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Dootz, J. N.

Publications and source records attributed to Dootz, J. N..

4 recordsLinked to original sources

Measurement Quality Metrics to Improve Absolute Microbial Cell Counting

Total and viable microbial cell counts are increasingly important for applications including live biotherapeutic products, food safety, and probiotics. In microbiology, cells are quantified using methods such as colony forming unit (CFU), flow cytometry, and polymerase chain reaction (PCR), but different methods measure different aspects of the cells (measurands), and results may not be directly comparable across methods. In the absence of a ground-truth reference material for cell count, one cannot quantify the accuracy of any cell counting method, which limits method performance assessments and comparisons. Herein, a modified analysis of cell counting methods based on the ISO 20391-2:2019 standard was developed and demonstrated for microbial cell samples diluted over a log-scale range of concentrations. Escherichia coli samples ranging in concentration from approximately 5 x 105 cells/mL to 2 x 107 cells/mL were quantified using CFU, Coulter principle, fluorescence flow cytometry, and impedance flow cytometry. Quality metrics modified from the ISO standard were calculated for each method and shown to be repeatable across replicate experiments. The quality metrics illustrate large differences in proportionality and variability across methods, with total cell counts in good agreement and viable cell count having more variability. As the ISO standard is meant to guide fit-for-purpose method selection, interpretation of the results and quality metrics can drive method choice and optimization. The framework introduced here will help researchers select fit-for-purpose counting methods for quantification of microbial total and viable cells across a range of applications.

microbiology↗

Measuring Microbial Community-Wide Antibiotic Resistance Propagation via Natural Transformation in the Human Gut Microbiome

1) This work explored the role of natural transformation - a mechanism by which bacteria uptake and express extracellular genes - in driving antibiotic resistance propagation in the human gut microbiome. The model extracellular antibiotic resistance gene (eARG) - a plasmid containing a kanamycin resistance (kanR) gene and a green fluorescence protein (GFP) gene - was dosed into pooled and homogenized human stool and incubated anaerobically. Cellular uptake of the eARG was assessed via droplet digital PCR, the expression of newly acquired genes was assessed by culturing on selective media and fluorescent microscopy, newly resistant isolates were identified by long-read Nanopore sequencing and the impacts on the taxonomy of the gut microbiome was assessed using shotgun Illumina sequencing. Significant gene uptake of both kanR and GFP was quantified in gut microbes, and extent of gene accumulation correlated with background kanamycin levels. Gut microbes dosed with background kanamycin expressed kanamycin resistance acquired by the eARG (as quantified by CFU on kanamycin-containing media). Newly resistant isolates, identified as Enterococcus faecium by long-read sequencing, also expressed green fluorescence acquired from the eARG. Though compositional changes of the kanamycin-resistant subpopulation were observed in the gut microbiome in response to eARG and antibiotic exposure, these changes were not reproducible among replicates and trends in taxonomy due to transformation could not be identified. This comprehensive analysis therefore establishes the significant propagation of antibiotic resistance within the human gut microbiome due to eARG exposure, while evaluating the utility of various measurements in characterizing transformation in a complex microbial community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/625464v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1d9dfc0org.highwire.dtl.DTLVardef@f0df34org.highwire.dtl.DTLVardef@1ce04bforg.highwire.dtl.DTLVardef@99a6e2_HPS_FORMAT_FIGEXP M_FIG C_FIG 2) ImportanceInfections from antibiotic resistant bacteria in the human gut microbiome are a growing public health concern. Antibiotic resistance may develop in gut microbiota from exposure to environmentally prevalent extracellular antibiotic resistance genes (eARGs). This work explores the impact of eARG exposure on a complex human gut microbial community. It quantifies significant accumulation and expression of eARG-borne genes by endogenous gut microorganisms, thereby demonstrating that natural transformation may play a role in resistance propagation in the human gut. It also demonstrates the highly variable changes in gut taxonomy in response to eARG exposure, implying that eARGs may impact gut composition and therefore downstream human health effects. These data may be useful in characterizing and mitigating resistance propagation in the human gut, and in general, the suite of genotypic and phenotypic measurements used constitute a quantitative framework to characterize the effects of perturbations on complex microbiomes.

evolutionary biology↗

Analytical Assessment of Metagenomic Workflows for Pathogen Detection with NIST RM 8376 and Two Sample Matrices

We assessed the analytical performance of metagenomic workflows using NIST Reference Material 8376 DNA from bacterial pathogens spiked into two simulated clinical samples: cerebral spinal fluid (CSF) and stool. Sequencing and taxonomic classification were used to generate signals for each sample and taxa of interest, and to estimate the limit of detection (LOD), the response function, and linear dynamic range. We found that the LODs for taxa spiked into CSF ranged from approximately (0.1 to 0.3) copy/L, with a linearity of 0.96 to 0.99. For stool, the LODs ranged from (10 to 221) copy/L, with a linearity of 0.99 to 1.01. Further, discriminating different E. coli strains proved to be workflow-dependent, as only one classifier:database combination of the three tested showed the ability to differentiate the two pathogenic and commensal strains. Surprisingly, when we compared the response functions of the same taxa in the two different sample types, we found those functions to be the same, despite large differences in LODs. This suggests that the "agnostic diagnostic" theory for metagenomics may apply to different target organisms and different sample types. Using RMs, we were able to generate quantitative analytical performance metrics for each workflow and sample set, enabling relatively rapid workflow screening before employing clinical samples. This makes these RMs a useful tool that will generate data needed to support translation of metagenomics into regulated use. ImportanceAssessing the analytical performance of metagenomic workflows, especially when developing clinical diagnostics, is foundational for ensuring that the measurements underlying a diagnosis are supported by rigorous characterization. To facilitate the translation of metagenomics into clinical practice, workflows must be tested using control samples designed to probe the analytical limitations (e.g. limit of detection). Spike-ins allow developers to generate fit-for-purpose control samples for initial workflow assessments and inform decisions about further development. However, clinical sample types include a wide range of compositions and concentrations, each presenting different detection challenges. In this work, we demonstrate how spike-ins elucidate workflow performance in two highly dissimilar sample types (stool and CSF); and we provide evidence that detection of individual organisms is unaffected by background sample composition, making detection sample agnostic within a workflow. These demonstrations and performance insights will facilitate translation of the technology to the clinic.

bioinformatics↗

A Sensitivity Analysis of Methodological Variables Associated with Microbiome Measurements

The experimental methods employed during metagenomic sequencing analyses of microbiome samples significantly impact the resulting data and typically vary substantially between laboratories. In this study, a full factorial experimental design was used to compare the effects of a select set of methodological choices (sample, operator, lot, extraction kit, variable region, reference database) on the analysis of biologically diverse stool samples. For each parameter investigated, a main effect was calculated that allowed direct comparison both between methodological choices (bias effects) and between samples (real biological differences). Overall, methodological bias was found to be similar in magnitude to real biological differences, while also exhibiting significant variations between individual taxa, even between closely related genera. The quantified method biases were then used to computationally improve the comparability of datasets collected under substantially different protocols. This investigation demonstrates a framework for quantitatively assessing methodological choices that could be routinely performed by individual laboratories to better understand their metagenomic sequencing workflows and to improve the scope of the datasets they produce.

microbiology↗