Metagenomics analysis of the neonatal intestinal resistome
Stefano LéoÖmer Faruk ÇetinerLaure F. PittetNicole L. MessinaWilliam JakobLaurent FalquetNigel CurtisPetra Zimmermann
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The intestinal microbiome forms a major reservoir for antibiotic resistance genes (ARGs). Little is known about the neonatal intestinal resistome.The objective of this study was to investigate the intestinal resistome and factors that influence the abundance of ARGs in a large cohort of neonates.Shotgun metagenomics was used to analyse the resistome in stool samples collected at 1 week of age from 390 healthy, term-born neonates who did not receive antibiotics.Overall, 913 ARGs belonging to 27 classes were identified. The most abundant ARGs were those conferring resistance to tetracyclines, quaternary ammonium compounds, and macrolide-lincosamide-streptogramin-B. Phylogenetic composition was strongly associated with the resistome composition. Other factors that were associated with the abundance of ARGs were delivery mode, gestational age, birth weight, feeding method, and antibiotics in the last trimester of pregnancy. Sex, ethnicity, probiotic use during pregnancy, and intrapartum antibiotics had little effect on the abundance of ARGs.Even in the absence of direct antibiotic exposure, the neonatal intestine harbours a high abundance and a variety of ARGs.Keywords:
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Horizontal Gene Transfer
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Clostridioides
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Antibiotic resistant bacteria and their resistance determinants have been frequently reported in all types of environments especially in water systems. The collection of antibiotic resistant genes and precursors in host bacteria occupying wastewater environment represents wastewater resistome. This resistome is highly potential to resist almost all types of antibiotics exposed to the environment. Since antimicrobial resistance could be transferred from environmental bacteria to clinical or veterinary pathogens and vice versa, wastewaters systems are spawning grounds for global antibiotic resistance, with potentially serious infection consequences for human and animals. Although the sub-lethal dose of antibiotics is key stimulating factor, however, physiological and environmental stress on bacteria due to various factors including non-antibiotic antimicrobials, organic pollutants like PAHs, chlorinated phenols and heavy metals may also drive the enrichment of antibiotic resistance genes in the environment. Although actual key mechanisms for survival of antibiotic resistance bacteria, proliferation and dissemination of such genes in wastewater are still elusive, this mini review briefly describes environmental resistome, possible drivers and dissemination paths of resistance genes in wastewater systems. Considering the hazards, efficient wastewater treatment technologies are needed to be developed for mitigation of antibiotic resistance bacteria and resistance genes.
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Antibiotic resistance is a global problem which affects human health. The imprudent use of antibiotics (medicine, agriculture, aquaculture, and food industry) has resulted in the broader dissemination of resistance. Urban wastewater & sewage treatment plants act as the hotspot for the widespread of antimicrobial resistance. Natural environment also plays an important role in the dissemination of resistance. Mapping of antibiotic resistance genes (ARGS) in environment is essential for mitigating antimicrobial resistance (AMR) widespread. Therefore, the review article emphasizes on the application of metagenomics for the surveillance of antimicrobial resistance. Metagenomics is the next generation tool which is being used for cataloging the resistome of diverse environments. We summarize the different metagenomic tools that can be used for mining of ARGs and acquired AMR present in the metagenomic data. Also, we recommend application of targeted sequencing/ capture platform for mapping of resistome with higher specificity and selectivity.
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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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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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Over the last decade, it has become increasingly apparent that the microbiome is a central component in human well-being and illness. However, to establish innovative therapeutic methods, it is crucial to learn more about the microbiota. Thereby, the area of metagenomics and associated bioinformatics methods and tools has become considerable in the study of the human microbiome biodiversity. The application of these metagenomics approaches to studying the gut microbiome in COVID-19 patients could be one of the promising areas of research in the fight against the SARS-CoV-2 infection and disparity. Therefore, understanding how the gut microbiome is affected by or could affect the SARS-CoV-2 is very important. Herein, we present an overview of approaches and methods used in the current published studies on COVID-19 patients and the gut microbiome. The accuracy of these researches depends on the appropriate choice and the optimal use of the metagenomics bioinformatics platforms and tools. Interestingly, most studies reported that COVID-19 patients’ microbiota are enriched with opportunistic microorganisms. The choice and use of appropriate computational tools and techniques to accurately investigate the gut microbiota is therefore critical in determining the appropriate microbiome profile for diagnosis and the most reliable antiviral or preventive microbial composition.
2019-20 coronavirus outbreak
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