Back

Core Microbiota Drive the Prevalence of Extracellular Antibiotic Resistome in the Water Compartments

Yu, K.-F.; Li, P.; Huang, Y.; Yang, J.; Li, H.; Xie, Y.; Li, R.; Zhang, B.; He, Y.

2022-01-04 microbiology
10.1101/2022.01.04.474879 bioRxiv
Show abstract

Unlike intracellular chromosome, extracellular DNA (eDNA) may accelerate the spreading of antibiotic resistance genes (ARGs) through natural transformation, but one of the core issues regarding to the bacterial taxonomic characterization of eDNA in the complex water environments is largely unknown. Hence, Illumina Miseq sequencing was used to identify the genotype of eDNA from wastewater (WW), river water (RW) and stormwater (SW) runoff. High-throughput qPCR targeting 384 genes was implemented to detect extracellular ARGs (eARGs) and mobile genetic elements (eMGEs). We obtained 2,708,291 high quality sequences from 66 eDNA samples. The SW exhibited the significant higher Shannon Index. Subsequently, we identified 34 core bacteria sources of eDNA widely distributed in the three water compartments. Among which, Pseudomonas, Flavobacterium, Limnohabitans, Burkholderiaceae_unclassified, Methylotenera and Acinetobacter were the most prevalent. A total of 302 eARGs and eMGEs were detected, suggesting that eDNA is an important antibiotic resistance reservoir. Among the 127 shared genes of the three groups, 15 core resistance genes were filtered, including IS6100, sul1 NEW, intI1, ISPps1-pseud, aac3-Via, qacH_351 and ISSm2-Xanthob. The Procrustes analysis and Variance Partitioning Analysis (VPA) demonstrated that core bacteria and MGEs were significantly correlated with eARGs. These results suggested that the occurrence and changes of eARGs in the water compartments may be largely attributed to the core microbiota and eMGEs. SynopsisThe identification of bacterial taxa of extracellular DNA is the preliminary issue for the natural transformation of extracellular antibiotic resistome.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.