Investigating the ecology and diversity of antibiotic resistance plasmids belonging to different incompatibility groups using a bioinformatics approach

Plasmids are extra-chromosomal mobile DNA elements found in bacteria, and often serve

as vectors for accumulation and transfer of antibiotic resistance genes (ARGs) within

bacterial populations in different environments. Plasmid mediated transfer of ARGs has

accounted for the spread of antibiotic resistance within clinically relevant pathogenic

bacteria; this has escalated into a global health problem. Plasmids can be classified into

several groups based on their genetic incompatibility; meaning that plasmid with similar

replication (rep) genes cannot co-exist in the same bacterial cell. Even though ARGs have

been detected in samples from clinical and non-clinical environments, their occurrence and

distribution within plasmid incompatibility groups is not well understood. The study

sought to determine the extent of diversity and distribution of different plasmid

incompatibility (Inc/rep) groups that are harbouring ARGs in three different environments

broadly classified as natural, host-associated and managed. The hypothesis is stated that

antibiotic resistance plasmids of a certain Inc/rep group occur in certain environments with

higher frequency than other Inc/rep groups. In this research, DNA sequence data of nearly

all known natural plasmids from NCBI databases was used to determine the correlation

between plasmid Inc/rep group encoding antibiotic resistance and their source

environment. The majority of antibiotic resistance plasmids were found in host associated

environments particularly in human samples from clinical settings. Inc P was the only

group clearly associated with all the 3 broad categories of environments. Results suggest

no clear or notable variability on the correlation proportions of plasmids of other

incompatibility groups being associated with specific environmental sources. The major

limitation within this bioinformatics study was the plasmid sequences that were not

completely sequenced and those not clearly described in terms of source or isolation. It is

recommended that sequence data submission to public sequence databases be improved for

future bioinformatics studies that aim to look at evolution of plasmids encoding antibiotic

resistance.