to the wild-type S. Enteritidis which may have contributed to the bacterial acid adaptation.

Discussion

This study highlights the potential for bacterial adaptation and evolution under environmental stress and underscores the importance of understanding the development of cross resistance to antibiotics in S. enterica populations. This study serves to enhance our understanding of the pathogenicity and survival strategies of S. enterica under acetic acid stress.

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

Multidrug Resistance Profiles and Resistance Mechanisms to β-Lactams and Fluoroquinolones in Bacterial Isolates from Hospital Wastewater in Bangladesh

Multidrug resistance (MDR) is one of the deadliest public health concerns of the 21st century, rendering many powerful antibiotics ineffective. The current study provides important insights into the prevalence and mechanisms of antibiotic resistance in hospital wastewater isolates. In this study, we determined the MDR profile of 68 bacterial isolates collected from five different hospitals in Dhaka, Bangladesh. Of them, 48 bacterial isolates were identified as Enterobacteriaceae. Additionally, we investigated the prevalence and distribution of five beta-lactam resistance genes, as well as quinolone resistance mechanisms among the isolates. The results of this study showed that 87% of the wastewater isolates were resistant to at least three different antibiotic classes, as revealed using the disc diffusion method. Resistance to β-lactams was the most common, with 88.24% of the isolates being resistant, closely followed by macrolides (80.88% resistant). Polymyxin was found to be the most effective against wastewater isolates, with 29.41% resistant isolates. The most common β-lactam resistance genes found in wastewater isolates were blaTEM (76.09%), blaCTX-M1 (71.74%), and blaNDM (67.39%). Two missense mutations in the quinolone resistance-determining region (QRDR) of gyrA (S83L and D87N) and one in both parC (S80I) and parE (S458A) were identified in all isolates, and one in parE (I529L), which had not previously been identified in Bangladesh. These findings suggest that hospital wastewater acts as an important reservoir of antibiotic-resistant bacteria wherein resistance mechanisms to β-lactams and fluoroquinolones are obvious. Our data also emphasize the need for establishing a nationwide surveillance system for antibiotic resistance monitoring to ensure that hospitals sanitize their wastewater before disposal, and regulation to ensure hospital wastewater is kept away from community settings.

ShinyBioHEAT: an interactive shiny app to identify phenotype driver genes in E.coli and B.subtilis

SUMMARY

In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers.

AVAILABILITY AND IMPLEMENTATION

Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.

CovET: A covariation-evolutionary trace method that identifies protein structure-function modules

Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

A Systematic Review on the Occurrence of Antimicrobial-Resistant Escherichia coli in Poultry and Poultry Environments in Bangladesh between 2010 and 2021

Antimicrobial resistance (AMR) is a significant public health issue in Bangladesh like many other developing countries where data on resistance trends are scarce. Moreover, the existence of multidrug-resistant (MDR) Escherichia coli exerts an ominous effect on the poultry sector. Therefore, the current systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was conducted to find out the AMR scenarios in E. coli isolates sourced from poultry and poultry environments in Bangladesh between 2010 and 2021. Following the PRISMA guidelines, a total of 17 published scientific articles were selected for this systematic review. This review revealed that 18 out of 64 districts in Bangladesh reported E. coli in poultry, having a higher prevalence (combined prevalence: 69.3%, 95% confidence interval, CI: 67.3-71%). Moreover, the prevalence ranged from 24.3% to 100%. This review found that E. coli isolates showed resistance to 14 antimicrobial classes and 45 different antimicrobial agents, including the last-line (reserve group) antibiotics and banned antimicrobial categories for the treatment of infections in agricultural animals. Phenotypic resistance of E. coli against penicillins and beta-lactamase inhibitors (20.2%-100%), cephalosporins (1.9%-100%), fluoroquinolones (5.98%-100%), aminoglycosides (6%-100%), tetracyclines (17.7%-100%), carbapenems (13.6%-72.7%), macrolides (11.8%-100%), polymyxins (7.9%-100%), phenicols (20%-97.2%), sulfa drugs (44.7%-100%), cephamycins (21.4%-48.8%), nitrofurans (21.4%-63.2%), monobactams (1.2%), and glycylcyclines (2.3%) was recorded in the last decades in Bangladesh. Also, 14 articles reported MDR E. coli in poultry, including a 100% MDR in nine articles and a 92.7% (95% CI: 91.2-94%) combined percentage of MDR E. coli isolates. Twenty-four different AMR genes encoding resistance to beta-lactams (bla _TEM, bla _CTX-M-1, bla _CTX-M-2, bla _CTX-M-9, bla _OXA-1, bla _OXA-47, bla _SHV, and CITM), colistin (mcr1 and mcr3), fluoroquinolones (qnrB and qnrS), tetracyclines (tetA, tetB, and tetC), sulfonamides (sulI and sulII), trimethoprim (dfrA1), aminoglycosides (rmtB), streptomycin (aadA1), gentamicin (aac-3-IV), erythromycin (ereA), and chloramphenicol (catA1 and cmlA) were detected in E. coli isolates. The presence of MDR E. coli and their corresponding resistance genes in poultry and poultry environments is an alarming issue for all health communities in Bangladesh. We suggest a regular antimicrobial surveillance program with a strong One Health approach to lessen the hazardous effects of AMR E. coli in poultry industries in Bangladesh.

Antimicrobial peptides: Defending the mucosal epithelial barrier

The recent epidemic caused by aerosolized SARS-CoV-2 virus illustrates the importance and vulnerability of the mucosal epithelial barrier against infection. Antimicrobial proteins and peptides (AMPs) are key to the epithelial barrier, providing immunity against microbes. In primitive life forms, AMPs protect the integument and the gut against pathogenic microbes. AMPs have also evolved in humans and other mammals to enhance newer, complex innate and adaptive immunity to favor the persistence of commensals over pathogenic microbes. The canonical AMPs are helictical peptides that form lethal pores in microbial membranes. In higher life forms, this type of AMP is exemplified by the defensin family of AMPs. In epithelial tissues, defensins, and calprotectin (complex of S100A8 and S100A9) have evolved to work cooperatively. The mechanisms of action differ. Unlike defensins, calprotectin sequesters essential trace metals from microbes, which inhibits growth. This review focuses on defensins and calprotectin as AMPs that appear to work cooperatively to fortify the epithelial barrier against infection. The antimicrobial spectrum is broad with overlap between the two AMPs. In mice, experimental models highlight the contribution of both AMPs to candidiasis as a fungal infection and periodontitis resulting from bacterial dysbiosis. These AMPs appear to contribute to innate immunity in humans, protecting the commensal microflora and restricting the emergence of pathobionts and pathogens. A striking example in human innate immunity is that elevated serum calprotectin protects against neonatal sepsis. Calprotectin is also remarkable because of functional differences when localized in epithelial and neutrophil cytoplasm or released into the extracellular environment. In the cytoplasm, calprotectin appears to protect against invasive pathogens. Extracellularly, calprotectin can engage pathogen-recognition receptors to activate innate immune and proinflammatory mechanisms. In inflamed epithelial and other tissue spaces, calprotectin, DNA, and histones are released from degranulated neutrophils to form insoluble antimicrobial barriers termed neutrophil extracellular traps. Hence, calprotectin and other AMPs use several strategies to provide microbial control and stimulate innate immunity.