An Analysis of Antimicrobial Resistance of Clinical Pathogens from Historical Samples for Six Countries

Karen Li,James Peng,Thomas Deng,Zuyi Huang,Dagmar Daniel,Qian Jia,Joanna Zheng

doi:10.3390/pr7120964

Karen Li, James Peng + Show 5 more

Open Access

https://doi.org/10.3390/pr7120964

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Journal: Processes	Publication Date: Dec 17, 2019
Citations: 8	License type: CC BY 4.0

Affiliation: Villanova University, Immaculata University

Abstract

The spread of antimicrobial resistance pathogens in humans has increasingly become an issue that threatens public health. While the NCBI Pathogen Detection Isolates Browser (NPDIB) database has been collecting clinical isolate samples over time for various countries, few studies have been done to identify genes and pathogens responsible for the antimicrobial resistance in clinical settings. This study conducted the first multivariate statistical analysis of the high-dimensional historical data from the NPDIB database for six different countries from majorly inhabited landmasses, including Australia, Brazil, China, South Africa, the UK, and the US. The similarities among different countries in terms of genes and pathogens were investigated to understand the potential avenues for antimicrobial-resistance gene spreading. The genes and pathogens that were closely involved in antimicrobial resistance were further studied temporally by plotting time profiles of their frequency to evaluate the trend of antimicrobial resistance. It was found that several of these significant genes (i.e., aph(3″)-Ib, aph(6)-Id, blaTEM-1, and qacEdelta1) are shared among all six countries studied. Based on the time profiles, a large number of genes and pathogens showed an increasing occurrence. The most shared pathogens responsible for carrying the most important genes in the six countries in the clinical setting were Acinetobacter baumannii, E. coli and Shigella, Klebsiella pneumoniae and Salmonella enterica. South Africa carried the least similar antimicrobial genes to the other countries in clinical isolates.

Highlights

In a post-antibiotic era, an increasing prevalence of antimicrobial resistance, often abbreviated as “AMR”, has been identified as a critical health hazard on a global scale
principal component analysis (PCA) was performed on this data set on both pathogens and genes to represent the multidimensional data into a two-dimensional plot
The occurrence profiles over years for those important genes and pathogens were further used to study the trend of antimicrobial resistance

Summary

Introduction

In a post-antibiotic era, an increasing prevalence of antimicrobial resistance, often abbreviated as “AMR”, has been identified as a critical health hazard on a global scale. An evaluation of 1606 inpatient and outpatient samples from an African country in 2014 revealed that pathogenic resistance to older established antibiotics is rising; if unchecked, the magnitude of mortality attributable to AMR may climb to 10 million lives by 2050 [1]. The common cold, for instance, is incorrectly believed by a third of people to be treatable with antibiotics and is the Processes 2019, 7, 964; doi:10.3390/pr7120964 www.mdpi.com/journal/processes. Overexposure to antimicrobials enables pathogens to gain adaptive survivability via mutation or by acquiring resistance genes through horizontal transfer, in which plasmids containing genetic material disperse and pass between different species of bacteria

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