bioRxiv Science⌕ Search

Biology subjects

Tadesse, D. A.

Publications and source records attributed to Tadesse, D. A..

3 recordsLinked to original sources

Application of Quasimetagenomics Methods to Define Microbial Diversity and Subtype Listeria monocytogenes in Dairy and Seafood Production Facilities

Microorganisms frequently colonize surfaces and equipment within food production facilities. Listeria monocytogenes is a ubiquitous foodborne pathogen widely distributed in food production environments and is the target of numerous control and prevention procedures. Detection of L. monocytogenes in a food production setting requires culture dependent methods, but the complex dynamics of bacterial interactions within these environments and their impact on pathogen detection remains largely unexplored. To address this challenge, we applied both 16S rRNA and shotgun quasimetagenomic (enriched microbiome) sequencing of swab culture enrichments from seafood and dairy production environments. Utilizing 16S rRNA amplicon sequencing, we observed variability between samples taken from different production facilities and a distinctive microbiome for each environment. With shotgun quasimetagenomic sequencing, we were able to assemble L. monocytogenes metagenome assembled genomes (MAGs) and compare these MAGSs to their previously sequenced whole genome sequencing (WGS) assemblies, which resulted in two polyphyletic clades (lineages I and II). Using these same datasets together with in silico downsampling to produce a titration series of proportional abundances of L. monocytogenes, we were able to begin to establish limits for Listeria detection and subtyping using shotgun quasimetagenomics. This study contributes to the understanding of microbial diversity within food production environments and presents insights into how many reads or relative abundance is needed in a metagenome sequencing dataset to detect, subtype, and source track at a SNP level, as well as providing an important foundation for utilizing metagenomics to mitigate unfavorable occurrences along the farm to fork continuum. IMPORTANCEIn developed countries, the human diet is predominantly food commodities, which have been manufactured, processed, and stored in a food production facility. It is well known that the pathogen Listeria monocytogenes is frequently isolated from food production facilities and can cause serious illness to susceptible populations. Multistate outbreaks of L. monocytogenes over the last 10 years have been attributed to food commodities manufactured and processed in production facilities, especially those dealing with dairy products such as cheese and ice cream. A myriad of recalls due to possible L. monocytogenes contamination have also been issued for seafood commodities originating from production facilities. It is critical to public health that the means of growth, survival and spread of Listeria in food production ecosystems is investigated with developing technologies, such as 16S rRNA and quasimetagenomic sequencing, to aid in the development of effective control methods.

microbiology↗

Template plasmid integration in germline genome-edited cattle.

We analyzed publicly available whole genome sequencing data from cattle which were germline genome-edited to introduce polledness. Our analysis discovered the unintended heterozygous integration of the plasmid and a second copy of the repair template sequence, at the target site. Our finding underscores the importance of employing screening methods suited to reliably detect the unintended integration of plasmids and multiple template copies.

genomics↗

Using the NCBI AMRFinder Tool to Determine Antimicrobial Resistance Genotype-Phenotype Correlations Within a Collection of NARMS Isolates

Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify acquired AMR genes in whole genome sequences, the National Center for Biotechnology Information (NCBI) has produced a high-quality, curated, AMR gene reference database consisting of up-to-date protein and gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, the Bacterial Antimicrobial Resistance Reference Gene Database contains 4,579 antimicrobial resistance gene proteins and more than 560 HMMs. Here, we describe AMRFinder, a tool that uses this reference dataset to identify AMR genes. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder against resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene database. Most gene calls were identical, but there were 1,229 gene symbol differences between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that Resfinder found, while Resfinder missed 1,147 loci AMRFinder identified. Two missing drug classes from the 2017 version of ResFinder contributed 81% of missed loci. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system. ImportanceAntimicrobial resistance is a major public health problem. Traditionally, antimicrobial resistance has been identified using phenotypic assays. With the advent of genome sequencing, we now can identify resistance genes and deduce if an isolate could be resistant to antibiotics. We describe a database of 4,579 acquired antimicrobial resistance genes, the largest publicly available, and a software tool to identify genes in bacterial genomes, AMRFinder. Unlike other tools, AMRFinder uses a gene hierarchy to prevent overpredicting what the correct gene call should be, enabling more accurate assessment. To assess these resources, we determined the resistance gene content of over 6,200 bacterial isolates from the National Antimicrobial Resistance Monitoring System that have been assayed using traditional methods and that also have had their genomes sequenced. We also compared our gene assessments to those of a popularly used tool. We found that AMRFinder has a high overall consistency between genotypes and phenotypes.

microbiology↗