scholarly journals Secrets of the Hospital Underbelly: Patterns of Abundance of Antimicrobial Resistance Genes in Hospital Wastewater Vary by Specific Antimicrobial and Bacterial Family

2021 ◽  
Vol 12 ◽  
Author(s):  
Meghan R. Perry ◽  
Hannah C. Lepper ◽  
Luke McNally ◽  
Bryan A. Wee ◽  
Patrick Munk ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Full Range ◽  
Procrustes Analysis ◽  
Clinical Isolates ◽  
Clinical Activity ◽  
Hospital Wastewater ◽  
Antimicrobial Usage ◽  
Hospital Site ◽  
Antimicrobial Resistance Genes

Background: Hospital wastewater is a major source of antimicrobial resistance (AMR) outflow into the environment. This study uses metagenomics to study how hospital clinical activity impacts antimicrobial resistance genes (ARGs) abundances in hospital wastewater.Methods: Sewage was collected over a 24-h period from multiple wastewater collection points (CPs) representing different specialties within a tertiary hospital site and simultaneously from community sewage works. High throughput shotgun sequencing was performed using Illumina HiSeq4000. ARG abundances were correlated to hospital antimicrobial usage (AMU), data on clinical activity and resistance prevalence in clinical isolates.Results: Microbiota and ARG composition varied between CPs and overall ARG abundance was higher in hospital wastewater than in community influent. ARG and microbiota compositions were correlated (Procrustes analysis, p=0.014). Total antimicrobial usage was not associated with higher ARG abundance in wastewater. However, there was a small positive association between resistance genes and antimicrobial usage matched to ARG phenotype (IRR 1.11, CI 1.06–1.16, p<0.001). Furthermore, analyzing carbapenem and vancomycin resistance separately indicated that counts of ARGs to these antimicrobials were positively associated with their increased usage [carbapenem rate ratio (RR) 1.91, 95% CI 1.01–3.72, p=0.07, and vancomycin RR 10.25, CI 2.32–49.10, p<0.01]. Overall, ARG abundance within hospital wastewater did not reflect resistance patterns in clinical isolates from concurrent hospital inpatients. However, for clinical isolates of the family Enterococcaceae and Staphylococcaceae, there was a positive relationship with wastewater ARG abundance [odds ratio (OR) 1.62, CI 1.33–2.00, p<0.001, and OR 1.65, CI 1.21–2.30, p=0.006 respectively].Conclusion: We found that the relationship between hospital wastewater ARGs and antimicrobial usage or clinical isolate resistance varies by specific antimicrobial and bacterial family studied. One explanation, we consider is that relationships observed from multiple departments within a single hospital site will be detectable only for ARGs against parenteral antimicrobials uniquely used in the hospital setting. Our work highlights that using metagenomics to identify the full range of ARGs in hospital wastewater is a useful surveillance tool to monitor hospital ARG carriage and outflow and guide environmental policy on AMR.

Expression of virulence and antimicrobial resistance genes among Escherichia coli clinical isolates from blood and stool samples

2019 ◽  
Vol 30 (3) ◽  
pp. 137-141
Author(s):  
Ali Rajabi ◽  
Hossein Rajabi-vardanjani ◽  
Kobra Rastiyani ◽  
Mais Emad Ahmed ◽  
Seyede Amene Mirforughi ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Clinical Isolates ◽  
Antimicrobial Resistance Genes ◽  
Stool Samples

scholarly journals Molecular epidemiology and antimicrobial resistance features of Acinetobacter baumannii clinical isolates from Pakistan

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Nabil Karah ◽  
Fizza Khalid ◽  
Sun Nyunt Wai ◽  
Bernt Eric Uhlin ◽  
Irfan Ahmad
Keyword(s):  
Antimicrobial Resistance ◽  
Molecular Epidemiology ◽  
Acinetobacter Baumannii ◽  
Resistance Genes ◽  
Antimicrobial Agents ◽  
Opportunistic Pathogen ◽  
Clinical Isolates ◽  
Diffusion Method ◽  
Agar Disc ◽  
Antimicrobial Resistance Genes

Abstract Background Acinetobacter baumannii is a Gram-negative opportunistic pathogen with a notorious reputation of being resistant to antimicrobial agents. The capability of A. baumannii to persist and disseminate between healthcare settings has raised a major concern worldwide. Methods Our study investigated the antibiotic resistance features and molecular epidemiology of 52 clinical isolates of A. baumannii collected in Pakistan between 2013 and 2015. Antimicrobial susceptibility patterns were determined by the agar disc diffusion method. Comparative sequence analyses of the ampC and blaOXA-51-like alleles were used to assign the isolates into clusters. The whole genomes of 25 representative isolates were sequenced using the MiSeq Desktop Sequencer. Free online applications were used to determine the phylogeny of genomic sequences, retrieve the multilocus sequence types (ST), and detect acquired antimicrobial resistance genes. Results Overall, the isolates were grouped into 7 clusters and 3 sporadic isolates. The largest cluster, Ab-Pak-cluster-1 (blaOXA-66 and ISAba1-ampC-19) included 24 isolates, belonged to ST2 and International clone (IC) II, and was distributed between two geographical far-off cities, Lahore and Peshawar. Ab-Pak-clusters-2 (blaOXA-66, ISAba1-ampC-2), and -3 (blaOXA-66, ISAba1-ampC-20) and the individual isolate Ab-Pak-Lah-01 (ISAba1-blaOXA-66, ISAba1-ampC-2) were also assigned to ST2 and IC II. On the other hand, Ab-Pak-clusters-4 (blaOXA-69, ampC-1), -5 (blaOXA-69, ISAba1-ampC-78), and -6A (blaOXA-371, ISAba1-ampC-3) belonged to ST1, while Ab-Pak-cluster-6B (blaOXA-371, ISAba1-ampC-8) belonged to ST1106, with both ST1 and ST1106 being members of IC I. Five isolates belonged to Ab-Pak-cluster-7 (blaOXA-65, ampC-43). This cluster corresponded to ST158, showed a well-delineated position on the genomic phylogenetic tree, and was equipped with several antimicrobial resistance genes including blaOXA-23 and blaGES-11. Conclusions Our study detected the occurrence of 7 clusters of A. baumannii in Pakistan. Altogether, 6/7 of the clusters and 45/52 (86.5%) of the isolates belonged to IC I (n = 9) or II (n = 36), making Pakistan no exception to the global domination of these two clones. The onset of ST158 in Pakistan marked a geographical dispersal of this clone beyond the Middle East and brought up the need for a detailed characterization.

scholarly journals A review of information resources on antimicrobial resistance genes

2019 ◽  
Vol 100 (3) ◽  
pp. 457-463
Author(s):  
A G Vinogradova ◽  
A Yu Kuzmenkov
Keyword(s):  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Full Range ◽  
Point Mutations ◽  
Chemical Compounds ◽  
Amino Acid Sequences ◽  
Information Resources ◽  
Limited Information ◽  
Information Updating ◽  
Antimicrobial Resistance Genes

Interest in the issues of antibiotic resistance control and monitoring remains actual during the past decades. A significant number of findings confirm the ever-growing ratio of antimicrobial-resistant microorganisms. The article describes the information resources including data on antimicrobial resistance genes. Efficient monitoring and timely detection of changes in this trend are possible provided that the large volume of information, including the range of the genes characterizing resistance to chemical compounds and medicines, is obtained. Using purpose-built databases describing the nucleotide and amino acid sequences that define antimicrobial resistance is particularly important. Moreover, the databases include data on point mutations in the genome of the microorganisms associated with antimicrobial resistance development. The first developed databases contained the limited information on genetic determinants of resistance. However, modern databases are more than ever tended to a full range display of information on various genes of resistance to antimicrobial medicines and chemical compounds. The approach provides meaningful data supplemented by graphic imaging of results in most cases. Access to a significant part of resources is free of charge and allows saving the final results that considerably simplifies communicating and improves interaction between researchers. A specific feature is continuous information updating and manual curation that provides better systematization of the available data.

scholarly journals Comparison of Antimicrobial Resistance and Pan-Genome of Clinical and Non-Clinical Enterococcus cecorum from Poultry Using Whole-Genome Sequencing

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 686
Author(s):  
Poonam Sharma ◽  
Sushim K. Gupta ◽  
John B. Barrett ◽  
Lari M. Hiott ◽  
Tiffanie A. Woodley ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Resistance Genes ◽  
Gene Families ◽  
Clinical Isolates ◽  
Whole Genome ◽  
Pan Genome ◽  
Antimicrobial Resistance Genes ◽  
Enterococcus Cecorum

Enterococcus cecorum is an emerging avian pathogen, particularly in chickens, but can be found in both diseased (clinical) and healthy (non-clinical) poultry. To better define differences between E. cecorum from the two groups, whole-genome sequencing (WGS) was used to identify and compare antimicrobial resistance genes as well as the pan-genome among the isolates. Eighteen strains selected from our previous study were subjected to WGS using Illumina MiSeq and comparatively analyzed. Assembled contigs were analyzed for resistance genes using ARG-ANNOT. Resistance to erythromycin was mediated by ermB, ermG, and mefA, in clinical isolates and ermB and mefA, in non-clinical isolates. Lincomycin resistance genes were identified as linB, lnuB, lnuC, and lnuD with lnuD found only in non-clinical E. cecorum; however, lnuB and linB were found in only one clinical isolate. For both groups of isolates, kanamycin resistance was mediated by aph3-III, while tetracycline resistance was conferred by tetM, tetO, and tetL. No mutations or known resistance genes were found for isolates resistant to either linezolid or chloramphenicol, suggesting possible new mechanisms of resistance to these drugs. A comparison of WGS results confirmed that non-clinical isolates contained more resistance genes than clinical isolates. The pan-genome of clinical and non-clinical isolates resulted in 3651 and 4950 gene families, respectively, whereas the core gene sets were comprised of 1559 and 1534 gene families in clinical and non-clinical isolates, respectively. Unique genes were found more frequently in non-clinical isolates than clinical. Phylogenetic analysis of the isolates and all the available complete and draft genomes showed no correlation between healthy and diseased poultry. Additional genomic comparison is required to elucidate genetic factors in E. cecorum that contribute to disease in poultry.

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