Mechanism of action-based classification of antibiotics using high-content bacterial image analysis

Dramatic increase in antimicrobial resistance in ESKAPE clinical isolates over the 2010-2020 decade in India

RATIONALE AND OBJECTIVES

ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) constitute a threat to humans worldwide. India is now the most populous country. The goal was to investigate the evolution of the rates of antimicrobial resistance in ESKAPE pathogens across India over the 2010-20 decade.

METHODS

The data (89 studies) were retrieved from the Medline PubMed repository using specific keywords.

RESULTS

The study of 20 177 ESKAPE isolates showed that A. baumannii isolates were the most represented (35.9%, n = 7238), followed by P. aeruginosa (25.3%, n = 5113), K. pneumoniae (19.5%, n = 3934), S. aureus (16.3%, n = 3286), E. faecium (2.6%, n = 517) and Enterobacter spp. (0.4%, n = 89). A notable increase in the resistance rates to antimicrobial agents occurred over the 2010-20 decade. The most important levels of resistance were observed in 2016-20 for A. baumannii (90% of resistance to the amoxicillin-clavulanate combination) and K. pneumoniae (81.6% of resistance to gentamycin). The rise in β-lactamase activities was correlated with an increase in the positivity of Gram-negative isolates for β-lactamase genes.

CONCLUSIONS

This review highlighted that, in contrast to developed countries that kept resistance levels under control, a considerable increase in resistance to various classes of antibiotics occurred in ESKAPE pathogens in India over the 2010-2020 decade.

Antibacterial Marinopyrroles and Pseudilins Act as Protonophores

Elucidating the mechanism of action (MoA) of antibacterial natural products is crucial to evaluating their potential as novel antibiotics. Marinopyrroles, pentachloropseudilin, and pentabromopseudilin are densely halogenated, hybrid pyrrole-phenol natural products with potent activity against Gram-positive bacterial pathogens like Staphylococcus aureus. However, the exact way they exert this antibacterial activity has not been established. In this study, we explore their structure-activity relationship, determine their spatial location in bacterial cells, and investigate their MoA. We show that the natural products share a common MoA based on membrane depolarization and dissipation of the proton motive force (PMF) that is essential for cell viability. The compounds show potent protonophore activity but do not appear to destroy the integrity of the cytoplasmic membrane via the formation of larger pores or interfere with the stability of the peptidoglycan sacculus. Thus, our current model for the antibacterial MoA of marinopyrrole, pentachloropseudilin, and pentabromopseudilin stipulates that the acidic compounds insert into the membrane and transport protons inside the cell. This MoA may explain many of the deleterious biological effects in mammalian cells, plants, phytoplankton, viruses, and protozoans that have been reported for these compounds.

The tale of antibiotics beyond antimicrobials: Expanding horizons

Antibiotics had proved to be a godsend for mankind since their discovery. They were once the magical solution to the vexing problem of infection-related deaths. German scientist Paul Ehrlich had termed salvarsan as the silver bullet to treatsyphilis.As time passed, the magic of newly discovered silver bullets got tarnished with raging antibiotic resistance among bacteria and associated side-effects. Still, antibiotics remain the primary line of treatment for bacterial infections. Our understanding of their chemical and biological activities has increased immensely with advancement in the research field. Non-antibacterial effects of antibiotics are studied extensively to optimise their safer, broad-range use. These non-antibacterial effects could be both useful and harmful to us. Various researchers across the globe including our lab are studying the direct/indirect effects and molecular mechanisms behind these non-antibacterial effects of antibiotics. So, it is interesting for us to sum up the available literature. In this review, we have briefed the possible reason behind the non-antibacterial effects of antibiotics, owing to the endosymbiotic origin of host mitochondria. We further discuss the physiological and immunomodulatory effects of antibiotics. We then extend the review to discuss molecular mechanisms behind the plausible use of antibiotics as anticancer agents.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Antibiotic discovery in the artificial intelligence era

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

Decentralized systems for the treatment of antimicrobial compounds released from hospital aquatic wastes

In many developing countries, untreated hospital effluents are discharged and treated simultaneously with municipal wastewater. However, if the hospital effluents are not treated separately, they pose concerning health risks due to the possible transport of the antimicrobial genes and microbes in the environment. Such effluent is considered as a point source for a number of potentially infectious microorganisms, waste antimicrobial compounds and other contaminants that could promote antimicrobial resistance development. The removal of these contaminants prior to discharge reduces the exposure of antimicrobials to the environment and this should lower the risk of superbug development. At an effluent discharge site, suitable pre-treatment of wastewater containing antimicrobials could maximise the ecological impact with potentially reduced risk to human health. In addressing these points, this paper reviews the applications of decentralized treatment systems toward reducing the concentration of antimicrobials in wastewater. The most commonly used techniques in decentralized wastewater treatment systems for onsite removal of antimicrobials were discussed and evidence suggests that hybrid techniques should be more useful for the efficient removal of antimicrobials. It is concluded that alongside the cooperation of administration departments, health industries, water treatment authorities and general public, decentralized treatment technology can efficiently enhance the removal of antimicrobial compounds, thereby decreasing the concentration of contaminants released to the environment that could pose risks to human and ecological health due to development of antimicrobial resistance in microbes.

Classification of antimicrobial mechanism of action using dynamic bacterial morphology imaging

Antimicrobial resistance is a major threat to human health. Basic knowledge of antimicrobial mechanism of action (MoA) is imperative for patient care and for identification of novel antimicrobials. However, the process of antimicrobial MoA identification is relatively laborious. Here, we developed a simple, quantitative time-lapse fluorescence imaging method, Dynamic Bacterial Morphology Imaging (DBMI), to facilitate this process. It uses a membrane dye and a nucleoid dye to track the morphological changes of single Bacillus subtilis cells in response to antimicrobials for up to 60 min. DBMI of bacterial cells facilitated assignment of the MoAs of 14 distinct, known antimicrobial compounds to the five main classes. We conclude that DBMI is a simple method, which facilitates rapid classification of the MoA of antimicrobials in functionally distinct classes.

Genomic characteristics of Listeria monocytogenes isolated during mushroom (Agaricus bisporus) production and processing

Listeria monocytogenes is a foodborne pathogen ubiquitously found in nature and which has been isolated from food and food processing environments. This study aimed to characterize L. monocytogenes strains isolated from the production and processing environments of frozen sliced mushrooms (Agaricus bisporus). An analysis was executed along the mushroom processing chain including one mushroom grower and two mushroom processing factories. A total of 153 L. monocytogenes strains were isolated, which could be grouped in three PCR serogroups, namely, serogroup 1/2a-3a (39.2%), serogroup 1/2b-3b-7 (34.0%) and serogroup 4b-4d-4e (26.8%). A selection of 44 L. monocytogenes strains isolated from the processing environment after cleaning and disinfection (C&D) and from frozen sliced mushrooms was genotyped by whole genome sequencing (WGS), because these strains pose a potential risk for product contamination after C&D and for human consumption. Multilocus sequence typing (MLST) revealed 11 clonal complexes (CCs), with strains belonging to CC1, CC4, CC37 and CC87 being detected in both processing factories. Comparative WGS analysis of the 44 strains showed the presence of Listeria pathogenicity island 1 (LIPI-1) with a disrupted version of actA in all CC1, CC4, CC5, CC59 strains, and all but one CC224 strains. Notably, both inlA and inlB were detected as full-length loci in every strain, except for inlA in a CC6 strain that harbored a three amino acid deletion. LIPI-3 was detected in all CC1, CC4, CC6 and CC224 strains, while LIPI-4 was detected in all CC4 and CC87 strains. In addition, antibiotic susceptibility tests showed susceptibility towards fourteen antibiotics tested. The bcrABC operon was found in one CC5 strain, that showed a higher tolerance towards benzalkonium chloride than any other strain tested with confluent growth till 12.5 μg/ml for the CC5 strain compared to 2.5 μg/ml for the other strains. This study highlights that the ecology of L. monocytogenes in the frozen sliced mushroom production chain is highly diverse, and shows the importance of hygienic measures to control L. monocytogenes along the frozen sliced mushroom production chain.

Fast identification of off-target liabilities in early antibiotic discovery with Fourier-transform infrared spectroscopy

Structural modifications of known antibiotic scaffolds have kept the upper hand on resistance, but we are on the verge of not having antibiotics for many common infections. Mechanism-based discovery assays reveal novelty, exclude off-target liabilities, and guide lead optimization. For that, we developed a fast and automatable protocol using high-throughput Fourier-transform infrared spectroscopy (FTIRS). Metabolic fingerprints of Staphylococcus aureus and Escherichia coli exposed to 35 compounds, dissolved in dimethyl sulfoxide (DMSO) or water, were acquired. Our data analysis pipeline identified biomarkers of off-target effects, optimized spectral preprocessing, and identified the top-performing machine learning algorithms for off-target liabilities and mechanism of action (MOA) identification. Spectral bands with known biochemical associations more often yielded more significant biomarkers of off-target liabilities when bacteria were exposed to compounds dissolved in water than DMSO. Highly discriminative models distinguished compounds with predominant off-target effects from antibiotics with well-defined MOA (AUROC > 0.87, AUPR > 0.79, F1 > 0.81), and from the latter predicted their MOA (AUROC > 0.88, AUPR > 0.70, F1 > 0.70). The compound solvent did not affect predictive models. FTIRS is fast, simple, inexpensive, automatable, and highly effective at predicting MOA and off-target liabilities. As such, FTIRS mechanism-based screening assays can be applied for hit discovery and to guide lead optimization during the early stages of antibiotic discovery.

Technologies for High-Throughput Identification of Antibiotic Mechanism of Action

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. MOA identification is important because it is a great facilitator of lead optimization and improves the chances of commercialization. Moreover, the ability to rapidly detect MOA could enable a shift from an activity-based discovery paradigm towards a mechanism-based approach. This would allow to probe the grey chemical matter, an underexplored source of structural novelty. In this study we review techniques with throughput suitable to screen large libraries and sufficient sensitivity to distinguish MOA. In particular, the techniques used in chemical genetics (e.g., based on overexpression and knockout/knockdown collections), promoter-reporter libraries, transcriptomics (e.g., using microarrays and RNA sequencing), proteomics (e.g., either gel-based or gel-free techniques), metabolomics (e.g., resourcing to nuclear magnetic resonance or mass spectrometry techniques), bacterial cytological profiling, and vibrational spectroscopy (e.g., Fourier-transform infrared or Raman scattering spectroscopy) were discussed. Ultimately, new and reinvigorated phenotypic assays bring renewed hope in the discovery of a new generation of antibiotics.

Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells in vivo

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitro techniques, there is a growing demand for in vivo information based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanella cells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

Genome Characterization of Lactiplantibacillus plantarum Strain UTNGt2 Originated from Theobroma grandiflorum (White Cacao) of Ecuadorian Amazon: Antimicrobial Peptides from Safety to Potential Applications

The genome characterization of the Lactiplantibacillus plantarum strain UTNGt2, isolated from wild copoazu or white cacao (Theobroma grandiflorum), is described. A total of 31 contigs is assembled with a total length of 3,264,448 bases, with all contigs matching the core genome of different groups in the database. The genome size is 3,540,752 bases with GC content of 44.53% and the genome repeat sequences constitute around 457,386 bases of the assembly. The UTNGt2 matches the Lactiplantibacillus plantarum genome with 99% identity. The genome contains 3115 genes, 3052 protein-coding genes, assigned with the EggNOG database. On the basis of the results, 745 proteins are classified with an unknown function, from which 128 proteins have no match in the BLASTN database. It also contains 57 tRNAs, 5 copies of 5S rRNA, and 1 copy of tmRNA. Based on gene prediction and annotation results, 9.4% of proteins are involved in carbohydrate transport and metabolism and 8.46% in transcription, 2.36% are responsible for defense mechanisms, 0.5% are responsible for the biosynthesis of secondary metabolites, transport, and catabolism, while 25.11% have an unknown function. The genome revealed the presence of genes involved in riboflavin and folate production, the presence of CRISPR/Cas genes, phage sequences, the absence of acquired antibiotics resistance genes, virulence, and pathogenic factors, suggesting that UTNGt2 is a safe strain. Its highly antimicrobial capacity is related to the presence of two bacteriocin clusters (class IIc) of the sactipeptide class (contig 4) and plantaricin E class (contig 22), as detected by the BAGEL 4 webserver. Several RiPP-like peptides (non-bactericidal ribosomally produced and post-translationally modified peptides), polyketides (PKs), and terpenes were predicted. Whole-genome sequencing analysis revealed that the UTNGt2 strain has diverse bacteriocins with a high inhibitory capacity, thus it is a bacteriocinogenic strain. Considering the safety profile, UTNGt2 is a nonpathogenic, nonvirulent strain with valuable biotechnological traits and can be further exploited for its probiotic and antimicrobial potential in the food industry or as a potential producer strain of antimicrobial peptides as an alternative to conventional antibiotics.

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.

Peptide Extracts from Native Lactic Acid Bacteria Generate Ghost Cells and Spheroplasts upon Interaction with Salmonella enterica, as Promising Food Antimicrobials

Protecting foods from contamination applying peptides produced by lactic acid bacteria is a promising strategy to increase the food quality and safety. Interacting with the pathogen membranes might produce visible changes in shape or cell wall damage. Previously, we showed that the peptides produced by Lactobacillus plantarum UTNGt2, Lactobacillus plantarum UTNCys5-4, and Lactococcus lactis subsp. lactis UTNGt28 exhibit a broad spectrum of antibacterial activity against several foodborne pathogens in vitro. In this study, their possible mode of action against the commensal microorganism Salmonella enterica subsp. enterica ATCC51741 was investigated. The target membrane permeability was determined by detection of beta-galactosidase release from ONPG (o-nitro-phenyl-L-D-galactoside) substrate and changes in the whole protein profile indicating that the peptide extracts destroy the membrane integrity and may induce breaks in membrane proteins to some extent. The release of aromatic molecules such as DNA/RNA was detected after the interaction of Salmonella with the peptide extract. Transmission electronic microscopy (TEM) micrographs depicted at least four simultaneous secondary events after the peptide extract treatment underlying their antimicrobial actions, including morphological alterations of the membrane. Spheroplast and filament formation, vacuolation, and DNA relaxation were identified as the principal events from the Gt2 and Cys5-4 peptide extracts, while Gt28 induced the formation of ghost cells by release of cytoplasmic content, filaments, and separation of cell envelope layers. Gel retarding assays indicate that the Gt2 and Gt28 peptide extracts are clearly binding the Salmonella DNA, while Cys5-4 partially interacted with Salmonella genomic DNA. These results increased our knowledge about the inhibitory mechanism employed by several peptide extracts from native lactic acid bacteria against Salmonella. Further, we shall develop peptide-based formulation and evaluate their biocontrol effect in the food chains.

Optical microscopy reveals the dynamic nature of B. pseudomallei morphology during β-lactam antimicrobial susceptibility testing

Background

In Gram-negative species, β-lactam antibiotics target penicillin binding proteins (PBPs) resulting in morphological alterations of bacterial cells. Observations of antibiotic-induced cell morphology changes can rapidly and accurately differentiate drug susceptible from resistant bacterial strains; however, resistant cells do not always remain unchanged. Burkholderia pseudomallei is a Gram-negative, biothreat pathogen and the causative agent of melioidosis, an often fatal infectious disease for humans.

Results

Here, we identified β-lactam targets in B. pseudomallei by in silico analysis. Ten genes encoding putative PBPs, including PBP-1, PBP-2, PBP-3 and PBP-6, were detected in the genomes of susceptible and resistant strains. Real-time, live-cell imaging of B. pseudomallei strains demonstrated dynamic morphological changes in broth containing clinically relevant β-lactam antibiotics. At sub-inhibitory concentrations of ceftazidime (CAZ), amoxicillin-clavulanic acid (AMC), and imipenem (IPM), filamentation, varying in length and proportion, was an initial response of the multidrug-resistant strain Bp1651 in exponential phase. However, a dominant morphotype reemerged during stationary phase that resembled cells unexposed to antibiotics. Similar morphology dynamics were observed for AMC-resistant strains, MSHR1655 and 724644, when exposed to sub-inhibitory concentrations of AMC. For all B. pseudomallei strains evaluated, increased exposure time and exposure to increased concentrations of AMC at and above minimal inhibitory concentrations (MICs) in broth resulted in cell morphology shifts from filaments to spheroplasts and/or cell lysis. B. pseudomallei morphology changes were more consistent in IPM. Spheroplast formation followed by cell lysis was observed for all strains in broth containing IPM at concentrations greater than or equal to MICs, however, the time to cell lysis was variable. B. pseudomallei cell lengths were strain-, drug- and drug concentration-dependent.

Conclusions

Both resistant and susceptible B. pseudomallei strains exhibited filamentation during early exposure to AMC and CAZ at concentrations used to interpret susceptibility (based on CLSI guidelines). While developing a rapid β-lactam antimicrobial susceptibility test based on cell-shape alone requires more extensive analyses, optical microscopy detected B. pseudomallei growth attributes that lend insight into antibiotic response and antibacterial mechanisms of action.

Prediction of modes of action of components of traditional medicinal preparations

Traditional medicine preparations are used to treat many ailments in multiple regions across the world. Despite their widespread use, the mode of action of these preparations and their constituents are not fully understood. Traditional methods of elucidating the modes of action of these natural products (NPs) can be expensive and time consuming e. g. biochemical methods, bioactivity guided fractionation, etc. In this review, we discuss some methods for the prediction of the modes of action of traditional medicine preparations, both in mixtures and as isolated NPs. These methods are useful to predict targets of NPs before they are experimentally validated. Case studies of the applications of these methods are also provided herein.

Isolation and Characterization of Antimicrobial Peptides with Unusual Disulfide Connectivity from the Colonial Ascidian Synoicum turgens

This study reports the isolation of two novel cysteine-rich antibacterial peptides, turgencin A and turgencin B, along with their oxidized derivatives, from the Arctic marine colonial ascidian Synoicum turgens. The peptides are post-translationally modified, containing six cysteines with an unusual disulfide connectivity of Cys¹-Cys⁶, Cys²-Cys⁵, and Cys³-Cys⁴ and an amidated C-terminus. Furthermore, the peptides contain methionine residues resulting in the isolation of peptides with different degrees of oxidation. The most potent peptide, turgencin A_Mox1 with one oxidized methionine, displayed antimicrobial activity against both Gram-negative and Gram-positive bacteria with a minimum inhibitory concentration (MIC) as low as 0.4 µM against selected bacterial strains. In addition, the peptide inhibited the growth of the melanoma cancer cell line A2058 (IC₅₀ = 1.4 µM) and the human fibroblast cell line MRC-5 (IC₅₀ = 4.8 µM). The results from this study show that natural peptides isolated from marine tunicates have the potential to be promising drug leads.

Morphological Deconvolution of Beta-Lactam Polyspecificity in E. coli

Beta-lactams comprise one of the earliest classes of antibiotic therapies. These molecules covalently inhibit enzymes from the family of penicillin-binding proteins (PBPs), which are essential in construction of the bacterial cell wall. As a result, beta-lactams cause striking changes to cellular morphology, the nature of which varies by the range of PBPs simultaneously engaged in the cell. The traditional method of exploring beta-lactam polyspecificity is a gel-based binding assay which is low-throughput and typically is run  ex situ in cell extracts. Here, we describe a medium-throughput, image-based assay combined with machine learning methods to automatically profile the activity of beta-lactams in E. coli cells. By testing for morphological change across a panel of strains with perturbations to individual PBP enzymes, our approach automatically and quantifiably relates different beta-lactam antibiotics according to their preferences for individual PBPs in cells. We show the potential of our approach for guiding the design of novel inhibitors toward different PBP-binding profiles by predicting the mechanisms of two recently reported PBP inhibitors.

Breathing new life into old antibiotics: overcoming antibacterial resistance by antibiotic-loaded nanogel carriers with cationic surface functionality

Multidrug-resistant pathogens are prevalent in chronic wounds. There is an urgent need to develop novel antimicrobials and formulation strategies that can overcome antibiotic resistance and provide a safe alternative to traditional antibiotics. This work aimed to develop a novel nanocarrier for two cationic antibiotics, tetracycline hydrochloride and lincomycin hydrochloride which can potentially overcome antibiotic resistance. In this study, we report the use of surface functionalised polyacrylic copolymer nanogels as carriers for cationic antibiotics. These nanogels can encapsulate small cationic antimicrobial molecules and act as a drug delivery system. They were further functionalised with a biocompatible cationic polyelectrolyte, bPEI, to increase their affinity towards the negatively charged bacterial cell walls. These bPEI-coated nanocarrier-encapsulated antibiotics were assessed against a range of wound isolated pathogens, which had been shown through antimicrobial susceptibility testing (AST) to be resistant to tetracycline and lincomycin. Our data reveal that bPEI-coated nanogels with encapsulated tetracycline or lincomycin displayed increased antimicrobial performance against selected wound-derived bacteria, including strains highly resistant to the free antibiotic in solution. Additionally, our nanocarrier-based antibiotics showed no detectable cytotoxic effect against human keratinocytes. We attribute the increase in the antimicrobial activity of the cationically functionalised antibiotic-loaded nanogel carriers to specific electrostatic adhesion to the microbial cell wall delivering a higher local antibiotic concentration, confirmed by scanning electron microscopy. Such a nanotechnology based approach may enhance the effectiveness of a wide variety of existing antibiotics, offering a potentially new mechanism to overcome antibiotic resistance.

Role of Natural Product in Modulation of Drug Transporters and New Delhi Metallo-β Lactamases

A rapid growth in drug resistance has brought options for treating antimicrobial resistance to a halt. Bacteria have evolved to accumulate a multitude of genes that encode resistance for a single drug within a single cell. Alternations of drug transporters are one of the causes for the development of resistance in drug interactions. Conversely, the production of enzymes also inactivates most antibiotics. The discovery of newer classes of antibiotics and drugs from natural products is urgently needed. Alternative medicines play an integral role in countries across the globe but many require validation for treatment strategies. It is essential to explore this chemical diversity in order to find novel drugs with specific activities which can be used as alternative drug targets. This review describes the interaction of drugs with resistant pathogens with a special focus on natural product-derived efflux pump and carbapenemase inhibitors.

Minocycline and Silver Dual-Loaded Polyphosphoester-Based Nanoparticles for Treatment of Resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa has been detected in the lungs of ∼50% of patients with cystic fibrosis (CF), including 20% of adult CF patients. The majority of these adult patients harbor multi-drug resistant (MDR) strains, limiting the available treatment options. Silver has long been used as a broad-spectrum antimicrobial agent with a low incidence of resistance. Despite low toxicity, poor availability of silver cations mandates a high dosage to effectively eradicate infections. To address this shortcoming of silver, nanoparticles have been used as delivery devices to improve treatment outcomes. Furthermore, studies have demonstrated that synergistic combinations with careful dose calibrations and efficient delivery systems result in superior antimicrobial activity while avoiding potential side effects of both therapeutics. Here 4-epi-minocycline, a metabolite of minocycline, was identified as an active antimicrobial against P. aeruginosa using a high-throughput screen. The antimicrobial activities of 4-epi-minocycline, minocycline, and silver acetate against clinical isolates of P. aeruginosa obtained from CF patients were evaluated in vitro. Next, the synergistic activity of the silver/minocycline combination against P. aeruginosa isolates was investigated using checkerboard assays and identified with end-point colony forming unit determination assays. Finally, nanoparticles coloaded with minocycline and silver were evaluated in vitro for antimicrobial activity. The results demonstrated that both silver and minocycline are potent antimicrobials alone and that the combination allows a reduced dosage of both therapeutics to achieve the same antimicrobial effect. Furthermore, the proposed synergistic silver/minocycline combination can be coloaded into nanoparticles as a next-generation antibiotic to combat the threats presented by MDR pathogens.

Colistin causes profound morphological alteration but minimal cytoplasmic membrane perforation in populations of Escherichia coli and Pseudomonas aeruginosa

Whilst colistin (polymyxin E) represents the last mainstream treatment option for multidrug-resistant Gram-negative pathogens, details of its mechanism of action remain to be fully resolved. In this study, the effects of sub-inhibitory, inhibitory-bactericidal, and supra-bactericidal levels of colistin on the membrane integrity and morphology of Escherichia coli and Pseudomonas aeruginosa were investigated using potassium loss, flow cytometry, and scanning electron microscopy (SEM). Supra-bactericidal colistin concentrations induced just 4-12% intracellular potassium loss from bacteria after 24 h. Flow cytometry data suggested colistin might alter cell arrangement, and SEM confirmed the antibiotic causes bacterial aggregation. Filamentation was not detected in either species at any concentration or time-point up to 24 h. These results argue against the hypotheses that colistin kills bacteria by puncturing the cytoplasmic membrane or disrupting DNA synthesis. The colistin-induced bacterial aggregation detected has implications for the interpretation of MBC, time-kill, and other test results obtained with this antibiotic.

Susceptibility of ruminal bacteria isolated from large and small ruminant to multiple conventional antibiotics

The pivotal aim of the present context was to isolate diversified group of bacteria from the ruminants and to evaluate their antibiogram pattern against 22 antibiotics of 14 different classes. The bacterial isolates from small and large ruminant (sheep, cattle and calves) were isolated from the rumen based on various colonies morphology, and subjected for preliminary antibiotics susceptibility assay using disc diffusion method. The most sensitive isolates (based on zone of inhibition) were selected for determining the minimum inhibitory concentration (MIC) of each antibiotic ranging from 1 to 256 μg/mL. Results revealed the concentration dependent growth inhibitory property of antibiotics a species-specific process. The maximum tolerable concentration (MTC) of each antibiotic was further determined using disc diffusion method, and results exhibited that the tolerance nature of ruminal isolates to antibiotics is a species-specific mechanism. Based on the MIC and MTC values of antibiotics, amikacin, ciprofloxacin, and amoxicilline were observed to be the most potent antibiotics in terms of inhibiting the growth of ruminal isolates. In brief, the findings of the current study showed that despite the overexploitation of antibiotics as additives in the animal's feed, most of the ruminal isolates are sensitive to multiple conventional antibiotics tested. The growth inhibitory trait of antibiotics proves these antimicrobials a propitious agent against the pathogenesis of ruminal isolates in livestock.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

Morphological and ultrastructural changes in bacterial cells as an indicator of antibacterial mechanism of action

Efforts to reduce the global burden of bacterial disease and contend with escalating bacterial resistance are spurring innovation in antibacterial drug and biocide development and related technologies such as photodynamic therapy and photochemical disinfection. Elucidation of the mechanism of action of these new agents and processes can greatly facilitate their development, but it is a complex endeavour. One strategy that has been popular for many years, and which is garnering increasing interest due to recent technological advances in microscopy and a deeper understanding of the molecular events involved, is the examination of treated bacteria for changes to their morphology and ultrastructure. In this review, we take a critical look at this approach. Variables affecting antibacterial-induced alterations are discussed first. These include characteristics of the test organism (e.g. cell wall structure) and incubation conditions (e.g. growth medium osmolarity). The main body of the review then describes the different alterations that can occur. Micrographs depicting these alterations are presented, together with information on agents that induce the change, and the sequence of molecular events that lead to the change. We close by highlighting those morphological and ultrastructural changes which are consistently induced by agents sharing the same mechanism (e.g. spheroplast formation by peptidoglycan synthesis inhibitors) and explaining how changes that are induced by multiple antibacterial classes (e.g. filamentation by DNA synthesis inhibitors, FtsZ disruptors, and other types of agent) can still yield useful mechanistic information. Lastly, recommendations are made regarding future study design and execution.

Contribution of high-content imaging technologies to the development of anti-infective drugs

Originally developed to study fundamental aspects of cellular biology, high‐content imaging (HCI) was rapidly adapted to study host–pathogen interactions at the cellular level and adopted as a technology of choice to unravel disease biology. HCI platforms allow for the visualization and quantification of discrete phenotypes that cannot be captured using classical screening approaches. A key advantage of high‐content screening technologies lies in the possibility to develop and interrogate physiologically significant, predictive ex vivo disease models that reproduce complex conditions relevant for infection. Here we review and discuss recent advances in HCI technologies and chemical biology approaches that are contributing to an increased understanding of the intricate host–pathogen interrelationship on the cellular level, and which will foster the development of novel therapeutic approaches for the treatment of human bacterial and protozoan infections. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC

Discovery of neuroprotective compounds by machine learning approaches

The classification models were constructed to discover neuroprotective compounds against glutamate or H₂O₂-induced neurotoxicity through machine learning approaches.

Small angle X-ray scattering as a high-throughput method to classify antimicrobial modes of action

Multi-drug resistant bacteria are currently undermining our health care system worldwide. While novel antimicrobial drugs, such as antimicrobial peptides, are urgently needed, identification of new modes of action is money and time consuming, and in addition current approaches are not available in a high throughput manner. Here we explore how small angle X-ray scattering (SAXS) as high throughput method can contribute to classify the mode of action for novel antimicrobials and therefore supports fast decision making in drug development. Using data bases for natural occurring antimicrobial peptides or predicting novel artificial peptides, many candidates can be discovered that will kill a selected target bacterium. However, in order to narrow down the selection it is important to know if these peptides follow all the same mode of action. In addition, the mode of action should be different from conventional antibiotics, in consequence peptide candidates can be developed further into drugs against multi-drug resistant bacteria. Here we used one short antimicrobial peptide with unknown mode of action and compared the ultrastructural changes of Escherichia coli cells after treatment with the peptide to cells treated with classic antibiotics. The key finding is that SAXS as a structure sensitive tool provides a rapid feedback on drug induced ultrastructural alterations in whole E. coli cells. We could demonstrate that ultrastructural changes depend on the used antibiotics and their specific mode of action. This is demonstrated using several well characterized antimicrobial compounds and the analysis of resulting SAXS curves by principal component analysis. To understand the result of the PCA analysis, the data is correlated with TEM images. In contrast to real space imaging techniques, SAXS allows to obtain nanoscale information averaged over approximately one million cells. The measurement takes only seconds, while conventional tests to identify a mode of action require days or weeks per single substance. The antimicrobial peptide showed a different mode of action as all tested antibiotics including polymyxin B and is therefore a good candidate for further drug development. We envision SAXS to become a useful tool within the high-throughput screening pipeline of modern drug discovery. This article is part of a Special Issue entitled: Antimicrobial peptides edited by Karl Lohner and Kai Hilpert.

Assay Development for Image-Based Quantification of Intracellular Bacterial Replication and Analysis of the Innate Immune Response to Infection

Severe bacterial infection can lead to inflammation, host tissue damage, and ultimately disseminated septic shock. The mammalian innate immune system responds to microbial infection through the detection of invariant pathogen-associated molecular patterns (PAMPs) by a range of pattern recognition receptors (PRRs) expressed by the host cell. A successful immune response involves tightly coordinated signaling from these receptors, leading to a robust transcriptional response producing cytokines and antimicrobial effectors. While the PRR-expressing phagocytes of the host innate immune system function to contain and degrade internalized bacteria through pathways such as selective autophagy, pathogenic bacteria may subvert this process to replicate in the host cell. We describe the development of imaging assays to investigate these host-pathogen interactions through gene perturbation screens, which could lead to the identification of novel effectors of the host response to bacterial infection. We identify markers of coordinated initial signaling in macrophages challenged with ligands to PRRs of the toll-like receptor (TLR) family and compare this response to that induced by intact bacteria of the Burkholderia cenocepacia complex (Bcc), an opportunistic pathogen that causes life-threatening infections in patients with cystic fibrosis and chronic granulomatous disease. Bcc has been shown to escape the endocytic pathway, activate selective autophagy, and replicate within human macrophages. We demonstrate robust image-based quantification of multiple stages of Bcc infection of macrophages: ubiquitin tagging of cytosolic bacteria, recruitment of selective autophagy effector proteins, and intracellular bacterial replication, and we show perturbation of bacterial replication using drug treatment or siRNA-based gene knockdown. The described panel of imaging assays can be extended to other bacterial infections and pathogenic ligand combinations where high-content siRNA screening could provide significant new insight into regulation of the innate immune response to infection.

Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing "grind and find" model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.

Connecting phenotype and chemotype: high-content discovery strategies for natural products research

In recent years, the field of natural products has seen an explosion in the breadth, resolution, and accuracy of profiling platforms for compound discovery, including many new chemical and biological annotation methods. With these new tools come opportunities to examine extract libraries using systematized profiling approaches that were not previously available to the field and which offer new approaches for the detailed characterization of the chemical and biological attributes of complex natural products mixtures. This review will present a summary of some of these untargeted profiling methods and provide perspective on the future opportunities offered by integrating these tools for novel natural products discovery.

Functional chromatographic technique for natural product isolation

Natural product discovery arises through a unique interplay between chromatographic purification and biological assays. Currently, most techniques used for natural product purification deliver leads without a defined biological action. We now describe a technique, referred to herein as functional chromatography, that deploys biological affinity as the matrix for compound isolation.

Antibacterial compounds of Canadian honeys target bacterial cell wall inducing phenotype changes, growth inhibition and cell lysis that resemble action of β-lactam antibiotics

Honeys show a desirable broad spectrum activity against Gram-positive and negative bacteria making antibacterial activity an intrinsic property of honey and a desirable source for new drug development. The cellular targets and underlying mechanism of action of honey antibacterial compounds remain largely unknown. To facilitate the target discovery, we employed a method of phenotypic profiling by directly comparing morphological changes in Escherichia coli induced by honeys to that of ampicillin, the cell wall-active β-lactam of known mechanism of action. Firstly, we demonstrated the purity of tested honeys from potential β-lactam contaminations using quantitative LC-ESI-MS. Exposure of log-phase E. coli to honey or ampicillin resulted in time- and concentration-dependent changes in bacterial cell shape with the appearance of filamentous phenotypes at sub-inhibitory concentrations and spheroplasts at the MBC. Cell wall destruction by both agents, clearly visible on microscopic micrographs, was accompanied by increased permeability of the lipopolysaccharide outer membrane as indicated by fluorescence-activated cell sorting (FACS). More than 90% E. coli exposed to honey or ampicillin became permeable to propidium iodide. Consistently with the FACS results, both honey-treated and ampicillin-treated E. coli cells released lipopolysaccharide endotoxins at comparable levels, which were significantly higher than controls (p<0.0001). E. coli cells transformed with the ampicillin-resistance gene (β-lactamase) remained sensitive to honey, displayed the same level of cytotoxicity, cell shape changes and endotoxin release as ampicillin-sensitive cells. As expected, β-lactamase protected the host cell from antibacterial action of ampicillin. Thus, both honey and ampicillin induced similar structural changes to the cell wall and LPS and that this ability underlies antibacterial activities of both agents. Since the cell wall is critical for cell growth and survival, honey active compounds would be highly applicable for therapeutic purposes while differences in the mode of action between honey and ampicillin may provide clinical advantage in eradicating β-lactam-resistant pathogens.

Tools for characterizing bacterial protein synthesis inhibitors

Many antibiotics inhibit the growth of sensitive bacteria by interfering with ribosome function. However, discovery of new protein synthesis inhibitors is curbed by the lack of facile techniques capable of readily identifying antibiotic target sites and modes of action. Furthermore, the frequent rediscovery of known antibiotic scaffolds, especially in natural product extracts, is time-consuming and expensive and diverts resources that could be used toward the isolation of novel lead molecules. In order to avoid these pitfalls and improve the process of dereplication of chemically complex extracts, we designed a two-pronged approach for the characterization of inhibitors of protein synthesis (ChIPS) that is suitable for the rapid identification of the site and mode of action on the bacterial ribosome. First, we engineered antibiotic-hypersensitive Escherichia coli strains that contain only one rRNA operon. These strains are used for the rapid isolation of resistance mutants in which rRNA mutations identify the site of the antibiotic action. Second, we show that patterns of drug-induced ribosome stalling on mRNA, monitored by primer extension, can be used to elucidate the mode of antibiotic action. These analyses can be performed within a few days and provide a rapid and efficient approach for identifying the site and mode of action of translation inhibitors targeting the bacterial ribosome. Both techniques were validated using a bacterial strain whose culture extract, composed of unknown metabolites, exhibited protein synthesis inhibitory activity; we were able to rapidly detect the presence of the antibiotic chloramphenicol.