Antimicrobial-specific response from resistance gene carriers studied in a natural, highly diverse microbiome

被引:21
作者
Wicaksono, Wisnu Adi [1 ]
Kusstatscher, Peter [1 ]
Erschen, Sabine [1 ]
Reisenhofer-Graber, Tamara [1 ]
Grube, Martin [2 ]
Cernava, Tomislav [1 ]
Berg, Gabriele [1 ]
机构
[1] Graz Univ Technol, Inst Environm Biotechnol, Graz, Austria
[2] Karl Franzens Univ Graz, Inst Biol, Graz, Austria
基金
奥地利科学基金会;
关键词
Lichen microbiota; Peltigera polydactylon; Antimicrobial resistance; Metagenomic mining; Genome recovery; GLYPHOSATE-CONTAINING HERBICIDE; ANTIBIOTIC-RESISTANCE; LICHEN MICROBIOTA; COLISTIN; RESISTOME; ALIGNMENT; EXPOSURE; IMPACT;
D O I
10.1186/s40168-020-00982-y
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Background Antimicrobial resistance (AMR) is a major threat to public health. Microorganisms equipped with AMR genes are suggested to have partially emerged from natural habitats; however, this hypothesis remains inconclusive so far. To understand the consequences of the introduction of exogenic antimicrobials into natural environments, we exposed lichen thalli of Peltigera polydactylon, which represent defined, highly diverse miniature ecosystems, to clinical (colistin, tetracycline), and non-clinical (glyphosate, alkylpyrazine) antimicrobials. We studied microbiome responses by analysing DNA- and RNA-based amplicon libraries and metagenomic datasets. Results The analyzed samples consisted of the thallus-forming fungus that is associated with cyanobacteria as well as other diverse and abundant bacterial communities (up to 10(8) 16S rRNA gene copies ng(-1) DNA) dominated by Alphaproteobacteria and Bacteroidetes. Moreover, the natural resistome of this meta-community encompassed 728 AMR genes spanning 30 antimicrobial classes. Following 10 days of exposure to the selected antimicrobials at four different concentrations (full therapeutic dosage and a gradient of sub-therapeutic dosages), we observed statistically significant, antimicrobial-specific shifts in the structure and function but not in bacterial abundances within the microbiota. We observed a relatively lower response after the exposure to the non-clinical compared to the clinical antimicrobial compounds. Furthermore, we observed specific bacterial responders, e.g., Pseudomonas and Burkholderia to clinical antimicrobials. Interestingly, the main positive responders naturally occur in low proportions in the lichen holobiont. Moreover, metagenomic recovery of the responders' genomes suggested that they are all naturally equipped with specific genetic repertoires that allow them to thrive and bloom when exposed to antimicrobials. Of the responders, Sphingomonas, Pseudomonas, and Methylobacterium showed the highest potential. Conclusions Antimicrobial exposure resulted in a microbial dysbiosis due to a bloom of naturally low abundant taxa (positive responders) with specific AMR features. Overall, this study provides mechanistic insights into community-level responses of a native microbiota to antimicrobials and suggests novel strategies for AMR prediction and management.
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页数:14
相关论文
共 80 条
[1]   LICHENS ARE MORE IMPORTANT THAN YOU THINK [J].
AHMADJIAN, V .
BIOSCIENCE, 1995, 45 (03) :124-124
[2]   DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data [J].
Arango-Argoty, Gustavo ;
Garner, Emily ;
Prudent, Amy ;
Heath, Lenwood S. ;
Vikesland, Peter ;
Zhang, Liqing .
MICROBIOME, 2018, 6
[3]   Environmental factors influencing the development and spread of antibiotic resistance [J].
Bengtsson-Palme, Johan ;
Kristiansson, Erik ;
Larsson, D. G. Joakim .
FEMS MICROBIOLOGY REVIEWS, 2018, 42 (01) :68-80
[4]   Colistin, mechanisms and prevalence of resistance [J].
Bialvaei, Abed Zahedi ;
Kafil, Hossein Samadi .
CURRENT MEDICAL RESEARCH AND OPINION, 2015, 31 (04) :707-721
[5]   Trimmomatic: a flexible trimmer for Illumina sequence data [J].
Bolger, Anthony M. ;
Lohse, Marc ;
Usadel, Bjoern .
BIOINFORMATICS, 2014, 30 (15) :2114-2120
[6]   Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 [J].
Bolyen, Evan ;
Rideout, Jai Ram ;
Dillon, Matthew R. ;
Bokulich, NicholasA. ;
Abnet, Christian C. ;
Al-Ghalith, Gabriel A. ;
Alexander, Harriet ;
Alm, Eric J. ;
Arumugam, Manimozhiyan ;
Asnicar, Francesco ;
Bai, Yang ;
Bisanz, Jordan E. ;
Bittinger, Kyle ;
Brejnrod, Asker ;
Brislawn, Colin J. ;
Brown, C. Titus ;
Callahan, Benjamin J. ;
Caraballo-Rodriguez, Andres Mauricio ;
Chase, John ;
Cope, Emily K. ;
Da Silva, Ricardo ;
Diener, Christian ;
Dorrestein, Pieter C. ;
Douglas, Gavin M. ;
Durall, Daniel M. ;
Duvallet, Claire ;
Edwardson, Christian F. ;
Ernst, Madeleine ;
Estaki, Mehrbod ;
Fouquier, Jennifer ;
Gauglitz, Julia M. ;
Gibbons, Sean M. ;
Gibson, Deanna L. ;
Gonzalez, Antonio ;
Gorlick, Kestrel ;
Guo, Jiarong ;
Hillmann, Benjamin ;
Holmes, Susan ;
Holste, Hannes ;
Huttenhower, Curtis ;
Huttley, Gavin A. ;
Janssen, Stefan ;
Jarmusch, Alan K. ;
Jiang, Lingjing ;
Kaehler, Benjamin D. ;
Bin Kang, Kyo ;
Keefe, Christopher R. ;
Keim, Paul ;
Kelley, Scott T. ;
Knights, Dan .
NATURE BIOTECHNOLOGY, 2019, 37 (08) :852-857
[7]   Fast and sensitive protein alignment using DIAMOND [J].
Buchfink, Benjamin ;
Xie, Chao ;
Huson, Daniel H. .
NATURE METHODS, 2015, 12 (01) :59-60
[8]   Antibiotic Persistence as a Metabolic Adaptation: Stress, Metabolism, the Host, and New Directions [J].
Cabral, Damien J. ;
Wurster, Jenna I. ;
Belenky, Peter .
PHARMACEUTICALS, 2018, 11 (01)
[9]   Antibiotic resistance genes in treated wastewater and in the receiving water bodies: A pan-European survey of urban settings [J].
Cacace, Damiano ;
Fatta-Kassinos, Despo ;
Manaia, Celia M. ;
Cytryn, Eddie ;
Kreuzinger, Norbert ;
Rizzo, Luigi ;
Karaolia, Popi ;
Schwartz, Thomas ;
Alexander, Johannes ;
Merlin, Christophe ;
Garelick, Hemda ;
Schmitt, Heike ;
de Vries, Daisy ;
Schwermer, Carsten U. ;
Meric, Sureyya ;
Ozkal, Can Burak ;
Pons, Marie-Noelle ;
Kneis, David ;
Berendonk, Thomas U. .
WATER RESEARCH, 2019, 162 :320-330
[10]  
Callahan BJ, 2016, NAT METHODS, V13, P581, DOI [10.1038/NMETH.3869, 10.1038/nmeth.3869]