Antibiotic resistome from the One-Health perspective: understanding and controlling antimicrobial resistance transmission
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Abstract The concept of the antibiotic resistome was introduced just over a decade ago, and since then, active resistome studies have been conducted. In the present study, we describe the previously established concept of the resistome, which encompasses all types of antibiotic resistance genes (ARGs), and the important findings from each One-Health sector considering this concept, thereby emphasizing the significance of the One-Health approach in understanding ARG transmission. Cutting-edge research methodologies are essential for deciphering the complex resistome structure in the microbiomes of humans, animals, and the environment. Based on the recent achievements of resistome studies in multiple One-Health sectors, future directions for resistome research have been suggested to improve the understanding and control of ARG transmission: (1) ranking the critical ARGs and their hosts; (2) understanding ARG transmission at the interfaces of One-Health sectors; (3) identifying selective pressures affecting the emergence, transmission, and evolution of ARGs; and (4) elucidating the mechanisms that allow an organism to overcome taxonomic barriers in ARG transmission.Keywords:
Resistome
Antimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of “resistomes” (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in the resulting phenotype. Here, we describe a novel strategy that combines targeted metagenomics using last generation in-solution capture platforms, with novel bioinformatics tools to establish a standardized framework that allows both quantitative and qualitative analyses of resistomes. We developed ResCap, a targeted sequence capture platform based on SeqCapEZ (NimbleGene) technology, which includes probes for 8667 canonical resistance genes (7963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), and 2517 relaxase genes (plasmid markers) and 78,600 genes homologous to the previous identified targets (47,806 for antibiotics and 30,794 for biocides or metals). Its performance was compared with metagenomic shotgun sequencing (MSS) for 17 fecal samples (9 humans, 8 swine). ResCap significantly improves MSS to detect “gene abundance” (from 2.0 to 83.2%) and “gene diversity” (26 versus 14.9 genes unequivocally detected per sample per million of reads; the number of reads unequivocally mapped increasing up to 300-fold by using ResCap), which were calculated using novel bioinformatic tools. ResCap also facilitated the analysis of novel genes potentially involved in the resistance to antibiotics, metals, biocides, or any combination thereof. ResCap, the first targeted sequence capture, specifically developed to analyze resistomes, greatly enhances the sensitivity and specificity of available metagenomic methods and offers the possibility to analyze genes related to the selection and transfer of antimicrobial resistance (biocides, heavy metals, plasmids). The model opens the possibility to study other complex microbial systems in which minority populations play a relevant role.
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Medical microbiology
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Resistome
Clostridioides
Gut microbiome
Pyrosequencing
Shotgun
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Identification
Microbial genetics
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Microbial genetics
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Abstract Background With the escalating risk of antimicrobial resistance (AMR), there are limited analytical options available that can comprehensively assess the burden of AMR carried by clinical/environmental samples. Food can be a potential source of AMR bacteria for humans, but its significance in driving the clinical spread of AMR remains unclear, largely due to the lack of holistic-yet-sensitive tools for surveillance and evaluation. Metagenomics is a culture-independent approach well suited for uncovering genetic determinants of defined microbial traits, such as AMR, present within unknown bacterial communities. Despite its popularity, the conventional approach of non-selectively sequencing a sample’s metagenome (namely, shotgun-metagenomics) has several technical drawbacks that lead to uncertainty about its effectiveness for AMR assessment; for instance, the low discovery rate of resistance-associated genes due to their naturally small genomic footprint within the vast metagenome. Here, we describe the development of a targeted resistome sequencing method and demonstrate its application in the characterization of the AMR gene profile of bacteria associated with several retail foods. Result A targeted-metagenomic sequencing workflow using a customized bait-capture system targeting over 4,000 referenced AMR genes and 263 plasmid replicon sequences was validated against both mock and sample-derived bacterial community preparations. Compared to shotgun-metagenomics, the targeted method consistently provided for improved recovery of resistance gene targets with a much-improved target detection efficiency (> 300-fold). Targeted resistome analyses conducted on 36 retail-acquired food samples (fresh sprouts, n = 10; ground meat, n = 26) and their corresponding bacterial enrichment cultures (n = 36) reveals in-depth features regarding the identity and diversity of AMR genes, most of which were otherwise undetected by the whole-metagenome shotgun sequencing method. Furthermore, our findings suggest that foodborne Gammaproteobacteria could be the major reservoir of food-associated AMR genetic determinants, and that the resistome structure of the selected high-risk food commodities are, to a large extent, dictated by microbiome composition. Conclusions For metagenomic sequencing-based surveillance of AMR, the target-capture method presented herein represents a more sensitive and efficient approach to evaluate the resistome profile of complex food or environmental samples. This study also further implicates retail foods as carriers of diverse resistance-conferring genes indicating a potential impact on the dissemination of AMR.
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Developments in high-throughput next generation sequencing (NGS) technology have rapidly advanced the understanding of overall microbial ecology as well as occurrence and diversity of specific genes within diverse environments. In the present study, we compared the ability of varying sequencing depths to generate meaningful information about the taxonomic structure and prevalence of antimicrobial resistance genes (ARGs) in the bovine fecal microbial community. Metagenomic sequencing was conducted on eight composite fecal samples originating from four beef cattle feedlots. Metagenomic DNA was sequenced to various depths, D1, D0.5 and D0.25, with average sample read counts of 117, 59 and 26 million, respectively. A comparative analysis of the relative abundance of reads aligning to different phyla and antimicrobial classes indicated that the relative proportions of read assignments remained fairly constant regardless of depth. However, the number of reads being assigned to ARGs as well as to microbial taxa increased significantly with increasing depth. We found a depth of D0.5 was suitable to describe the microbiome and resistome of cattle fecal samples. This study helps define a balance between cost and required sequencing depth to acquire meaningful results.
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UniFrac
Pyrosequencing
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Shotgun metagenomic sequencing of the collective genomic information carried across microbial communities is emerging as a powerful approach for monitoring antibiotic resistance in environmental matrices. Metagenomics is advantageous in that known and putative antibiotic resistance genes (ARGs) (i.e., the resistome) can be screened simultaneously without a priori selection of targets. Additionally, as new ARGs are discovered and catalogued, stored sequencing data can be reanalyzed to assess the prevalence of emerging genes or pathogens. However, best practices for metagenomic data generation and processing are needed to support comparability across space and time. To support reproducible downstream analysis, guidance is first needed with respect to sampling design, sample preservation and storage, DNA extraction, library preparation, sequencing depth, and experimental controls. Here we conducted a systematic review to assess current practices for the application of metagenomics for AR profiling of wastewater, recycled water, and surface water and to offer recommendations to support comparability in the collection, production, and analysis of resulting data. Based on integrated analysis of findings and data reported across 95 articles identified, a field to benchtop metagenomic workflow is discussed for optimizing the representativeness and comparability of generated data. Through the reanalysis of 1474 publicly-available metagenomes, appropriate sequencing depths per environment and uniform normalization strategies are provided. Further, there is opportunity to harness the quantitative capacity of metagenomics more overtly through inclusion of sequencing controls. The recommendations will amplify the overall value of the metagenomic data generated to support within and between study comparisons, now and in the future.
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Comparability
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Antibiotic resistance (AR) represents a challenge for the treatment of infectious diseases. Traditionally, antibiotic resistance determinants have been retrieved from culturable bacteria which represent a minor fraction of the total microbial diversity found in natural environments such as soils. In this review, we summarize recent advances in the study of antibiotic resistance using two main culture-independent approaches: sequence-based metagenomics and functional metagenomics.
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The unprecedented rise of life-threatening antibiotic resistance (AR), combined with the unparalleled advances in DNA sequencing of genomes and metagenomes, has pushed the need for in silico detection of the resistance potential of clinical and environmental metagenomic samples through the quantification of AR genes (i.e., genes conferring antibiotic resistance). Therefore, determining an optimal methodology to quantitatively and accurately assess AR genes in a given environment is pivotal. Here, we optimized and improved existing AR detection methodologies from metagenomic datasets to properly consider AR-generating mutations in antibiotic target genes. Through comparative metagenomic analysis of previously published AR gene abundance in three publicly available metagenomes, we illustrate how mutation-generated resistance genes are either falsely assigned or neglected, which alters the detection and quantitation of the antibiotic resistome. In addition, we inspected factors influencing the outcome of AR gene quantification using metagenome simulation experiments, and identified that genome size, AR gene length, total number of metagenomics reads and selected sequencing platforms had pronounced effects on the level of detected AR. In conclusion, our proposed improvements in the current methodologies for accurate AR detection and resistome assessment show reliable results when tested on real and simulated metagenomic datasets.
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Antibiotic resistance is considered one of the greatest threats to global public health. Resistance is often conferred by the presence of antibiotic resistance genes (ARGs), which are readily found in the oral microbiome. In-depth genetic analyses of the oral microbiome through metagenomic techniques reveal a broad distribution of ARGs (including novel ARGs) in individuals not recently exposed to antibiotics, including humans in isolated indigenous populations. This has resulted in a paradigm shift from focusing on the carriage of antibiotic resistance in pathogenic bacteria to a broader concept of an oral resistome, which includes all resistance genes in the microbiome. Metagenomics is beginning to demonstrate the role of the oral resistome and horizontal gene transfer within and between commensals in the absence of selective pressure, such as an antibiotic. At the chairside, metagenomic data reinforce our need to adhere to current antibiotic guidelines to minimize the spread of resistance, as such data reveal the extent of ARGs without exposure to antimicrobials and the ecologic changes created in the oral microbiome by even a single dose of antibiotics. The aim of this review is to discuss the role of metagenomics in the investigation of the oral resistome, including the transmission of antibiotic resistance in the oral microbiome. Future perspectives, including clinical implications of the findings from metagenomic investigations of oral ARGs, are also considered.
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Horizontal Gene Transfer
Oral Microbiome
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