ANTIMICROBIAL WOUND DRESSINGS FOR FULL-THICKNESS INFECTED BURN WOUNDS.

ABSTRACT: Infection of wounds delays healing, increases treatment costs, and leads to major complications. Current methods to manage such infections include antibiotic ointments and antimicrobial wound dressings, both of which have significant drawbacks, including frequent reapplication and contribution to antimicrobial resistance. In this work, we developed wound dressings fabricated with a medical-grade polyurethane coating composed of natural plant secondary metabolites, cinnamaldehyde, and alpha-terpineol. Our wound dressings are easy to change and do not adhere to the wound bed. They kill gram-positive and -negative microbes in infected wounds due to the Food and Drug Administration-approved for human consumption components. The wound dressings were fabricated by dip coating. Antimicrobial efficacy was determined by quantifying the bacteria colonies after a 24 h of immersion. Wound healing and bacterial reduction were assessed in an in vivo full-thickness porcine burn model. Our antimicrobial wound dressings showed a > 5-log reduction (99.999%) of different gram-positive and gram-negative bacteria, while maintaining absorbency. In the in vivo porcine burn model, our wound dressings were superior to bacitracin in decreasing bacterial burden during daily changes, without interfering with wound healing. Additionally, the dressings had a significantly lower adhesion to the wound bed. Our antimicrobial wound dressings reduced the burden of clinically relevant bacteria more than commercial antimicrobial wound dressings. In an in vivo infected burn wound model, our coatings performed as well or better than bacitracin. We anticipate that our wound dressings would be useful for the treatment of various types of acute and chronic wounds.

Continuous sepsis trajectory prediction using tensor-reduced physiological signals.

The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p = 0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p < 0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p = 0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p < 0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.

Rapid diagnosis of bloodstream infections using a culture-free phenotypic platform.

BACKGROUND: Bloodstream infections (BSIs) are a life-threatening acute medical condition and current diagnostics for BSIs suffer from long turnaround time (TAT). Here we show the validation of a rapid detection-analysis platform (RDAP) for the diagnosis of BSIs performed on clinical blood samples METHODS: The validation was performed on a cohort of 59 clinical blood samples, including positive culture samples, which indicated confirmed bloodstream infections, and negative culture samples. The bacteria in the positive culture samples included Gram-positive and Gram-negative pathogenic species. RDAP is based on an electrochemical sandwich immunoassay with voltage-controlled signal amplification, which provides an ultra-low limit of detection (4 CFU/mL), allowing the platform to detect and identify bacteria without requiring culture and perform phenotypic antibiotic susceptibility testing (AST) with only 1-2 h of antibiotic exposure. The preliminary diagnostic performance of RDAP was compared with that of standard commercial diagnostic technologies. RESULTS: Using a typical clinical microbiology laboratory diagnostic workflow that involved sample culture, agar plating, bacteria identification using matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry, and AST using MicroScan as a clinical diagnostic reference, RDAP showed diagnostic accuracy of 93.3% and 95.4% for detection-identification and AST, respectively. However, RDAP provided results at least 15 h faster. CONCLUSIONS: This study shows the preliminary feasibility of using RDAP to rapidly diagnose BSIs, including AST. Limitations and potential mitigation strategies for clinical translation of the present RDAP prototype are discussed. The results of this clinical feasibility study indicate an approach to provide near real-time diagnostic information for clinicians to significantly enhance the treatment outcome of BSIs.

Effective treatment of bloodstream infections (BSIs), a life-threatening acute medical condition, requires rapid diagnosis. Current diagnostic methods involve culturing the bacteria from the patient's blood, which requires typically 16­48 h to produce a diagnosis. Here, we demonstrate the feasibility of using a culture-free platform to perform rapid diagnosis of BSIs. We tested the diagnostic platform on a cohort of clinical blood samples. The bacteria contained in the samples covered a representative range of bacteria that cause BSIs. The culture-free platform produced diagnosis in about 15 hours faster than standard commercial diagnostic technologies and  the diagnostic results were in good agreement with that of standard technologies. The results of this study indicate an approach to providing near real-time diagnostic information for clinicians to significantly enhance the treatment outcome of BSIs.

Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals.

The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient's quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.

Self-Organization of Iron Sulfide Nanoparticles into Complex Multicompartment Supraparticles.

Self-assembled compartments from nanoscale components are found in all life forms. Their characteristic dimensions are in 50-1000 nm scale, typically assembled from a variety of bioorganic "building blocks". Among the various functions that these mesoscale compartments carry out, protection of the content from the environment is central. Finding synthetic pathways to similarly complex and functional particles from technologically friendly inorganic nanoparticles (NPs) is needed for a multitude of biomedical, biochemical, and biotechnological processes. Here, it is shown that FeS2 NPs stabilized by l-cysteine self-assemble into multicompartment supraparticles (mSPs). The NPs initially produce ≈55 nm concave assemblies that reconfigure into ≈75 nm closed mSPs with ≈340 interconnected compartments with an average size of ≈5 nm. The intercompartmental partitions and mSP surface are formed primarily from FeS2 and Fe2 O3 NPs, respectively. The intermediate formation of cup-like particles enables encapsulation of biological cargo. This capability is demonstrated by loading mSPs with DNA and subsequent transfection of mammalian cells. Also it is found that the temperature stability of the DNA cargo is enhanced compared to the traditional delivery vehicles. These findings demonstrate that biomimetic compartmentalized particles can be used to successfully encapsulate and enhance temperature stability of the nucleic acid cargo for a variety of bioapplications.

Chiral chromatography and surface chirality of carbon nanoparticles.

Chiral carbon nanoparticles (CNPs) represent a rapidly evolving area of research for optical and biomedical technologies. Similar to small molecules, applications of CNPs as well as fundamental relationships between their optical activity and structural asymmetry would greatly benefit from their enantioselective separations by chromatography. However, this technique remains in its infancy for chiral carbon and other nanoparticles. The possibility of effective separations using high performance liquid chromatography (HPLC) with chiral stationary phases remains an open question whose answer can also shed light on the components of multiscale chirality of the nanoparticles. Herein, we report a detailed methodology of HPLC for successful separation of chiral CNPs and establish a path for its future optimization. A mobile phase of water/acetonitrile was able to achieve chiral separation of CNPs derived from L- and D-cysteine denoted as L-CNPs and D-CNPs. Molecular dynamics simulations show that the teicoplanin-based stationary phase has a higher affinity for L-CNPs than for D-CNPs, in agreement with experiments. The experimental and computational findings jointly indicate that chiral centers of chiral CNPs are present at their surface, which is essential for the multiple applications of these chiral nanostructures and equally essential for interactions with biomolecules and circularly polarized photons.

Struct2Graph: a graph attention network for structure based predictions of protein-protein interactions.

BACKGROUND: Development of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. RESULTS: In this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a fivefold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein-protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein-protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy. CONCLUSIONS: In this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph ). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

Surfaces with instant and persistent antimicrobial efficacy against bacteria and SARS-CoV-2.

Surfaces contaminated with bacteria and viruses contribute to the transmission of infectious diseases and pose a significant threat to global public health. Modern day disinfection either relies on fast-acting (>3-log reduction within a few minutes), yet impermanent, liquid-, vapor-, or radiation-based disinfection techniques, or long-lasting, but slower-acting, passive antimicrobial surfaces based on heavy metal surfaces, or metallic nanoparticles. There is currently no surface that provides instant and persistent antimicrobial efficacy against a broad spectrum of bacteria and viruses. In this work, we describe a class of extremely durable antimicrobial surfaces incorporating different plant secondary metabolites that are capable of rapid disinfection (>4-log reduction) of current and emerging pathogens within minutes, while maintaining persistent efficacy over several months and under significant environmental duress. We also show that these surfaces can be readily applied onto a variety of desired substrates or devices via simple application techniques such as spray, flow, or brush coating.

Unifying structural descriptors for biological and bioinspired nanoscale complexes.

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein-protein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on protein-protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.

Dosage-dependent antimicrobial activity of DNA-histone microwebs against Staphylococcus aureus.

Neutrophil extracellular traps (NETs) is an antimicrobial cobweb-structured material produced by immune cells for clearance of pathogens in the body, but paradoxically associated with biofilm formation and exacerbated lung infections. To provide a better materials perspective on the pleiotropic roles played by NETs at diverse compositions/concentrations, a NETs-like material (called 'microwebs', abbreviated as µwebs) is synthesized for decoding the antimicrobial activity of NETs against Staphylococcus aureus in infection-relevant conditions. We show that µwebs composed of low-to-intermediate concentrations of DNA-histone complexes successfully trap and inhibit S. aureus growth and biofilm formation. However, with growing concentrations and histone proportions, the resulting microwebs appear gel-like structures accompanied by reduced antimicrobial activity that can even promote formation of S. aureus biofilms. Our simplified model of NETs provides a materials-based evidence on NETs-relevant pathology in the development of biofilms.

Staphylococcus epidermidis Has Growth Phase Dependent Affinity for Fibrinogen and Resulting Fibrin Clot Elasticity.

Bacterial infection and thrombosis are highly correlated, especially in patients with indwelling medical devices. Coagulase-negative staphylococci, typified by Staphylococcus epidermidis, are a common cause of medical device infections owing to their biofilm forming capacity which provides protection from antibiotics and host immune response. Attention has been drawn to the interaction between S. epidermidis and host proteins, specifically fibrinogen. However, little is known regarding the impact of the transition from planktonic to biofilm forming phenotype on this interaction. Here we investigate the growth phase dependence of bacteria-fibrinogen interaction and the resulting effect on fibrin clot formation, structure, and mechanics. Flow cytometry demonstrated growth phase dependent affinity for fibrinogen. To mimic intravascular device seeding, we quantified the adhesion of S. epidermidis to a fibrinogen coated surface under continuous flow conditions in vitro. The bacterial deposition rate onto fibrinogen was significantly greater for stationary (5,360 ± 1,776 cells/cm2s) versus exponential phase (2,212 ± 264, cells/cm2 s). Furthermore, the expression of sdrG-a cell surface adhesion protein with specificity for fibrinogen-was upregulated ∼twofold in the stationary versus the exponential phase. Rheometry and confocal microscopy demonstrated that stationary phase S. epidermidis slows clot formation and generates a more heterogeneous fibrin network structure with greater elasticity (G' = 5.7 ± 1.0 Pa) compared to sterile fibrinogen (G' = l.5 ± 0.2 Pa), while exponential phase cells had little effect. This work contributes to the current understanding of the growth phase dependent regulation of bacterial virulence factors and the correlation between bacterial infection and thrombosis.

A novel swine model of the acute respiratory distress syndrome using clinically relevant injury exposures.

To date, existing animal models of the acute respiratory distress syndrome (ARDS) have failed to translate preclinical discoveries into effective pharmacotherapy or diagnostic biomarkers. To address this translational gap, we developed a high-fidelity swine model of ARDS utilizing clinically relevant lung injury exposures. Fourteen male swine were anesthetized, mechanically ventilated, and surgically instrumented for hemodynamic monitoring, blood, and tissue sampling. Animals were allocated to one of three groups: (1) Indirect lung injury only: animals were inoculated by direct injection of Escherichia coli into the kidney parenchyma, provoking systemic inflammation and distributive shock physiology; (2) Direct lung injury only: animals received volutrauma, hyperoxia, and bronchoscope-delivered gastric particles; (3) Combined indirect and direct lung injury: animals were administered both above-described indirect and direct lung injury exposures. Animals were monitored for up to 12 h, with serial collection of physiologic data, blood samples, and radiographic imaging. Lung tissue was acquired postmortem for pathological examination. In contrast to indirect lung injury only and direct lung injury only groups, animals in the combined indirect and direct lung injury group exhibited all of the physiological, radiographic, and histopathologic hallmarks of human ARDS: impaired gas exchange (mean PaO2 /FiO2 ratio 124.8 ± 63.8), diffuse bilateral opacities on chest radiographs, and extensive pathologic evidence of diffuse alveolar damage. Our novel porcine model of ARDS, built on clinically relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS and fills a crucial gap in the translational study of human lung injury.

Improved diagnostic prediction of the pathogenicity of bloodstream isolates of Staphylococcus epidermidis.

With an estimated 440,000 active cases occurring each year, medical device associated infections pose a significant burden on the US healthcare system, costing about $9.8 billion in 2013. Staphylococcus epidermidis is the most common cause of these device-associated infections, which typically involve isolates that are multi-drug resistant and possess multiple virulence factors. S. epidermidis is also frequently a benign contaminant of otherwise sterile blood cultures. Therefore, tests that distinguish pathogenic from non-pathogenic isolates would improve the accuracy of diagnosis and prevent overuse/misuse of antibiotics. Attempts to use multi-locus sequence typing (MLST) with machine learning for this purpose had poor accuracy (~73%). In this study we sought to improve the diagnostic accuracy of predicting pathogenicity by focusing on phenotypic markers (i.e., antibiotic resistance, growth fitness in human plasma, and biofilm forming capacity) and the presence of specific virulence genes (i.e., mecA, ses1, and sdrF). Commensal isolates from healthy individuals (n = 23), blood culture contaminants (n = 21), and pathogenic isolates considered true bacteremia (n = 54) were used. Multiple machine learning approaches were applied to characterize strains as pathogenic vs non-pathogenic. The combination of phenotypic markers and virulence genes improved the diagnostic accuracy to 82.4% (sensitivity: 84.9% and specificity: 80.9%). Oxacillin resistance was the most important variable followed by growth rate in plasma. This work shows promise for the addition of phenotypic testing in clinical diagnostic applications.

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine.

BACKGROUND: The systemic responses to infection and its progression to sepsis remains poorly understood. Progress in the field has been stifled by the shortcomings of experimental models which include poor replication of the human condition. To address these challenges, we developed and piloted a novel large animal model of severe infection that is capable of generating multi-system clinically relevant data. METHODS: Male swine (n = 5) were anesthetized, mechanically ventilated, and surgically instrumented for continuous hemodynamic monitoring and serial blood sampling. Animals were inoculated with uropathogenic E. coli by direct injection into the renal parenchyma and were maintained until a priori endpoints were met. The natural history of the infection was studied. Animals were not resuscitated. Multi-system data were collected hourly to 6 hours; all animals were euthanized at predetermined physiologic endpoints. RESULTS: Core body temperature progressively increased from mean (SD) 37.9(0.8)°C at baseline to 43.0(1.2)°C at experiment termination (p = 0.006). Mean arterial pressure did not begin to decline until 6h post inoculation, dropping from 86(9) mmHg at baseline to 28(5) mmHg (p = 0.005) at termination. Blood glucose progressively declined but lactate levels did not elevate until the last hours of the experiment. There were also temporal changes in whole blood concentrations of a number of metabolites including increases in the catecholamine precursors, tyrosine (p = 0.005) and phenylalanine (p = 0.005). Lung, liver, and kidney function parameters worsened as infection progressed and at study termination there was histopathological evidence of injury in these end-organs. CONCLUSION: We demonstrate a versatile, multi-system, longitudinal, swine model of infection that could be used to further our understanding of the mechanisms that underlie infection-induced multi-organ dysfunction and failure, optimize resuscitation protocols and test therapeutic interventions. Such a model could improve translation of findings from the bench to the bedside, circumventing a significant obstacle in sepsis research.

Lysis and direct detection of coliforms on printed paper-based microfluidic devices.

Coliforms are one of the most common families of bacteria responsible for water contamination. Certain coliform strains can be extremely toxic, and even fatal if consumed. Current technologies for coliform detection are expensive, require multiple complicated steps, and can take up to 24 hours to produce accurate results. Recently, open-channel, paper-based microfluidic devices have become popular for rapid, inexpensive, and accurate bioassays. In this work, we have created an integrated microfluidic coliform lysis and detection device by fabricating customizable omniphilic regions via direct printing of omniphilic channels on an omniphobic, fluorinated paper. This paper-based device is the first of its kind to demonstrate successful cell lysing on-chip, as it can allow for the flow and control of both high and low surface tension liquids, including different cell lysing agents. The fabricated microfluidic device was able to successfully detect E. coli, via the presence of the coliform-specific enzyme, ß-galactosidase, at a concentration as low as ∼104 CFU mL-1. Further, E. coli at an initial concentration of 1 CFU mL-1 could be detected after only 6 hours of incubation. We believe that these devices can be readily utilized for real world E. coli contamination detection in multiple applications, including food and water safety.

Differential Effects of Heated Perfusate on Morphology, Viability, and Dissemination of Staphylococcus epidermidis Biofilms.

The biofilm phenotype offers bacterial communities protection from environmental factors, as evidenced by its role in the viability, persistence, and virulence of cells under conditions in which flow is present, such as in riverbeds, industrial piping networks, and the human circulatory system. Here, we examined the hypothesis that temperature-an environmental factor that affects the growth of the Gram-positive bacterium Staphylococcus epidermidis-controls, through dual mechanisms, persistence of this bacterial strain in a shear environment characteristic of the human circulatory system. We demonstrated that temperature and antibiotics impact the surface-adhered biofilm and material disseminated downstream in different ways. Specifically, by means of three-dimensional (3D) confocal and scanning electron microscopy, an increase in surface-adhered biofilm heterogeneity was observed with increasing temperature. Additionally, we found a 4-log decrease in cell viability at the biofilm surface as the perfusate temperature was increased from 37°C to 50°C. Finally, the viability of cell-containing fragments that were disseminated from the substrate was assessed by downstream sampling, culture, and optical density measurement. We found that although temperature decreased the viability of the surface-adhered biofilm, the downstream material remained viable. And yet, in the presence of antibiotics, the growth of disseminated material was nearly completely inhibited, even though the addition of antibiotics had no significant impact on the viability of the surface-adhered biofilm. The mechanism involves both biofilm structural damage, as quantified by morphology of debrided material, and reduced cell viability, as quantified by assay of bacterial cells present in the surface-adherent biofilm and in the downstream effluent. The results potentially identify parameter ranges in which elevated temperature could augment current antibiotic treatment regimens.IMPORTANCE Bacterial biofilms are a leading cause of medical device infections. Staphylococcus epidermidis is commonly responsible for these types of infections. With increasing occurrences of antibacterial resistance, there has been a new push to explore treatment options that augment traditional antibiotic therapies. Here, we show how thermal treatment can be applied to both degrade bacterial biofilms on substrates and impede the proliferation of cells that detach from them. Understanding the response of both surface-adhered and dispersed bacterial cells under thermal stress conditions is a foundational step toward the development of an in situ treatment/remediation method for biofilm growth in medical devices; such an application could use oscillatory flow of heated fluid in a catheter as an adjuvant to antibiotic treatment. The work furthermore provides new insight into the viability of disseminated biofilm material.

Plasmonic nanoparticles assemblies templated by helical bacteria and resulting optical activity.

Plasmonic nanoparticles (NPs) adsorbing onto helical bacteria can lead to formation of NP helicoids with micron scale pitch. Associated chiroptical effects can be utilized as bioanalytical tool for bacterial detection and better understanding of the spectral behavior of helical self-assembled structures with different scales. Here, we report that enantiomerically pure helices with micron scale of chirality can be assembled on Campylobacter jejuni, a helical bacterium known for severe stomach infections. These organisms have right-handed helical shapes with a pitch of 1-2 microns and can serve as versatile templates for a variety of NPs. The bacteria itself shows no observable rotatory activity in the visible, red, and near-IR ranges of electromagnetic spectrum. The bacterial dispersion acquires chiroptical activity at 500-750 nm upon plasmonic functionalization with Au NPs. Finite-difference time-domain simulations confirmed the attribution of the chiroptical activity to the helical assembly of gold nanoparticles. The position of the circular dichroism peaks observed for these chiral structures overlaps with those obtained before for Au NPs and their constructs with molecular and nanoscale chirality. This work provides an experimental and computational pathway to utilize chiroplasmonic particles assembled on bacteria for bioanalytical purposes.

Anti-Biofilm Activity of Graphene Quantum Dots via Self-Assembly with Bacterial Amyloid Proteins.

Bacterial biofilms represent an essential part of Earth's ecosystem that can cause multiple ecological, technological, and health problems. The environmental resilience and sophisticated organization of biofilms are enabled by the extracellular matrix that creates a protective network of biomolecules around the bacterial community. Current anti-biofilm agents can interfere with extracellular matrix production but, being based on small molecules, are degraded by bacteria and rapidly diffuse away from biofilms. Both factors severely reduce their efficacy, while their toxicity to higher organisms creates additional barriers to their practicality. In this paper, we report on the ability of graphene quantum dots to effectively disperse mature amyloid-rich Staphylococcus aureus biofilms, interfering with the self-assembly of amyloid fibers, a key structural component of the extracellular matrix. Mimicking peptide-binding biomolecules, graphene quantum dots form supramolecular complexes with phenol-soluble modulins, the peptide monomers of amyloid fibers. Experimental and computational results show that graphene quantum dots efficiently dock near the N-terminus of the peptide and change the secondary structure of phenol-soluble modulins, which disrupts their fibrillation and represents a strategy for mitigation of bacterial communities.

Antimicrobial Microwebs of DNA-Histone Inspired from Neutrophil Extracellular Traps.

Neutrophil extracellular traps (NETs) are decondensed chromatin networks released by neutrophils that can trap and kill pathogens but can also paradoxically promote biofilms. The mechanism of NET functions remains ambiguous, at least in part, due to their complex and variable compositions. To unravel the antimicrobial performance of NETs, a minimalistic NET-like synthetic structure, termed "microwebs," is produced by the sonochemical complexation of DNA and histone. The prepared microwebs have structural similarity to NETs at the nanometer to micrometer dimensions but with well-defined molecular compositions. Microwebs prepared with different DNA to histone ratios show that microwebs trap pathogenic Escherichia coli in a manner similar to NETs when the zeta potential of the microwebs is positive. The DNA nanofiber networks and the bactericidal histone constituting the microwebs inhibit the growth of E. coli. Moreover, microwebs work synergistically with colistin sulfate, a common and a last-resort antibiotic, by targeting the cell envelope of pathogenic bacteria. The synthesis of microwebs enables mechanistic studies not possible with NETs, and it opens new possibilities for constructing biomimetic bacterial microenvironments to better understand and predict physiological pathogen responses.

Unexpected insights into antibacterial activity of zinc oxide nanoparticles against methicillin resistant Staphylococcus aureus (MRSA).

Zinc oxide nanoparticles (ZnO-NPs) are attractive as broad-spectrum antibiotics, however, their further engineering as antimicrobial agents and clinical translation is impeded by controversial data about their mechanism of activity. It is commonly reported that ZnO-NP's antimicrobial activity is associated with the production of reactive oxygen species (ROS). Here we disprove this concept by comparing the antibacterial potency of ZnO-NPs and their capacity to generate ROS with hydrogen peroxide (H2O2). Then, using gene transcription microarray analysis, we provide evidence for a novel toxicity mechanism. Exposure to ZnO-NPs resulted in over three-log reduction in colonies of methicillin resistant S. aureus with minimal increase in ROS or lipid peroxidation. The amount of ROS required for the same amount of killing by H2O2 was much greater than that generated by ZnO-NPs. In contrast to H2O2, ZnO-NP mediated killing was not mitigated by the antioxidant, N-acetylcysteine. ZnO-NPs caused significant up-regulation of pyrimidine biosynthesis and carbohydrate degradation. Simultaneously, amino acid synthesis in S. aureus was significantly down-regulated indicating a complex mechanism of antimicrobial action involving multiple metabolic pathways. The results of this study point to the importance of specific experimental controls in the interpretation of antimicrobial mechanistic studies and the need for targeted molecular mechanism studies. Continued investigation on the antibacterial mechanisms of biomimetic ZnO-NPs is essential for future clinical translation.