Mixed Populations and Co-Infection: Pseudomonas aeruginosa and Staphylococcus aureus

Supramolecular discrimination and diagnosis-guided treatment of intracellular bacteria

Pathogenic intracellular bacteria pose a significant threat to global public health due to the barriers presented by host cells hindering the timely detection of hidden bacteria and the effective delivery of therapeutic agents. To address these challenges, we propose a tandem diagnosis-guided treatment paradigm. A supramolecular sensor array is developed for simple, rapid, accurate, and high-throughput identification of intracellular bacteria. This diagnostic approach executes the significant guiding missions of screening a customized host-guest drug delivery system by disclosing the rationale behind the discrimination. We design eight azocalix[4]arenes with differential active targeting, cellular internalization, and hypoxia responsiveness to penetrate cells and interact with bacteria. Loaded with fluorescent indicators, these azocalix[4]arenes form a sensor array capable of discriminating eight intracellular bacterial species without cell lysis or separation. By fingerprinting specimens collected from bacteria-infected mice, the facilitated accurate diagnosis offers valuable guidance for selecting appropriate antibiotics. Moreover, mannose-modified azocalix[4]arene (ManAC4A) is screened as a drug carrier efficiently taken up by macrophages. Doxycycline loaded with ManAC4A exhibits improved efficacy against methicillin-resistant Staphylococcus aureus-infected peritonitis. This study introduces an emerging paradigm to intracellular bacterial diagnosis and treatment, offering broad potential in combating bacterial infectious diseases.

DJK-5, an anti-biofilm peptide, increases Staphylococcus aureus sensitivity to colistin killing in co-biofilms with Pseudomonas aeruginosa

Chronic infections represent a significant global health and economic challenge. Biofilms, which are bacterial communities encased in an extracellular polysaccharide matrix, contribute to approximately 80% of these infections. In particular, pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus are frequently co-isolated from the sputum of patients with cystic fibrosis and are commonly found in chronic wound infections. Within biofilms, bacteria demonstrate a remarkable increase in resistance and tolerance to antimicrobial treatment. We investigated the efficacy of combining the last-line antibiotic colistin with a membrane- and stringent stress response-targeting anti-biofilm peptide DJK-5 against co-biofilms comprised of multidrug-resistant P. aeruginosa and methicillin-resistant S. aureus (MRSA). Colistin lacks canonical activity against S. aureus. However, our study revealed that under co-biofilm conditions, the antibiofilm peptide DJK-5 synergized with colistin against S. aureus. Similar enhancement was observed when daptomycin, a cyclic lipopeptide against Gram-positive bacteria, was combined with DJK-5, resulting in increased activity against P. aeruginosa. The combinatorial treatment induced morphological changes in both P. aeruginosa and S. aureus cell shape and size within co-biofilms. Importantly, our findings also demonstrate synergistic activity against both P. aeruginosa and S. aureus in a murine subcutaneous biofilm-like abscess model. In conclusion, combinatorial treatments with colistin or daptomycin and the anti-biofilm peptide DJK-5 show significant potential for targeting co-biofilm infections. These findings offer promising avenues for developing new therapeutic approaches to combat complex chronic infections.

Facemask acne attenuation through modulation of indirect microbiome interactions

During the COVID-19 pandemic, facemasks played a pivotal role in preventing person-person droplet transmission of viral particles. However, prolonged facemask wearing causes skin irritations colloquially referred to as 'maskne' (mask + acne), which manifests as acne and contact dermatitis and is mostly caused by pathogenic skin microbes. Previous studies revealed that the putative causal microbes were anaerobic bacteria, but the pathogenesis of facemask-associated skin conditions remains poorly defined. We therefore characterized the role of the facemask-associated skin microbiota in the development of maskne using culture-dependent and -independent methodologies. Metagenomic analysis revealed that the majority of the facemask microbiota were anaerobic bacteria that originated from the skin rather than saliva. Previous work demonstrated direct interaction between pathogenic bacteria and antagonistic strains in the microbiome. We expanded this analysis to include indirect interaction between pathogenic bacteria and other indigenous bacteria classified as either 'pathogen helper (PH)' or 'pathogen inhibitor (PIn)' strains. In vitro screening of bacteria isolated from facemasks identified both strains that antagonized and promoted pathogen growth. These data were validated using a mouse skin infection model, where we observed attenuation of symptoms following pathogen infection. Moreover, the inhibitor of pathogen helper (IPH) strain, which did not directly attenuate pathogen growth in vitro and in vivo, functioned to suppress symptom development and pathogen growth indirectly through PH inhibitory antibacterial products such as phenyl lactic acid. Taken together, our study is the first to define a mechanism by which indirect microbiota interactions under facemasks can control symptoms of maskne by suppressing a skin pathogen.

A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria

Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options.

Strong Activity and No Resistance Induction Exerted by Cell-Free Supernatants from Lacticaseibacillus rhamnosus against Mono-Species and Dual-Species Biofilms of Wound Pathogens in In Vivo-like Conditions

It is widely agreed that microbial biofilms play a major role in promoting infection and delaying healing of chronic wounds. In the era of microbial resistance, probiotic strains or their metabolic products are emerging as an innovative approach for the treatment of hard-to-heal (chronic) wounds due to their antimicrobial, healing, and host immune-modulatory effects. In this study, we aimed to investigate the potential of cell-free supernatants (CFS) from Lacticaseibacillus rhamnosus GG against mono- and dual-species biofilms of wound pathogens in a 3D in vitro infection model. Mature biofilms of Pseudomonas aeruginosa and Staphylococcus aureus were obtained on collagen scaffolds in the presence of a simulant wound fluid (SWF) and treated with CFS at different doses and time intervals. At 1:4 dilution in SWF, CFS caused a marked reduction in the colony forming-unit (CFU) numbers of bacteria embedded in mono-species biofilms as well as bacteria released by the biofilms in the supernatant. CFU count and electron microscopy imaging also demonstrated a marked antibiofilm effect against dual-species biofilms starting from 8 h of incubation. Furthermore, CFS exhibited acceptable levels of cytotoxicity at 24 h of incubation against HaCaT cells and, differently from ciprofloxacin, failed to induce resistance after 15 passages at sub-inhibitory concentrations. Overall, the results obtained point to L. rhamnosus GG postbiotics as a promising strategy for the treatment of wound biofilms.

The great divide: rhamnolipids mediate separation between P. aeruginosa and S. aureus

The interactions between bacterial species during infection can have significant impacts on pathogenesis. Pseudomonas aeruginosa and Staphylococcus aureus are opportunistic bacterial pathogens that can co-infect hosts and cause serious illness. The factors that dictate whether one species outcompetes the other or whether the two species coexist are not fully understood. We investigated the role of surfactants in the interactions between these two species on a surface that enables P. aeruginosa to swarm. We found that P. aeruginosa swarms are repelled by colonies of clinical S. aureus isolates, creating physical separation between the two strains. This effect was abolished in mutants of S. aureus that were defective in the production of phenol-soluble modulins (PSMs), which form amyloid fibrils around wild-type S. aureus colonies. We investigated the mechanism that establishes physical separation between the two species using Imaging of Reflected Illuminated Structures (IRIS), which is a non-invasive imaging method that tracks the flow of surfactants produced by P. aeruginosa. We found that PSMs produced by S. aureus deflected the surfactant flow, which in turn, altered the direction of P. aeruginosa swarms. These findings show that rhamnolipids mediate physical separation between P. aeruginosa and S. aureus, which could facilitate coexistence between these species. Additionally, we found that a number of molecules repelled P. aeruginosa swarms, consistent with a surfactant deflection mechanism. These include Bacillus subtilis surfactant, the fatty acids oleic acid and linoleic acid, and the synthetic lubricant polydimethylsiloxane. Lung surfactant repelled P. aeruginosa swarms and inhibited swarm expansion altogether at higher concentration. Our results suggest that surfactant interactions could have major impacts on bacteria-bacteria and bacteria-host relationships. In addition, our findings uncover a mechanism responsible for P. aeruginosa swarm development that does not rely solely on sensing but instead is based on the flow of surfactant.

Using metabolic potential within the airway microbiome as predictors of clinical state in persons with cystic fibrosis

Introduction

Pulmonary exacerbations (PEx) in persons with cystic fibrosis (CF) are primarily related to acute or chronic inflammation associated with bacterial lung infections, which may be caused by several bacteria that activate similar bacterial genes and produce similar by-products. The goal of our study was to perform a stratified functional analysis of bacterial genes at three distinct time points in the treatment of a PEx in order to determine the role that specific airway microbiome community members may play within each clinical state (i.e., PEx, end of antibiotic treatment, and follow-up). Our secondary goal was to compare the change between clinical states with the metabolic activity of specific airway microbiome community members.

Methods

This was a prospective observational study of persons with CF treated with intravenous antibiotics for PEx between 2016 and 2020 at Children's National Hospital. Demographic and clinical information as well as respiratory samples were collected at hospital admission for PEx, end of antibiotic treatment, and follow-up. Metagenomic sequencing was performed; MetaPhlAn3 and HUMANn3 were used to assign sequences to bacterial species and bacterial metabolic genes, respectively.

Results

Twenty-two persons with CF, with a mean age of 14.5 (range 7-23) years, experienced 45 PEx during the study period. Two-hundred twenty-one bacterial species were identified in the respiratory samples from the study cohort. Ten bacterial species had differential gene abundance across changes in the clinical state including Staphylococcus aureus, Streptococcus salivarius, and Veillonella atypica (all padj < 0.01 and log2FoldChange > |2|). These corresponded to a differential abundance of bacterial genes, with S. aureus accounting for 81% of the genes more abundant in PEx and S. salivarius accounting for 83% of the genes more abundant in follow-up, all compared to the end of treatment. Lastly, 8,653 metabolic pathways were identified across samples, with again S. aureus and S. salivarius contributing to the differential abundance of pathways (106 in PEx vs. 66 in follow-up, respectively). V. atypica was associated with a single metabolic pathway (UDP-N-acetyl-D-glucosamine biosynthesis) increased in follow-up compared to PEx.

Discussion

Taken together, these data suggest that the metabolic potential of bacterial species can provide more insight into changes across clinical states than the relative abundance of the bacteria alone.