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. 2015 Nov;9(11):2490-502.
doi: 10.1038/ismej.2015.59. Epub 2015 Apr 28.

Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes

Bing Li  1 , Ying Yang  1 , Liping Ma  1 , Feng Ju  1 , Feng Guo  1 , James M Tiedje  2 , Tong Zhang  1
Affiliations

Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes

Bing Li et al. ISME J. 2015 Nov.

Abstract

A metagenomic approach and network analysis was used to investigate the wide-spectrum profiles of antibiotic resistance genes (ARGs) and their co-occurrence patterns in 50 samples from 10 typical environments. In total, 260 ARG subtypes belonging to 18 ARG types were detected with an abundance range of 5.4 × 10(-6)-2.2 × 10(-1) copy of ARG per copy of 16S-rRNA gene. The trend of the total ARG abundances in environments matched well with the levels of anthropogenic impacts on these environments. From the less impacted environments to the seriously impacted environments, the total ARG abundances increased up to three orders of magnitude, that is, from 3.2 × 10(-3) to 3.1 × 10(0) copy of ARG per copy of 16S-rRNA gene. The abundant ARGs were associated with aminoglycoside, bacitracin, β-lactam, chloramphenicol, macrolide-lincosamide-streptogramin, quinolone, sulphonamide and tetracycline, in agreement with the antibiotics extensively used in human medicine or veterinary medicine/promoters. The widespread occurrences and abundance variation trend of vancomycin resistance genes in different environments might imply the spread of vancomycin resistance genes because of the selective pressure resulting from vancomycin use. The simultaneous enrichment of 12 ARG types in adult chicken faeces suggests the coselection of multiple ARGs in this production system. Non-metric multidimensional scaling analysis revealed that samples belonging to the same environment generally possessed similar ARG compositions. Based on the co-occurrence pattern revealed by network analysis, tetM and aminoglycoside resistance protein, the hubs of the ARG network, are proposed to be indicators to quantitatively estimate the abundance of 23 other co-occurring ARG subtypes by power functions.

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Figures

Figure 1
Figure 1
(a) Broad-spectrum quantitative profile of the ARG types (copy of ARG per copy of 16S-rRNA gene) in 50 environmental samples. (b) Comparison of total ARG abundance in different environments.
Figure 2
Figure 2
Abundance of the 129 major (>1.0 × 10−3 ratio in at least one sample) ARG subtypes in the 50 environmental samples.
Figure 3
Figure 3
NMDS plot showing the ARG composition differences among the 50 environmental samples. The sample code in the NMDS plot and the corresponding sample names are listed in Supplementary Table S6.
Figure 4
Figure 4
(a) Venn diagram showing the number of shared and unique ARGs (at the reference sequences level) among human gut, piglet gut and chick gut. (b) Ternary plot showing the abundance comparison of the 99 shared ARGs in human gut, piglet gut and chick gut. The sum of the abundance for one specific ARG in these three types of gut was set as 100%. In the ternary plot, the percentage (%) of one specific ARG in each gut is equal to its corresponding abundance divided by the abundance sum of this ARG in the three types of gut. The symbol size indicated the abundance of the ARGs.
Figure 5
Figure 5
The network analysis revealing the co-occurrence patterns among ARG subtypes. The nodes were coloured according to modularity class. A connection represents a strong (Spearman's correlation coefficient ρ>0.8) and significant (P-value <0.01) correlation. The size of each node is proportional to the number of connections, that is, the degree.
Figure 6
Figure 6
The Network analysis revealing the co-occurrence patterns between ARG subtypes and microbial taxa. The nodes were coloured according to ARG types and genus. A connection represents a strong (Spearman's correlation coefficient ρ>0.8) and significant (P-value <0.01) correlation. The size of each node is proportional to the number of connections, that is, degree.
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