This repository includes the raw data, analysis code, and results of a systematic analysis of natural transformation (i.e., horizontal gene transfer; HGT) of bacteria in a naturally occurring microbiome (human stool) following exposure to free extracellular DNA (eDNA) that coded for antibiotic resistance. A full description of this effort is available in the associated publication.

This repository contains raw and intermediate data files as well as analysis code from the manuscript "Evaluating the Analytical Performance of Direct-to-Consumer Gut Microbiome Testing Services". In this analysis a pilot version of the homogenized whole human stool reference material was submitted as a sample to commercial direct to consumer microbiome testing companies. In addition, NIST conducted in house characterization by 16S amplicon sequencing and whole metagenomic sequencing (WMS) on the same sample. Raw sequencing data from the sequencing providers was not obtained for this study; however sequencing files (fastq) both 16S amplicon sequencing analysis and whole metagenomic sequencing (WMS) conducted at NIST are included. In addition, the fastq files from other donors that contributed to the pilot material and included in this analysis to represent biological diversity are also included in this record.

This repository contains raw and intermediate data files as well as analysis code from the manuscript ?Evaluating the Analytical Performance of Direct-to-Consumer Gut Microbiome Testing Services?. In this analysis a pilot version of the homogenized whole human stool reference material was submitted as a sample to commercial direct to consumer microbiome testing companies. In addition, NIST conducted in house characterization by 16S amplicon sequencing and whole metagenomic sequencing (WMS) on the same sample. Raw sequencing data from the sequencing providers was not obtained for this study; however sequencing files (fastq) both 16S amplicon sequencing analysis and whole metagenomic sequencing (WMS) conducted at NIST are included. In addition, the fastq files from other donors that contributed to the pilot material and included in this analysis to represent biological diversity are also included in this record.

In this study, the abundance and distribution of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), as well as the concentrations of antibiotics present in a mixed-use watershed in Athens, GA, USA were examined, in order to enhance understanding of the existing state of AR in the freshwater environment. The current study has shown that antibiotic-related contaminants are prevalent in the freshwater environment, including commensal and pathogenic bacteria that are resistant to antibiotics used for human and veterinary purposes, medically important antibiotics, as well as the genes associated with resistance to these antibiotics. This dataset is not publicly accessible because: Data belong to coauthor at USDA ARS. It can be accessed through the following means: The data presented in this study are available on request from the corresponding author, Jonathan Frye at USDA. Format: Statistical analysis of data from surface water samples, see the journal article's Supplementary Materials for additional information: https://www.mdpi.com/article/10.3390/antibiotics12111586/s1. This dataset is associated with the following publication: Cho, S., L. Hiott, Q. Read, J. Damashek, J. Westrich, M. Edwards, R. Seim, D. Glinski, J. Bateman McDonald, E. Ottesen, E. Lipp, M. Henderson, C. Jackson, and J. Frye. Distribution of Antibiotic Resistance in a Mixed-Use Watershed and the Impact of Wastewater Treatment Plants on Antibiotic Resistance in Surface Water. The Journal of Antibiotics. Springer Nature, New York, NY, USA, 12(11): 1586, (2023).

,Draft genome sequences of five Enterococcus faecium, two Enterococcus hirae, and one Enterococcus gallinarum from enviromental sources and chicken carcass rinsates. Isolates were selected for their resistance to the streptogramin antibiotic, Quinupristin-Dalfopristin and were all collected in the United States between 2001 and 2004. Antimicrobial resistance genes were identified conferring resistance to the macrolide-lincosamide-streptogramins, aminoglycosides, tetracycline, beta-lactams, and glycopeptides.,,

Background PCR amplification of bacterial 16S rRNA genes provides the most comprehensive and flexible means of sampling bacterial communities. Sequence analysis of these cloned fragments can provide a qualitative and quantitative insight of the microbial population under scrutiny although this approach is not suited to large-scale screenings. Other methods, such as denaturing gradient gel electrophoresis, heteroduplex or terminal restriction fragment analysis are rapid and therefore amenable to field-scale experiments. A very recent addition to these analytical tools is represented by microarray technology. Results Here we present our results using a Universal DNA Microarray approach as an analytical tool for bacterial discrimination. The proposed procedure is based on the properties of the DNA ligation reaction and requires the design of two probes specific for each target sequence. One oligo carries a fluorescent label and the other a unique sequence (cZipCode or complementary ZipCode) which identifies a ligation product. Ligated fragments, obtained in presence of a proper template (a PCR amplified fragment of the 16s rRNA gene) contain either the fluorescent label or the unique sequence and therefore are addressed to the location on the microarray where the ZipCode sequence has been spotted. Such an array is therefore "Universal" being unrelated to a specific molecular analysis. Here we present the design of probes specific for some groups of bacteria and their application to bacterial diagnostics. Conclusions The combined use of selective probes, ligation reaction and the Universal Array approach yielded an analytical procedure with a good power of discrimination among bacteria.

,Groundwater was collected by dead-end ultrafiltration and small-volume grab sampling from 138 wells in southwest Wisconsin across Grant, Iowa, and Lafayette Counties. Samples were collected to assess occurrence of antibiotic resistance genes in private wells and investigate their association with microbial source tracking markers. For ultrafiltration samples, microbes were backflushed, desiccated beef extract was added to the eluate, and samples were concentrated by polyethylene glycol precipitation; concentrate was frozen at -80 degrees Celcius (C). Small-volume grab samples were concentrated on 0.45-micron mixed cellulose ester filters, filters were eluted, and eluate was frozen at -80 degrees C following addition of beef extract. Nucleic acids were extracted from both sample types using a QIAcube and QIAamp DNA mini kit with viral lysis buffer (AVL) and carrier RNA (Qiagen). Nucleic acids were extracted from 280 microliter (µL) of sample concentrate and eluted into 140 µL AE Buffer (Qiagen). Nucleic acids were analyzed in duplicate using quantitative polymerase chain reaction (qPCR) on a Roche LightCycler 480 II using hydrolysis probes. Inhibition was assessed for every sample using Sketa DNA as inhibition control and mitigated by dilution with AE buffer as necessary. No-template negative controls were performed for all analysis steps: secondary concentration, nucleic acid extraction, and qPCR. For each assay with amplification in negative controls, the cycle of quantification (Cq) in unknown samples must be below the censoring threshold to be accepted as positive. Censoring thresholds were calculated as the mean Cq of negative controls - 3 standard deviations; censoring thresholds for each assay and sample type are reported in a separate file (Censoring thresholds.csv). Positive controls (bovine herpes virus vaccine) for extraction were included with each analysis batch and evaluated qualitatively. Positive controls were run in duplicate reactions for all targets and had to be within 0.5 cycles of the expected Cq. Data are expressed as genomic copies per liter of groundwater sampled. Dataset consists of 1 spreadsheet file (qPCR data results.csv). Variables in this file are described in the included data dictionary.,

Dataset for a year long watershed study in southwestern Ohio to analyze antimicrobial resistant bacteria (E. coli, Enterococcus, and Salmonella) in surface waters and wastewater treatment plant effluent.