Historically, b-lactam antibiotics are the most widely used antibiotics in clinical settings around the world
( ( class="ltx_cite" data-bib-text="@article{Garau_2005,
doi = {10.1128/aac.49.7.2778-2784.2005},
url = {http://dx.doi.org/10.1128/aac.49.7.2778-2784.2005},
year = 2005,
...
author = {G. Garau and A. M. Di Guilmi and B. G. Hall},
title = {Structure-Based Phylogeny of the Metallo-~-Lactamases},
journal = {Antimicrobial Agents and Chemotherapy}
}" data-bib-key="Garau_2005" contenteditable="false">
Garau 2005,
class="ltx_cite" data-bib-text="@article{Van_Boeckel_2014,
doi = {10.1016/s1473-3099(14)70780-7},
url = {http://dx.doi.org/10.1016/s1473-3099(14)70780-7},
year = 2014,
...
author = {Thomas P Van Boeckel and Sumanth Gandra and Ashvin Ashok and Quentin Caudron and Bryan T Grenfell and Simon A Levin and Ramanan Laxminarayan},
title = {Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data},
journal = {The Lancet Infectious Diseases}
}" data-bib-key="Van_Boeckel_2014" contenteditable="false">
Boeckel 2014 ESAC 2009, ECDC 2012). The major cause of resistance to b-lactams antibiotics are bacterial enzymes called b-lactamases with the capacity to hydrolyze the molecular structure of the b-lactam antibiotic
( ( class="ltx_cite" data-bib-text="@article{Davies_1994,
doi = {10.1126/science.8153624},
url = {http://dx.doi.org/10.1126/science.8153624},
year = 1994,
...
author = {J Davies},
title = {Inactivation of antibiotics and the dissemination of resistance genes},
journal = {Science}
}" data-bib-key="Davies_1994" contenteditable="false">
Davies 1994). Many studies have assessed the presence and diversity of b-lactamases in clinical settings
( ( class="ltx_cite" data-bib-text="@article{Paterson_2005,
doi = {10.1128/cmr.18.4.657-686.2005},
url = {http://dx.doi.org/10.1128/cmr.18.4.657-686.2005},
year = 2005,
...
author = {D. L. Paterson and R. A. Bonomo},
title = {Extended-Spectrum ~-Lactamases: a Clinical Update},
journal = {Clinical Microbiology Reviews}
}" data-bib-key="Paterson_2005" contenteditable="false">
Paterson 2005,
class="ltx_cite" data-bib-text="@article{Rice_2001,
doi = {10.1378/chest.119.2_suppl.391s},
url = {http://dx.doi.org/10.1378/chest.119.2_suppl.391s},
year = 2001,
...
author = {Louis Rice},
title = {Evolution and Clinical Importance of Extended-Spectrum $\upbeta$-Lactamases},
journal = {Chest}
}" data-bib-key="Rice_2001" contenteditable="false">
Rice 2001,
class="ltx_cite" data-bib-text="@article{Ramphal_2006,
doi = {10.1086/500663},
url = {http://dx.doi.org/10.1086/500663},
year = 2006,
...
author = {R. Ramphal and P. G. A. $\greater$},
title = {Extended-Spectrum ~-Lactamases and Clinical Outcomes: Current Data},
journal = {Clinical Infectious Diseases}
}" data-bib-key="Ramphal_2006" contenteditable="false">
Ramphal 2006,
class="ltx_cite" data-bib-text="@article{Shahid_2014,
doi = {10.12816/0008130},
url = {http://dx.doi.org/10.12816/0008130},
year = 2014,
...
author = {Mohammad Shahid},
title = {Prevalence of {CTX} M Extended-Spectrum Beta-Lactamases in Clinical Gram-Negative Bacteria},
journal = {Bahrain Medical Bulletin}
}" data-bib-key="Shahid_2014" contenteditable="false">
Shahid 2014,
class="ltx_cite" data-bib-text="@article{Sullivan_2015,
doi = {10.4172/2161-0703.1000203},
url = {http://dx.doi.org/10.4172/2161-0703.1000203},
year = 2015,
...
author = {Rebecca Sullivan and David Schaus},
title = {Extended Spectrum Beta- Lactamases: A Minireview of Clinical Relevant Groups},
journal = {Journal of Medical Microbiology {\&} Diagnosis}
}" data-bib-key="Sullivan_2015" contenteditable="false">
Sullivan 2015), but no efforts have been put in assess b-lactamases in non-clinical settings.
Studies assessing the AR phenomenon in non-clinical environments usually are based on functional metagenomics studies, PCR reactions, MIC tests
( ( class="ltx_cite" data-bib-text="@article{Donato_2010,
doi = {10.1128/aem.01763-09},
url = {http://dx.doi.org/10.1128/aem.01763-09},
year = 2010,
...
author = {J. J. Donato and L. A. Moe and B. J. Converse and K. D. Smart and F. C. Berklein and P. S. McManus and J. Handelsman},
title = {Metagenomic Analysis of Apple Orchard Soil Reveals Antibiotic Resistance Genes Encoding Predicted Bifunctional Proteins},
journal = {Applied and Environmental Microbiology}
}" data-bib-key="Donato_2010" contenteditable="false">
Donato 2010,
class="ltx_cite" data-bib-text="@article{Bhullar_2012,
doi = {10.1371/journal.pone.0034953},
url = {http://dx.doi.org/10.1371/journal.pone.0034953},
year = 2012,
...
editor = {Ramy K. Aziz},
title = {Antibiotic Resistance Is Prevalent in an Isolated Cave Microbiome},
journal = {{PLoS} {ONE}}
}" data-bib-key="Bhullar_2012" contenteditable="false">
Bhullar 2012,
class="ltx_cite" data-bib-text="@article{Segawa_2012,
doi = {10.1111/1758-2229.12011},
url = {http://dx.doi.org/10.1111/1758-2229.12011},
year = 2012,
...
author = {Takahiro Segawa and Nozomu Takeuchi and Andres Rivera and Akinori Yamada and Yoshitaka Yoshimura and Gonzalo Barcaza and Kunio Shinbori and Hideaki Motoyama and Shiro Kohshima and Kazunari Ushida},
title = {Distribution of antibiotic resistance genes in glacier environments},
journal = {Environmental Microbiology Reports}
}" data-bib-key="Segawa_2012" contenteditable="false">
Segawa 2012,
2012, class="ltx_cite" data-bib-text="@article{Amos_2014,
doi = {10.1016/j.vetmic.2014.02.017},
url = {http://dx.doi.org/10.1016/j.vetmic.2014.02.017},
year = 2014,
...
author = {G.C.A. Amos and L. Zhang and P.M. Hawkey and W.H. Gaze and E.M. Wellington},
title = {Functional metagenomic analysis reveals rivers are a reservoir for diverse antibiotic resistance genes},
journal = {Veterinary Microbiology}
}" data-bib-key="Amos_2014" contenteditable="false">
Amos 2014,
class="ltx_cite" data-bib-text="@article{Forsberg_2014,
doi = {10.1038/nature13377},
url = {http://dx.doi.org/10.1038/nature13377},
year = 2014,
...
author = {Kevin J. Forsberg and Sanket Patel and Molly K. Gibson and Christian L. Lauber and Rob Knight and Noah Fierer and Gautam Dantas},
title = {Bacterial phylogeny structures soil resistomes across habitats},
journal = {Nature}
}" data-bib-key="Forsberg_2014" contenteditable="false">
Forsberg 2014,
class="ltx_cite" data-bib-text="@article{Su_2014,
doi = {10.1016/j.envint.2013.12.010},
url = {http://dx.doi.org/10.1016/j.envint.2013.12.010},
year = 2014,
...
author = {Jian Qiang Su and Bei Wei and Chun Yan Xu and Min Qiao and Yong Guan Zhu},
title = {Functional metagenomic characterization of antibiotic resistance genes in agricultural soils from China},
journal = {Environment International}
}" data-bib-key="Su_2014" contenteditable="false">
Su 2014,
2014, class="ltx_cite" data-bib-text="@article{Ma_2014,
doi = {10.1007/s00253-014-5511-3},
url = {http://dx.doi.org/10.1007/s00253-014-5511-3},
year = 2014,
...
author = {Liping Ma and Bing Li and Tong Zhang},
title = {Abundant rifampin resistance genes and significant correlations of antibiotic resistance genes and plasmids in various environments revealed by metagenomic analysis},
journal = {Appl Microbiol Biotechnol}
}" data-bib-key="Ma_2014" contenteditable="false">
Ma 2014), but in the last years, the use of metagenomic approaches
( ( class="ltx_cite" data-bib-text="@article{Yang_2013,
doi = {10.1007/s00248-013-0187-2},
url = {http://dx.doi.org/10.1007/s00248-013-0187-2},
year = 2013,
...
author = {Jing Yang and Chao Wang and Chang Shu and Li Liu and Jianing Geng and Songnian Hu and Jie Feng},
title = {Marine Sediment Bacteria Harbor Antibiotic Resistance Genes Highly Similar to Those Found in Human Pathogens},
journal = {Microb Ecol}
}" data-bib-key="Yang_2013" contenteditable="false">
Yang 2013,
2013, class="ltx_cite" data-bib-text="@article{Li_2015,
doi = {10.1038/ismej.2015.59},
url = {http://dx.doi.org/10.1038/ismej.2015.59},
year = 2015,
...
author = {Bing Li and Ying Yang and Liping Ma and Feng Ju and Feng Guo and James M Tiedje and Tong Zhang},
title = {Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes},
journal = {The {ISME} Journal}
}" data-bib-key="Li_2015" contenteditable="false">
Li 2015,
2015, class="ltx_cite" data-bib-text="@article{Fondi_2016,
doi = {10.1093/gbe/evw077},
url = {http://dx.doi.org/10.1093/gbe/evw077},
year = 2016,
...
author = {Marco Fondi and Antti Karkman and Manu V. Tamminen and Emanuele Bosi and Marko Virta and Renato Fani and Eric Alm and James O. McInerney},
title = {{\textquotedblleft}Every Gene Is Everywhere but the Environment Selects{\textquotedblright}: Global Geolocalization of Gene Sharing in Environmental Samples through Network Analysis},
journal = {Genome Biol Evol}
}" data-bib-key="Fondi_2016" contenteditable="false">
Fondi 2016) appear such as an interesting tool to assess ARGs in environmental samples. In this context, the focus on antibiotic resistance genes in natural environments should be considered, specially if we consider the evidence that these genes have a long evolutionary history in natural environments
( ( class="ltx_cite" data-bib-text="@article{Aminov_2009,
doi = {10.1111/j.1462-2920.2009.01972.x},
url = {http://dx.doi.org/10.1111/j.1462-2920.2009.01972.x},
year = 2009,
...
author = {Rustam I. Aminov},
title = {The role of antibiotics and antibiotic resistance in nature},
journal = {Environmental Microbiology}
}" data-bib-key="Aminov_2009" contenteditable="false">
Aminov 2009,
class="ltx_cite" data-bib-text="@article{Hall_2004,
doi = {10.1016/j.drup.2004.02.003},
url = {http://dx.doi.org/10.1016/j.drup.2004.02.003},
year = 2004,
...
title = {Evolution of the serine $\upbeta$-lactamases: past,
present and future},
journal = {Drug Resistance Updates}
}" data-bib-key="Hall_2004" contenteditable="false">
Hall 2004,
class="ltx_cite" data-bib-text="@article{Garau_2005,
doi = {10.1128/aac.49.7.2778-2784.2005},
url = {http://dx.doi.org/10.1128/aac.49.7.2778-2784.2005},
year = 2005,
...
author = {G. Garau and A. M. Di Guilmi and B. G. Hall},
title = {Structure-Based Phylogeny of the Metallo-~-Lactamases},
journal = {Antimicrobial Agents and Chemotherapy}
}" data-bib-key="Garau_2005" contenteditable="false">
Garau 2005). Thus, natural environments appear such as a reservoir of potential ARGs to pathogenic bacteria
( ( class="ltx_cite" data-bib-text="@article{Berglund_2015,
doi = {10.3402/iee.v5.28564},
url = {http://dx.doi.org/10.3402/iee.v5.28564},
year = 2015,
...
author = {Björn Berglund},
title = {Environmental dissemination of antibiotic resistance genes and correlation to anthropogenic contamination with antibiotics},
journal = {Infection Ecology {\&} Epidemiology}
}" data-bib-key="Berglund_2015" contenteditable="false">
Berglund 2015,
class="ltx_cite" data-bib-text="@article{Versluis_2015,
doi = {10.1038/srep11981},
url = {http://dx.doi.org/10.1038/srep11981},
year = 2015,
...
author = {Dennis Versluis and Marco Maria D'Andrea and Javier Ramiro Garcia and Milkha M. Leimena and Floor Hugenholtz and Jing Zhang and Ba{\c{s}}ak Öztürk and Lotta Nylund and Detmer Sipkema and Willem van Schaik and Willem M. de Vos and Michiel Kleerebezem and Hauke Smidt and Mark W.J. van Passel},
title = {Mining microbial metatranscriptomes for expression of antibiotic resistance genes under natural conditions},
journal = {Sci. Rep.}
}" data-bib-key="Versluis_2015" contenteditable="false">
Versluis 2015) that cannot be ignored.
Despite that in the last years more studies are focused on ARGs in the environment, the lack of studies on b-lactamases in different environments, avoid the understanding of important process with clinical implications such as b-lactamase gene transfer between environments and the impact of anthropogenic forces on both b-lactamase content and diversity in natural environments.
In this study we assess the presence and diversity of b-lacatamases in different environments through a wide metagenomic approach and network oriented analysis, providing important findings on the pool of b-lactamase genes in different environments.
Results
A effort to understand the scope of b-lactam resistance in the environment was performed through a global survey of b-lactamases present in 232 shotgun metagenomes. Metagenomic data sets were obtained from two important public repositories. These data sets account for 770 Gigabytes of information and more than 4.7 billion of metagenomic reads (details can be found in table S1); and are related to different environments such as agricultural soils, non-agricultural soils, glaciers, fresh water, oceans, human gut, cow feces, cow rumen and wastewater treatment plant environment. The metagenomic reads were compared to the EX-B database; a database that include more than 1500 b-lactamases (for details see material and methods). Metagenomic reads containing b-lactamases were identified by BLASTX based on their similarity to the EX-B database at the cutoff thresholds described in material and methods (i.e. percent of identity equal o higher than 50%, e-value equal or lower than
class="ltx_Math" contenteditable="false" data-equation="10^{-5}">\(10^{-5}\), and a bit score equal o higher than 30). Environmental b-lactamases can be diverse relative to clinically characterized b-lactamases ( ( class="ltx_cite" data-bib-text="@article{Forsberg_2014,
doi = {10.1038/nature13377},
url = {http://dx.doi.org/10.1038/nature13377},
year = 2014,
...
author = {Kevin J. Forsberg and Sanket Patel and Molly K. Gibson and Christian L. Lauber and Rob Knight and Noah Fierer and Gautam Dantas},
title = {Bacterial phylogeny structures soil resistomes across habitats},
journal = {Nature}
}" data-bib-key="Forsberg_2014" contenteditable="false">
Forsberg 2014), due to we chose a identity percent equal o higher than 50%. Reads matching b-lactamases were used to construct distance matrices and b-lactamase gene networks, which were used for downstream analysis.
Abundance and diversity of b-lactamases in the environment
The metagenomic analysis show that b-lactamases were present in each metagenome analyzed, ranged from 0.0003% (percent of reads assigned to b-lactamases from the total number of analyzed reads) in a ocean metagenome (accession number SRS582462) to 0.0335% in a human gut metagenome (accession number SRS056259). The abundance of b-lactamases per environment is in average a 0,01% in soils (n=80 metagenomes, including agricultural and non-agricultural soils), 0.0068% in glacier (n=11), 0.0046% in fresh water (n=11), 0.002% in ocean metagenomes (n=22), 0.0121 in human gut (n=63), 0.0049% in cow gut (n=27 metagenomes, including feces and rumen samples) and 0.0092% in wastewater treatment plant environment (n=19). Parameters of abundance and diversity of b-lactamases in the environment can be observed in Fig.1; thus, soil and glacier environments show the higher abundance of b-lactamases genes, followed by fresh water and wastewater environments (Fig. 1a). Is important indicate that some b-lactamase genes can present different variants, i.e. blaOXA gen present 257 different variants in the EX-B database, but all the variants hits obtained in a given sample/environment are assigned to blaOXA gene, which explains the differences between the number of b-lactamases in the EX-B database and the number of b-lactamase genes in the richness graph. Diversity indexes show that the XXX, YYYY and ZZZ are the most b-lactamase diverse environments (Fig 1, B and C).
The presence of b-lactamases, grouped according molecular classes, show differences according environment (Fig. 2); thus, class A b-lactamases are dominant in non-agricultural soils, cow (including feces and rumen metagenomes) and human gut environments (more than 70% in each case), class B b-lactamases are dominant in agricultural soils, fresh water, oceans and wastewater treatment plant environments (between 40 to 60% in each environment), class C b-lactamases are more represented in agricultural soils and fresh water environments (20% approx. in each environment) and class D b-lactamases being more abundant in fresh water, wastewater treatment plant and glacier environments (from 15% to 30% approx.). A more detailed picture of b-lactamases in the environment is presented in table 1; where b-lactamase genes were analyzed according their occurrence in a particular environment throughout an indicator species analysis
( ( class="ltx_cite" data-bib-text="@article{Dufrene_1997,
doi = {10.2307/2963459},
url = {http://dx.doi.org/10.2307/2963459},
year = 1997,
...
author = {Marc Dufrene and Pierre Legendre},
title = {Species Assemblages and Indicator Species: The Need for a Flexible Asymmetrical Approach},
journal = {Ecological Monographs}
}" data-bib-key="Dufrene_1997" contenteditable="false">
Dufrene 1997). According these results, 16 b-lactamase genes show a high faithfulness of occurrence in non-agricultural soils, other 16 b-lactamase genes show a faithfulness of occurrence in agricultural soils and 14 b-laxctamase genes show a higher probability to be present in the wastewater treatment plant environment. Only four b-lactamases (blaEBR, CfxA, HGI and mecA) were found to be highly present in the human gut environment (p<0.005) instead of other analyzed environments. Interestingly, when the environments are grouped according level of anthropogenic impact (anthropogenic impact level 1= wastewater treatment plant; level 2= human gut; level 3= agricultural soils, fresh water, oceans and cow gut; level 4= non-agricultural soils and glaciers), the less impacted environments show a high level of faithfulness of occurrence of b-lactamase genes (50 genes for anthropogenic impact level 3 and 4) than the observed in the more anthropogenic impacted environments, where only 13 b-lactamase genes show faithfulness of occurrence.
b-lactamase hits obtained by BLAST were used to construct metagenome distance matrices and b-lactamase gene networks. When all the b-lactamase hits were used to construct the gene network (Fig. 3), different cluster were clearly observable. The network analysis indicate that each clusters harbor metagenomes almost exclusively related to a given environment; thus, the clusters represent human gut, cow gut and wastewater treatment plant environments. Other two identified clusters harbored metagenomes related to different environments; one of those cluster included mainly metagenomes of agricultural soils, glaciers and fresh water, and the other cluster include non-agricultural soil metagenomes together with some glacier and fresh water metagenomes. Cluster analysis performed on b-lactamase gene networks, indicated that samples from the same environment are more tightly connected than samples from different environments (Table S2). This is clearly visible in Fig. 3, where nodes from a given environment (soils, human gut, cow gut and wastewater treatment plant environment) show more conections between them than the number of conections with nodes related to other environments. Explicar asortividad, clustering coeficient, y algun otro..............
In order to test if sample geography influences on b-lactamase gene content, nodes present in Fig. 3 were presented according their geographic origin (Fig. 4). The obtained results indicated that geography is not correlated with both b-lactamase gene content and diversity.
The BLAST analysis performed on metagenomic reads indicate the presence of b-lactamase genes in different environments, but also it indicates how close are the metagenomic reads related to the reference database. To elucidate it, we choose four clinically important b-lactamases such as blaCTX-M, blaGES, blaOXA and blaTEM and the hits obtained by BLAST were grouped according percent of identity in five groupes (identities from 50 to 59, 60 to 69, 70 to 79, 80 to 89 and 90 to 100 percent) (Fig. 5). The results indicate that in each case, most of the hits show a low percent of identity; thus, for blaOXA gene, 40% of the hits or more show a percent of identity between 50 to 59 percent. In the case of blaGES, 40% or more hits show a percent of identity between 50 to 59 percent, and for blaCTX-M and blaTEM, close to 40% of the hits show a percent of identity of 50 to 59 percent. In some cases, a higher percent of identity can be observed for most of the hits in a given environment (blaCTX-M gene in human and bovine environment, blaTEM in ocean, human gut or bovine rumen) but in those cases, the total number of hits obtained was very low. Higher percent of identity (between 90 to 100%) were only detected in some specific cases; thus, blaCTX-M gene shows hits with the high percent of identity in samples obtained from human gut. hits with high percent of identity to blaGES gene were found only in wastewater treatment plant environment; in relation to blaOXA, hits with higher percent of identity to our database were obtained from human gut, wastewater treatment plant environment, fresh water and glacier metagenomes and finally, blaTEM hits that shows high percent of identity to our EX-B database were obtained from human gut, wastewater treatment plant environment, oceans, fresh water, glacier and agricultural soil metagenomes.
