Biology subjects

McDermott, P. F.

Publications and source records attributed to McDermott, P. F..

8 recordsLinked to original sources

bioRxiv2024

Paired metagenomic and chemical evaluation of an aflatoxin contaminated dog kibble

Identification of chemical toxins from complex or highly processed foods can present needle in the haystack challenges for chemists. Metagenomic data can guide chemical toxicity evaluations with DNA-based description of the wholistic composition (bacterial, eukaryotic, protozoal, viral, and antimicrobial resistance) of any food suspected to harbor toxins, allergens, or pathogens. This approach can focus chemistry-based diagnostics, improve risk assessment, and address data gaps. There is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity. Here we evaluate an aflatoxin contaminated kibble with known levels of specific mycotoxins and demonstrate that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with levels of aflatoxin quantified by Liquid Chromatography Mass Spectrometry (LCMS). Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. Metagenomic data paired with chemical data provides a novel modality to address current data gaps pertaining to mycotoxin toxicity exposures, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

microbiology↗

bioRxiv2024

The spread of pESI-mediated extended-spectrum cephalosporin resistance in Salmonella serovars - Infantis, Senftenberg, and Alachua isolated from food animal sources in the United States

The goal of this study is to investigate the origin, prevalence, and evolution of the pESI megaplasmid in Salmonella isolated from animals, foods, and humans. We queried 510,097 Salmonella genomes under the National Center for Biotechnology Information (NCBI) Pathogen Detection (PD) database for the presence of potential sequences containing the pESI plasmid in animal, food, and environmental sources. The presence of the pESI megaplasmid was confirmed by using seven plasmid-specific markers (rdA, pilL, SogS, TrbA, ipf, ipr2 and IncFIB(pN55391)). The plasmid and chromosome phylogeny of these isolates was inferred from single nucleotide polymorphisms (SNPs). Our search resolved six Salmonella clusters carrying the pESI plasmid. Four were emergent Salmonella Infantis clusters, and one each belonged to serovar Senftenberg and Alachua. The Infantis cluster with a pESI plasmid carrying blaCTX-M-65 gene was the biggest of the four emergent Infantis clusters, with over 10,000 isolates. This cluster was first detected in South America and has since spread widely in United States. Over time the composition of pESI in United States has changed with the average number of resistance genes showing a decrease from 9 in 2014 to 5 in 2022, resulting from changes in gene content in two integrons present in the plasmid. A recent and emerging cluster of Senftenberg, which carries the blaCTX-M-65 gene and is primarily associated with turkey sources, was the second largest in the United States. SNP analysis showed that this cluster likely originated in North Carolina with the recent acquisition of the pESI plasmid. A single Alachua isolate from turkey was also found to carry the pESI plasmid containing blaCTX-M-65 gene. The study of the pESI plasmid, its evolution and mechanism of spread can help us in developing appropriate strategies for the prevention and further spread of this multi-drug resistant plasmid in Salmonella in poultry and humans.

genomics↗

bioRxiv2023

Fecal microbiomes of laboratory beagles receiving antiparasitic formulations in an experimental setting

Here we describe the fecal microbiome of laboratory beagles in a non-invasive and humane experiment designed to contrast in vivo versus invitro bioequivalence in response to antiparasitic drug administration. The experiment provided a unique opportunity to describe the fecal microbiota of dogs in an experimental setting prior to their adoption. These data are contributed as a resource for the scientific community by the Center for Veterinary Medicine (CVM) of the U.S. Food and Drug Administration (FDA).

microbiology↗

bioRxiv2023

Metagenomic survey of antimicrobial resistance (AMR) in Maryland surface waters differentiated by high and low human impact

In alignment with the One Health paradigm, surface waters are being evaluated as a modality to better understand baseline antimicrobial resistance (AMR) across the environment to supplement existing AMR monitoring in pathogens associated with humans, foods, and animals. Here, we use metagenomic and quasimetagenomic sequence data to describe AMR in Maryland surface waters from developed (high human impact) and natural (low human impact) classifications by the National Land Cover Database (NLCD). Critically important {beta}-lactamase genes were observed in twice as many high human impact zones. All data are available under BioProject PRJNA79347. https://www.ncbi.nlm.nih.gov/bioproject/794347

microbiology↗

bioRxiv2022

Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods

Surface waters present a unique challenge for the monitoring of critically important antimicrobial resistance. Metagenomic approaches provide unbiased descriptions of taxonomy and antimicrobial resistance genes in many environments, but for surface water, culture independent data is insufficient to describe critically important resistance. To address this challenge and expand resistome reporting capacity of antimicrobial resistance in surface waters, we apply metagenomic and quasimetagenomic (enriched microbiome) data to examine and contrast water from two sites, a creek near a hospital, and a reservoir used for recreation and municipal water. Approximately 30% of the National Antimicrobial Resistance Monitoring Systems critically important resistance gene targets were identified in enriched data contrasted to only 1% in culture independent data. Four different analytical approaches consistently reported substantially more antimicrobial resistance genes in quasimetagenomic data compared to culture independent data across most classes of antimicrobial resistance. Statistically significant differential fold changes (p<0.05) of resistance determinants were used to infer microbiological differences in the waters. Important pathogens associated with critical antimicrobial resistance were described for each water source. While the single time-point for only two sites represents a small pilot project, the successful reporting of critically important resistance determinants is proof of concept that the quasimetagenomic approach is robust and can be expanded to multiple sites and timepoints for national and global monitoring and surveillance of antimicrobial resistance in surface waters.

microbiology↗

bioRxiv2019

Genomic Analysis of Emerging Florfenicol-Resistant Campylobacter coli Isolated from the Intestinal Cecal Contents of Cattle in the United States

ObjectiveGenomic analyses were performed on florfenicol resistant (FFNR) Campylobacter coli (C. coli) isolated from cattle and the cfr(C) gene-associated multi-drug resistance (MDR) plasmid was characterized.\n\nMethodsSixteen FFNR C. coli isolates recovered between 2013-2018 from beef cattle were sequenced using MiSeq. Genomes and plasmids were closed for three of the isolates using the PacBio(R) system. Single nucleotide polymorphisms (SNPs) across the genome and the structures of MDR plasmids were investigated. Conjugation experiments were performed to determine the transferability of cfr(C) associated MDR plasmids. The spectrum of resistance encoded by the cfr(C) gene was further investigated by agar dilution antimicrobial susceptibility testing.\n\nResultsAll 16 FFNR isolates were MDR and exhibited co-resistance to ciprofloxacin, nalidixic acid, clindamycin and tetracycline. All isolates shared the same resistance genotype, carrying aph(3)-III, hph, {triangleup}aadE (truncated), blaOXA-61, cfr(C), and tet(O) genes plus a mutation of GyrA T86I. The cfr(C), aph(3)-III, hph {triangleup}aadE, and tet(O) genes were co-located on transferable MDR plasmids with size 48-50 kb. These plasmids showed high sequence homology with the pTet plasmid, and carried several Campylobacter virulence genes, including virB2, virB4, virB5, VirB6, virB7, virB8, virb9, virB10, virB11 and virD4. The cfr(C) gene conferred resistance to florfenicol (8-32 {micro}g/ml), clindamycin (512-1,024 {micro}g/ml), linezolid (128-512 {micro}g/ml), and tiamulin (1,024 {micro}g/ml). Phylogenetic analysis showed SNP differences ranging from 11-2,248 among the 16 isolates.\n\nConclusionsThe results showed that the cfr(C) gene located in the conjugative pTet MDR/virulence plasmid is present in diverse strains, where it confers high levels of resistance to several antimicrobials, including linezolid, a critical drug for treating Gram positive bacterial infections in humans. This study highlights the power of genomic antimicrobial resistance surveillance to uncover the intricacies of transmissible co-resistance and provides information that is needed for accurate risk assessment and mitigation strategies.

microbiology↗

bioRxiv2019

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↗

bioRxiv2018

Using machine learning to predict antimicrobial minimum inhibitory concentrations and associated genomic features for nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of food-borne disease in the United States. Whole genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, were used to generate XGBoost-based machine learning models for predicting minimum inhibitory concentrations (MICs) for 15 antibiotics. The MIC prediction models have average accuracies between 95-96% within {+/-} 1 two-fold dilution factor and can predict MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for training sets, we show that highly accurate MIC prediction models can be generated with fewer than 500 genomes. We also show that our approach for predicting MICs is stable over time despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

bioinformatics↗