Global ocean resistome revealed: Exploring antibiotic resistance gene abundance and distribution in TARA Oceans samples

Abstract

Background The rise of antibiotic resistance (AR) in clinical settings is of great concern. Therefore, the understanding of AR mechanisms, evolution, and global distribution is a priority for patient survival. Despite all efforts in the elucidation of AR mechanisms in clinical strains, little is known about its prevalence and evolution in environmental microorganisms. We used 293 metagenomic samples from the TARA Oceans project to detect and quantify environmental antibiotic resistance genes (ARGs) using machine learning tools. Results After manual curation of ARGs, their abundance and distribution in the global ocean are presented. Additionally, the potential of horizontal ARG transfer by plasmids and their correlation with environmental and geographical parameters is shown. A total of 99,205 environmental open reading frames (ORFs) were classified as 1 of 560 different ARGs conferring resistance to 26 antibiotic classes. We found 24,567 ORFs in putative plasmid sequences, suggesting the importance of mobile genetic elements in the dynamics of environmental ARG transmission. Moreover, 4,804 contigs with >=2 putative ARGs were found, including 2 plasmid-like contigs with 5 different ARGs, highlighting the potential presence of multi-resistant microorganisms in the natural ocean environment. Finally, we identified ARGs conferring resistance to some of the most relevant clinical antibiotics, revealing the presence of 15 ARGs similar to mobilized colistin resistance genes (mcr) with high abundance on polar biomes. Of these, 5 are assigned to Psychrobacter, a genus including opportunistic human pathogens. Conclusions This study uncovers the diversity and abundance of ARGs in the global ocean metagenome. Our results are available on Zenodo in MySQL database dump format, and all the code used for the analyses, including a Jupyter notebook js avaliable on Github. We also developed a dashboard web application (http://www.resistomedb.com) for data visualization.

Putative mobilized colistin resistance genes in the human gut microbiome

“…We identified a total of 22,746 ARGs (from 21 ARG classes and some unclassified) co-occurring with MCR-like sequences, being the most abundant classes the multidrug resistance (10,008 ARGs), beta-lactam (2,271 ARGs) and glycopeptide (2,261 ARGs) (Figure 4). However, several of those sequences on the multidrug class are efflux proteins, and as discussed in our previous study 9 , those are very hard to distinguish from other transporters that are not involved in antibiotic resistance. Tn402-like transposons).…”

“…Especially the latter is of high interest when studying ARGs since the genetic environment often shows the genetic mobility of ARGs, e.g., their location on genetic islands or plasmids 21 . Besides, it is also possible to investigate the presence of multi-drug-resistant (MDR) bacteria by detecting more than one ARG in the same bacterial genome or contig 9 when using the MAGs approach.…”

“…The resistome is defined as the collection of the antibiotic resistance genes (ARGs) in a single microorganism, or in a microbial community, and has been investigated in different environments, such as soils 8 or ocean 9 , and in host-associated microbiotas such as the animal 10 or human 11 gut. Understanding the human resistome in hospitalized and non-hospitalized populations are essential not only because the commensal microbiota can host and transfer ARGs from and to pathogenic bacteria by horizontal gene transfer (HGT) 12 , e.g., during an infection.…”

Techniques Used for Analyzing Microplastics, Antimicrobial Resistance and Microbial Community Composition: A Mini-Review

“…There are two aspects that should be considered in future analyses: (1) presence of non-culturable bacteria and (2) expression level of resistant genes in the bacterial communities. Integrating metagenomics and metatranscriptomics with machine-learning tools such as DeepARG, trained to find the existing and novel ARGs and MRGs, is a suitable option for this challenge ( Arango-Argoty et al, 2018 ; Cuadrat et al, 2020 ; Figure 2 ). Studying heterogeneous modulation of gene expression by HMs (and MPs/NPs) in a single bacterium is possible, but single-cell RNA sequencing (scRNA-seq) studies are still scarce due to differences from eukaryotic cells such as low mRNA content and lack of polyadenylation.…”

“…Abbreviations for technologies in alphabetical order: AAS, Atomic absorption spectroscopy; AFM, Atomic Force Microscopy; AFM-IR/Raman, Atomic force microscopy infrared/Raman; CLSM, Confocal Laser Scanning Microscopy; DeepARG, Deep learning model for antibiotic resistance genes; EM, Electron microscopy; FCM, Flow cytometry; PFGE, Pulsed-field gel electrophoresis; FM, Fluorescence microscopy; FTIR, Fourier transform infrared microscopy; GC-MS, Gas chromatography–mass spectrometry; GREACE, Genome Replication Engineering Assisted Continuous Evolution; HPLC, High-performance liquid chromatography; HT-qPCR, High-throughput qPCR; ICP-MS, Inductively coupled plasma mass spectrometry; LM, Light microscopy; MALDI-MSI/FISH, Matrix assisted laser desorption/ionization–Mass spectrometry imaging/Fluorescence in situ hybridization; microSPLIT, Microbial Split-Pool Ligation Transcriptomics; Py-GCToF, Pyrolysis–Gas Chromatography Time of Flight Mass Spectrometry; RT-PCR, Reverse transcription polymerase chain reaction; SEM, Scanning Electron Microscopy; UPLC, Ultra-performance liquid chromatography; UV-VIS, Ultraviolet–visible spectrophotometry; XRD, X-ray diffraction; WGS, Whole genome sequencing; 1D/2D-LC-MS/MS, One dimensional/Two dimensional online separation-liquid chromatography-tandem mass spectrometry; 2D-PAGE, Two-dimensional gel electrophoresis. References in numerical order: (1) = ( Zhang Y. et al, 2020 ), (2) = ( Imhof et al, 2016 ), (3) = ( Fu et al, 2020 ), (4) = ( Sullivan et al, 2020 ), (5) = ( Gimiliani et al, 2020 ), (6) = ( Kaile et al, 2020 ), (7) = ( Dussud et al, 2018 ), (8) = ( Hossain et al, 2019 , (9) = ( Li et al, 2018 ), (10) = ( Munier and Bendell, 2018 ), (11) = ( Bolívar-Subirats et al, 2021 ), (12) = ( Zhang et al, 2018 ), (13) = ( Yu et al, 2020b ), (14) = ( Pousti et al, 2019 ), (15) = ( Secchi et al, 2020 ), (16) = ( Leng et al, 2019 ), (17) = ( Meier et al, 2020 ), (18) = ( Pathak et al, 2020 ), (19) = ( Zhao Y. et al, 2019 ), (20) = ( Li X. et al, 2019 ), (21) = ( Qin et al, 2019 ), (22) = ( Cuadrat et al, 2020 ), (23) = ( Kuchina et al, 2020 ), (24) = ( Scheler et al, 2020 ), (25) = ( Bar et al, 2007 ), (26) = ( Hinzke et al, 2019 ), (27) = ( Li W. et al, 2019 ), (28) = ( Geier et al, 2020 ).…”