Biology subjects

Gadjeva, M.

Publications and source records attributed to Gadjeva, M..

5 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomics identifies the Pseudomonas aeruginosa major outer membrane protein OprF as a critical factor in blinding corneal infections

Pseudomonas aeruginosa is a globally recognized pathogen causing pulmonary, skin, and severe corneal infections (keratitis), with the potential to induce irreversible blindness if untreated. Spatial transcriptomic analysis of P. aeruginosa infected corneas identified elevated expression of the outer membrane proteins OprF and OprL and PA1414, which encodes the small RNA SicX in the corneal stroma compared with corneal epithelium. Comparative spatial transcriptomics analysis of corneas infected with an oprF transposon (TN) mutant showed reduced expression of the type III effector protein ExoT, which was absent in an oprF deficient mutant ({Delta}oprF) and in contrast to PA14, did not inhibit reactive oxygen species (ROS) production by neutrophils. Corneal infection with the {Delta}oprF mutant resulted in reduced corneal virulence and lower CFU compared to the parental PA14 strain. Collectively, our findings demonstrate a coordinated virulence program connecting OprF functionality with the release of ExoT and its ability to block ROS production and survive in infected corneas.

microbiology↗

bioRxiv2026

Emerging Pathogens in Urinary Tract Infections: Virulence and Phenotypic Characterization of Pseudomonas aeruginosa strains

Urinary tract infections (UTIs) affect a broad patient population and inflict a substantial financial burden on the U.S. healthcare system. While uropathogenic Escherichia coli (UPEC) causes the majority of cases, other pathogens are emerging. Analysis of patient data from our healthcare system in the Gulf Coast region of Alabama revealed that Pseudomonas aeruginosa accounted for 4.0% of UTI cases, roughly double the national average, prompting further investigation into this historically understudied uropathogen. Here, we performed whole-genome sequencing and phenotypic assays on 55 urinary P. aeruginosa isolates to identify key drivers of pathogenicity in the context of UTI. Multilocus sequence typing identified 19 novel sequence types, underscoring the uncharacterized diversity of urinary P. aeruginosa isolates. Serotype O6 was most common and enriched in patients with indwelling catheters, whereas O4 was linked to diabetes mellitus. Antibiotic susceptibility testing (AST) revealed high levofloxacin resistance (30.9%), with 23.6% multidrug-resistant (MDR) and 9.1% extensively drug-resistant (XDR) isolates. Resistance patterns correlated with demographics, including significantly higher meropenem and aztreonam resistance in isolates from African American patients. Phenotypic assays of growth, motility, and biofilm formation revealed negative correlations between antibiotic resistance and virulence. Specific virulence genes predicted enhanced iron acquisition, hemolysis, and colonization potential. Notably, motility and exotoxin profiles emerged as strong predictors of P. aeruginosa ascension in a murine UTI model. Together, these findings provide new biological and clinical insight into P. aeruginosa as a uropathogen and emphasize the need for continued research. IMPORTANCEPseudomonas aeruginosa is an emerging but understudied pathogen in urinary tract infections (UTIs). Given its resilience, adaptability, and the growing threat of multidrug resistance, P. aeruginosa remains a significant challenge in clinical microbiology and infection control. Our data reveal an increased prevalence of P. aeruginosa in our local patient population. In this study, we examined both genotypic and phenotypic traits of clinical isolates and correlated them with colonization in murine models and extensive patient metadata. We identified strong associations between antibiotic resistance patterns and patient demographics. Novel sequence type strains were linked to motility phenotypes in vitro. Additionally, specific flagellar alleles were associated with enhanced murine kidney colonization and recurrent UTIs in patients. These findings provide new insight into the evolutionary adaptations that contribute to P. aeruginosa uropathogenicity and support a more nuanced understanding of its clinical significance.

microbiology↗

bioRxiv2024

Spatial transcriptomics identifies novel P. aeruginosa virulence factors

To holistically unravel the complexity of pathogen-host interactions within infected tissues we leverage a dual spatial transcriptomic approach that, for the first time, simultaneously captures the expression of Pseudomonas aeruginosa genes alongside the entire host transcriptome in a model of ocular infection. This innovative method reveals differential pathogen and host-specific gene expression patterns across specific anatomical regions generating a unified transcriptional map of infection. By integrating these data, we developed a predictive ridge regression model trained on images from infected tissues. The model achieved an R{superscript 2} score of 0.923 in predicting bacterial burden distributions by using host features thereby predicting novel biomarkers associated with disease severity. Our analysis revealed a complex interplay between P. aeruginosa nutritional requirements and protective host responses and identified novel interactions between bacterial metabolite transport proteins and host autophagy. Among an array of iron acquisition gene transcripts that showed significant enrichment at the host-pathogen interface, we discovered a novel virulence mediator PA2590. This study highlights the power of spatial transcriptomics, particularly in combining bacterial and host transcriptomes, to uncover novel host-pathogen interactions, advance our understanding of bacterial virulence mechanisms, and point to druggable molecules.

microbiology↗

bioRxiv2024

Targeting Iron - Respiratory Reciprocity Promotes Bacterial Death

Discovering new bacterial signaling pathways offers unique antibiotic strategies. Here, through an unbiased resistance screen of 3,884 gene knockout strains, we uncovered a previously unknown non-lytic bactericidal mechanism that sequentially couples three transporters and downstream transcription to lethally suppress respiration of the highly virulent P. aeruginosa strain PA14 - one of three species on the WHOs Priority 1: Critical list. By targeting outer membrane YaiW, cationic lacritin peptide N-104 translocates into the periplasm where it ligates outer loops 4 and 2 of the inner membrane transporters FeoB and PotH, respectively, to suppress both ferrous iron and polyamine uptake. This broadly shuts down transcription of many biofilm-associated genes, including ferrous iron-dependent TauD and ExbB1. The mechanism is innate to the surface of the eye and is enhanced by synergistic coupling with thrombin peptide GKY20. This is the first example of an inhibitor of multiple bacterial transporters.

microbiology↗

bioRxiv2023

mtx-COBRA: Subcellular localization prediction for bacterial proteins

BackgroundBacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets. MethodWe present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Metas protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset. ResultsUsing benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb. Conclusionsmtx-COBRA provides an accessible pipeline with greater efficiency to classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.

bioinformatics↗