Biology subjects

Bezdan, D.

Publications and source records attributed to Bezdan, D..

3 recordsLinked to original sources

bioRxiv2018

Ozone Treatment for Elimination of Bacteria in Medical Environments

Pathogenic bacteria and viruses in medical environments can lead to treatment complications and hospital-acquired infections (HAIs), and current cleaning protocols do not address hard-to-access areas or that may be beyond line-of-sight treatment such as with ultraviolet radiation. At the time of writing, the ongoing pandemic of the novel coronavirus known as novel coronavirus (2019-nCoV) has claimed over 4 million cases worldwide and is expected to have multiple peaks, with possible resurgences throughout 2020. It is therefore imperative that disinfection methods in the meantime be employed to keep up with the supply of personal protective equipment (PPE) and sterilize a wide array of surfaces as quarantine lockdowns begin to be lifted. Here, we tested the efficacy of Sani Sport ozone devices as a means to treat hospital equipment and surfaces for killing bacteria, degrading synthetic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, and RNA from non-replicative capsid enclosed SARS-CoV-2. We observed a rapid killing of medically-relevant and environmental bacteria (Escherichia coli, Enterococcus faecalis, Bacillus subtlis, and Deinococcus radiodurans) across four surfaces (blankets, catheter, remotes, and syringes) within 30 minutes, and up to a 99% reduction in viable bacteria at the end of 2-hour treatment cycles. Significant RNA degradation of synthetic SARS-CoV-2 RNA was seen an hour into the ozone treatment as compared to non-treated controls and a non-replicative form of the virus was shown to have significant RNA degradation at 30 minutes compared to a no treatment control and RNA degradation could be reliably detected at 10,000 and 1,000 copies of virus per sample. These results show the strong promise of ozone treatment for reducing risk of infection and HAIs.

microbiology↗

bioRxiv2018

Multi-omics approach identifies novel pathogen-derived prognostic biomarkers in patients with Pseudomonas aeruginosa bloodstream infection

Pseudomonas aeruginosa is a human pathogen that causes health-care associated blood stream infections (BSI). Although P. aeruginosa BSI are associated with high mortality rates, the clinical relevance of pathogen-derived prognostic biomarker to identify patients at risk for unfavorable outcome remains largely unexplored. We found novel pathogen-derived prognostic biomarker candidates by applying a multi-omics approach on a multicenter sepsis patient cohort. Multi-level Cox regression was used to investigate the relation between patient characteristics and pathogen features (2298 accessory genes, 1078 core protein levels, 107 parsimony-informative variations in reported virulence factors) with 30-day mortality. Our analysis revealed that presence of the helP gene encoding a putative DEAD-box helicase was independently associated with a fatal outcome (hazard ratio 2.01, p = 0.05). helP is located within a region related to the pathogenicity island PAPI-1 in close proximity to a pil gene cluster, which has been associated with horizontal gene transfer. Besides helP, elevated protein levels of the bacterial flagellum protein FliL (hazard ratio 3.44, p < 0.001) and of a bacterioferritin-like protein (hazard ratio 1.74, p = 0.003) increased the risk of death, while high protein levels of a putative aminotransferase were associated with an improved outcome (hazard ratio 0.12, p < 0.001). The prognostic potential of biomarker candidates and clinical factors was confirmed with different machine learning approaches using training and hold-out datasets. The helP genotype appeared the most attractive biomarker for clinical risk stratification due to its relevant predictive power and ease of detection.

microbiology↗

bioRxiv2017

Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Metagenomics

Emerging Linked-Read technologies (aka Read-Cloud or barcoded short-reads) have revived interest in standard short-read technology as a viable way to understand large-scale structure in genomes and metagenomes. Linked-Read technologies, such as the 10X Chromium system, use a microfluidic system and a set of specially designed 3 barcodes (aka UIDs) to tag short DNA reads which were originally sourced from the same long fragment of DNA; subsequently, these specially barcoded reads are sequenced on standard short read platforms. This approach results in interesting compromises. Each long fragment of DNA is covered only sparsely by short reads, no information about the relative ordering of reads from the same fragment is preserved, and typically each 3 barcode matches reads from 2-20 long fragments of DNA. However, compared to long read platforms like those produced by Pacific Biosciences and Oxford Nanopore the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads.\n\nThe use of Linked-Reads presents a new set of algorithmic challenges. In this paper, we formally describe one particular issue common to all applications of Linked-Read technology: the deconvolution of reads with a single 3 barcode into clusters that correspond to a single long fragment of DNA. We introduce Minerva, A graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results: improving the specificity of taxonomic assignments, and by improving clustering of related sequences. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics.

bioinformatics↗