Antibiotic Resistance Gene Detection in the Microbiome Context

Abstract

Within the past decade, microbiologists have moved from detecting single antibiotic resistance genes (ARGs) to detecting all known resistance genes within a sample due to advances in next generation sequencing. This has provided a wealth of data on the variation and relative abundances of ARGs present in a total bacterial population. However, to use these data in terms of therapy or risk to patients, they must be analyzed in the context of the background microbiome. Using a quantitative PCR ARG chip and 16S rRNA amplicon sequencing, we have sought to identify the ARGs and bacteria present in a fecal sample of a healthy adult using genomic tools. Of the 42 ARGs detected, 12 fitted into the ResCon1 category of ARGs: cfxA, cphA, bacA, sul3, aadE, bla, aphA1, aphA3, aph(2')-Id, aacA/aphd, catA1, and vanC. Therefore, we describe these 12 genes as the core resistome of this person's fecal microbiome and the remaining 30 ARGs as descriptors of the microbial population within the fecal microbiome. The dominant phyla and genera agree with those previously detected in the greatest abundances in fecal samples of healthy humans. The majority of the ARGs detected were associated with the presence of specific bacterial taxa, which were confirmed using microbiome analysis. We acknowledge the limitations of the data in the context of the limited sample set. However, the principle of combining qPCR and microbiome analysis was shown to be helpful to identify the association of the ARGs with specific taxa.

Human disease-related pathways increasing with opportunistic pathogens bloom in intestinal microbiome after vancomycin exposure and restoration with fecal transplantation by simulator of the human intestinal microbial ecosystem (SHIME)

“…All of the findings, as mentioned above, were also linked with positive correlations between the abundances of these opportunistic pathogens and human disease-related pathways in our study. Some previous studies have also reported multiple ARGs resided in these types of pathogenic bacteria [49,50], that maybe one of the critical reason for ARGs were more abundantly present in control samples.…”

Culture-enriched human gut microbiomes reveal core and accessory resistance genes

“…Exposure to oxygen may have affected obligate anaerobic bacteria during sample collection and culture preparation. The genome of these bacteria could then have been present in the fecal samples, but the microorganism may not have been able to grow in culture [33]. The composition of culture media would also affect the community composition.…”

“…The composition of culture media would also affect the community composition. For example, MCDA broth did not include short-chain fatty acids, which are required for the grown of some anaerobic bacteria like Roseburia [33]. In addition, some bacterial species may have faster growth rates in the selected culture conditions, which may favor them in the obtained CEM compared to bacteria with slower growth.…”

Utility of DNA Next-Generation Sequencing and Expanded Quantitative Urine Culture in Diagnosis and Management of Chronic or Persistent Lower Urinary Tract Symptoms

“…Our knowledge of the phenotypic outcomes of ARG expression and interactions in complex bacterial systems, such as the gut or urinary microbiome, is limited. This likely precludes optimal or safe clinical application of the generated results (19). More specifically, there is a need for further investigation into the characterization of the baseline bacteria and ARGs (both chromosomal and mobile elements) present in the resident microbiome.…”

“…More specifically, there is a need for further investigation into the characterization of the baseline bacteria and ARGs (both chromosomal and mobile elements) present in the resident microbiome. This would allow us to better understand how ARGs are transferred bidirectionally between the commensal gut microflora and pathogenic species both naturally and under the lens of selective antibiotic pressure (19). Consequently, the "resistome" data obtained through NGS can be used to infer or predict phenotypic antibiotic resistance patterns.…”