Biology subjects

Feizi, S.

Publications and source records attributed to Feizi, S..

4 recordsLinked to original sources

bioRxiv2025

LicD-Mediated Cell Wall Decoration Governs Phage Sensitivity in Enterococcus faecalis Clinical Isolates

Enterococcus faecalis has emerged as a prevalent antibiotic-resistant pathogen in clinical settings. Herein, we report the identification of three novel lytic phages targeting vancomycin-resistant E. faecalis. While the isolated phages all belonged to the Kochikohdavirus genus, there were distinctive differences in their tail fibre proteins, affecting their adsorption. The phages showed strong antibacterial activity with wide host range, infecting >90% of the tested E. faecalis clinical and hospital wastewater isolates (n=13) with variable efficiency. The variation in host range was genomically correlated to the presence of the LicD gene in phage sensitive bacteria, which is responsible for phosphorylcholine decoration of the bacterial cell wall. Furthermore, the isolated phages were predicted to harbour genes encoding for depolymerase enzymes, which was confirmed by in vitro testing showing a >80% reduction in biofilm biomass. Phages inhibited bacterial growth for [≥]12 hours, followed by the emergence of bacteriophage insensitive mutants (BIMs) that were 4-fold more sensitive to vancomycin compared to parent strains. In addition, the isolated BIMs showed less capability of evading THP-1 macrophage and produced weaker biofilms. These findings underpin the potential of the isolated phages in combating recalcitrant E. faecalis associated biofilm-mediated infections.

microbiology↗

bioRxiv2023

Prophage acquisition by Staphylococcus aureus contributes to the expansion of Staphylococcal immune evasion

Staphylococcus aureus colonizes 30% of the human population, but only a few clones cause severe infections. S. aureus virulence varies and partly depends on the presence of prophages, viral DNA embedded in the S. aureus core genome, such as hlb-converting prophage ({phi}Sa3int). Human-adapted S. aureus often harbours a {phi}Sa3int group of prophages preferentially integrated into their {beta}-hemolysin (hlb) gene that encodes human immune evasion cluster (IEC) genes. Exotoxins and immune modulatory molecules encoded by this prophage can inhibit human innate immunity increasing S. aureus pathogenicity. This study aims to investigate the genomic and phenotypic plasticity of S. aureus and changes in its extracellular proteome after the acquisition of {phi}Sa3int prophage. To achieve this, we used S. aureus strains isolated from the sinus cavities of a patient with severe chronic rhinosinusitis (CRS) at two different time points (S. aureus SA222 and S. aureus SA333) and hybrid sequenced the strains using short-read Illumina and long-read Oxford nanopore technology. In silico analysis showed the presence of a {phi}Sa3int prophage in the later isolate but not in the earlier isolate while most of the core genes remained identical. Using mitomycin C, we induced the {phi}Sa3int prophage, and transduced it into the Sa3int-prophage-free SA222 isolate to obtain a laboratory generated double lysogen. We confirmed the successful lysogenisation with culture methods (spot assay, blood agar) and also by sequencing. We compared growth kinetics, biofilm biomass and metabolic activity between parent and the lysogen by establishing growth curves, crystal violet and resazurin assays. Exoproteins were identified and quantified using mass spectrophotometry. Integration of {phi}Sa3int prophage in SA222 down-regulated the beta-hemolysin expression of the lysogen. In silico analysis of the S. aureus genome confirmed the insertion of a [~]43.8 kb {phi}Sa3int prophage into hlb gene. Insertion of prophage DNA did not alter the growth kinetics, biofilm formation, adhesion to primary human nasal epithelial cells and the metabolic activity in a biofilm. However, the acquisition of {phi}Sa3int prophage significantly changed the expression of various secreted proteins, both bacterial and prophage-encoded. Altogether, thirty-eight exoproteins were significantly differentially regulated in the laboratory created lysogen, compared to its recipient strain SA222. Among these proteins, there was significant upregulation of 21 exoproteins (55.3 %) including staphylokinase (sak), SCIN (scn), and intercellular adhesion protein B (icaB) and downregulation of 17 exoproteins (44.7 %), including {beta}-hemolysin (hlb/sph) and outer membrane porin (phoE). Most of the upregulated proteins were involved in immunomodulation that help S. aureus escape human innate immunity and help cause chronic infection. These findings may contribute to the development of novel approaches to render S. aureus susceptible to the immune response by blocking prophage-associated defence mechanisms. HighlightsO_LIA {phi}Sa3int prophage preferentially integrates into the {beta}-haemolysin gene (hlb) gene thereby disrupting the beta-hemolysin function. C_LIO_LIA [~]43.8 kb {phi}Sa3int prophage acquisition by S. aureus has no impact on its growth kinetics, biofilm formation and adhesion to primary human nasal epithelial cells (HNECs). C_LIO_LIThe presence of a {phi}Sa3int group prophage likely enhances Staphylococcus aureus human immune evasion capability as the prophage encodes a complete set of immune evasion cluster (IEC) genes. C_LIO_LITargeted identification of virulence factors in addition to species and strain identification may lead to better-personalized therapy as not all S. aureus carry the same virulence genes. C_LI

microbiology↗

bioRxiv2019

scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks

Single cell RNA sequencing (scRNA-seq) provides a rich view into the heterogeneity underlying a cell population. However single-cell data are usually noisy and very sparse due to the presence of dropout genes. In this work we propose an approach to impute missing gene expressions in single cell data using generative adversarial networks (GANs). By learning an approximate distribution of the data, our approach, scGAIN, can impute dropouts in simulated and real single cell data. The work in this paper discusses how to adopt GAIN training model into the domain of imputing single cell data. Experiments show that scGAIN gives competitive results compared to the state-of-the-art approaches while showing superiority in various aspects in simulation and real data. Imputation by scGAIN successfully recovers the underlying clustering of different subpopulations, provides sharp estimates around true mean expressions and increase the correspondence with matched bulk RNAseq experiments.

bioinformatics↗

bioRxiv2018

Predicting trait regulators by identifying co-localization of DNA binding and GWAS variants in regulatory regions

Genomic regions associated with complex traits and diseases are primarily located in non-coding regions of the genome and have unknown mechanism of action. A critical step to understanding the genetics of complex traits is to fine-map each associated locus; that is, to find the causal variant(s) that underlie genetic associations with a trait. Fine-mapping approaches are currently focused on identifying genomic annotations, such as transcription factor binding sites, which are enriched in direct overlap with candidate causal variants. We introduce CONVERGE, the first computational tool to search for co-localization of GWAS causal variants with transcription factor binding sites in the same regulatory regions, without requiring direct overlap. As a proof of principle, we demonstrate that CONVERGE is able to identify five novel regulators of type 2 diabetes which subsequently validated in knockdown experiments in pancreatic beta cells, while existing fine-mapping methods were unable to find any statistically significant regulators. CONVERGE also recovers more established regulators for total cholesterol compared to other fine-mapping methods. CONVERGE is therefore unique and complementary to existing fine-mapping methods and is useful for exploring the regulatory architecture of complex traits.

bioinformatics↗