Biology subjects

Gadjeva, M.

Publications and source records attributed to Gadjeva, M..

3 recordsLinked to original sources

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↗