Double-edged sword: impact of fecal microbiome transplants on the gut resistome
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Purpose of review Fecal microbiome transplants (FMT) show promise in treating various diseases, such as Clostridioides difficile infections. FMT have also demonstrated the capacity to modulate the collection of antibiotic resistance genes (ARGs), termed the resistome, within the gut. The purpose of this review was to critically evaluate the literature regarding the interaction between FMT and the gut resistome and determine whether FMT could be used specifically to reduce ARG carriage in the gut. Recent findings Several studies have demonstrated a decrease in ARG carriage post-FMT administration in various disease states, including recurrent C. difficile infection and after antibiotic usage. However, other studies have reported an expansion of the resistome following FMT. Most studies contained small patient cohorts regardless of the outcome and showed heterogeneity in responses. Summary Research on resistome modulation by FMT is preliminary, and human studies currently lack consensus regarding benefits and risks. From a safety perspective, screening donor samples for ARGs in addition to antibiotic-resistant organisms may be advisable. Additional studies on the mechanisms underlying heterogeneity between studies and individuals are required before FMT is considered an efficient approach for resistome amelioration.Keywords:
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Gut microbiome
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Clostridioides
Gut microbiome
Pyrosequencing
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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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It is well known that there is an imbalance of gut microbiota in liver diseases, our previous study has proved that Aronia melanocarpa polyphenols (AMPs) can modulate the gut microbiota and affect the progression of liver diseases. Here, we analyzed the gut microbiota by 16S rRNA sequencing and bioinformatic analysis to explored the changes of gut microbiota composition and functions after LPS and AMPs intervention. Our results showed that there were significant differences in the gut microbiota structure between different treatment groups, such as increasing the abundance of Lactobacillaceae and Muribaculaceae, decreasing the abundance of Ruminococcaceae and Acidaminococcaceae. Furthermore, PICRUSt prediction showed that 29 functional pathways have changed significantly which may promote the treatment of liver diseases. This study could help to supplement the information about the community of gut microbiota in liver diseases and provide a new strategy for the treatment of liver diseases.
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Diversity and activity of gut microbiota residing in humans and animals are significantly influenced by the diet. A quercetin containing diet is effective in recovering gut microbiota in mice after antibiotic treatment and may act as a prebiotic in combating gut dysbiosis.
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Previous studies suggested a possible gut microbiota dysbiosis in chronic heart failure (CHF). However, direct evidence was lacking. In this study, we investigated the composition and metabolic patterns of gut microbiota in CHF patients to provide direct evidence and comprehensive understanding of gut microbiota dysbiosis in CHF. We enrolled 53 CHF patients and 41 controls. Metagenomic analyses of faecal samples and metabolomic analyses of faecal and plasma samples were then performed. We found that the composition of gut microbiota in CHF was significantly different from controls. Faecalibacterium prausnitzii decrease and Ruminococcus gnavus increase were the essential characteristics in CHF patients' gut microbiota. We also observed an imbalance of gut microbes involved in the metabolism of protective metabolites such as butyrate and harmful metabolites such as trimethylamine N-oxide in CHF patients. Metabolic features of both faecal and plasma samples from CHF patients also significantly changed. Moreover, alterations in faecal and plasma metabolic patterns correlated with gut microbiota dysbiosis in CHF. Taken together, we found that CHF was associated with distinct gut microbiota dysbiosis and pinpointed the specific core bacteria imbalance in CHF, along with correlations between changes in certain metabolites and gut microbes.
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Faecalibacterium prausnitzii
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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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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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