Biology subjects

Kashi, Y.

Publications and source records attributed to Kashi, Y..

3 recordsLinked to original sources

bioRxiv2019

Intestinal dysbiosis in carbapenem-resistant Enterobacteriaceae carriers

Infection with Carbapenem-Resistant Enterobacteriaceae (CRE) became an important challenge in health-care settings and a growing concern worldwide. Since infection is preceded by colonization, an understanding of the latter may reduce CRE-infections. We aimed to characterize the gut microbiota after colonization by CRE, assuming that an imbalanced gastrointestinal tract (GIT)-associated microbiota precedes CRE-colonization. We evaluated the GIT-microbiota using 16S rRNA genes sequencing extracted of fecal samples, collected from hospitalized CRE-carriers, and two control groups of hospitalized non-carriers and healthy adults. The microbiota diversity and composition in CRE-colonized patients differed from that of the control groups participants. These CRE-carriers displayed lower phylogenetic diversity and dysbiotic microbiota, enriched with members of the Enterobacteriaceae family. Concurrent with the bloom in Enterobacteriaceae, a depletion of anaerobic commensals was observed. Additionally, changes in several predicted metabolic pathways were observed for the CRE-carriers. Concomitant, we found higher prevalence of bacteremia in the CRE-carriers. Several clinical factors that might induce change in the microbiota were examined and found as insignificant between the groups. CRE-colonized patients have dysbiotic gut microbiota in terms of diversity and community membership, associated with increased risk for systemic infection. Our study results provides justification for attempts to restore the dysbiotic microbiota with probiotics or fecal transplantation.

microbiology↗

bioRxiv2019

Sticky Situations: Bacterial Attachment Deciphered by Interferometry of Silicon Microstructures

The peculiarities of surface-bound bacterial cells are often overshadowed by the study of planktonic cells in clinical microbiology. Thus, we employ phase-shift reflectometric interference spectroscopic measurements to observe the interactions between bacterial cells and abiotic, microstructured material surfaces in a label-free, real-time manner. Both material characteristics (i.e., substrate surface charge and wettability) and characteristics of the bacterial cells (i.e., motility, cell charge, biofilm formation, and physiology) drive bacteria to adhere to a particular surface. We conclude that the attachment of bacterial cells to a surface is determined by the culmination of numerous factors. When specific characteristics of the bacteria are met with factors of the surface, enhanced cell attachment and biofilm formation occur. Such knowledge can be exploited to predict antibiotic efficacy, biofilm development, enhance biosensor development, as well as prevent biofouling.

microbiology↗

bioRxiv2019

Genome-Scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation

Atrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as an in silico tool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model, iRZ960, for Paenarthrobacter aurescens TC1 - a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence of P. aurescens TC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verified in vitro. Experimental validations support the reliability of the models predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

bioinformatics↗