Biology subjects

Varga, J. J.

Publications and source records attributed to Varga, J. J..

3 recordsLinked to original sources

bioRxiv2023

Efficacy of a Pseudomonas aeruginosa Serogroup O9 Vaccine

There are currently no approved vaccines against the opportunistic pathogen Pseudomonas aeruginosa. Among vaccine targets, the lipopolysaccharide (LPS) O antigen of P. aeruginosa is the most immunodominant protective candidate. There are twenty different O antigens composed of different repeat sugars structures conferring serogroup specificity, and ten are found most frequently in infection. Thus, one approach to combat infection by P. aeruginosa could be to generate immunity with a vaccine cocktail that includes all these serogroups. Serogroup O9 is one of the ten serogroups commonly found in infection, but it has never been developed into a vaccine, likely due, in part, to the acid labile nature of the O9 polysaccharide. Our laboratory has previously shown that intranasal administration of an attenuated Salmonella strain expressing the P. aeruginosa serogroup O11 LPS O antigen was effective in clearing and preventing mortality in mice following intranasal challenge with serogroup O11 P. aeruginosa. Consequently, we set out to develop a P. aeruginosa serogroup O9 vaccine using a similar approach. Here we show that Salmonella expressing serogroup O9 triggered an antibody-mediated immune response following intranasal administration to mice and that it conferred protection from P. aeruginosa serogroup O9 in a murine model of acute pneumonia.

microbiology↗

bioRxiv2021

Antibiotics drive expansion of rare pathogens in a chronic infection microbiome model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment, and are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model, and benchmark on clinical data. We show that, in the absence of antibiotics, the microbiome structure in a synthetic sputum medium is highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path towards microbiome-informed conditional treatment strategies.

microbiology↗

bioRxiv2019

Microbiome data enhances predictive models of lung function in people with CF

BackgroundMicrobiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but non-pathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). MethodsWe collected health information (lung function, age, BMI) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use Elastic Net regularization to train linear models predicting lung function and extract the most informative features. ResultsModels trained on whole microbiome quantitation outperform models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retain key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF related diabetes). In addition, our models select non-pathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. ConclusionsThese results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.

bioinformatics↗