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322700.9981Geographic pattern of antibiotic resistance genes in the metagenomes of the giant panda. The rise in infections by antibiotic-resistant bacteria poses a serious public health problem worldwide. The gut microbiome of animals is a reservoir for antibiotic resistance genes (ARGs). However, the correlation between the gut microbiome of wild animals and ARGs remains controversial. Here, based on the metagenomes of giant pandas (including three wild populations from the Qinling, Qionglai and Xiaoxiangling Mountains, and two major captive populations from Yaan and Chengdu), we investigated the potential correlation between the constitution of the gut microbiome and the composition of ARGs across the different geographic locations and living environments. We found that the types of ARGs were correlated with gut microbiome composition. The NMDS cluster analysis using Jaccard distance of the ARGs composition of the gut microbiome of wild giant pandas displayed a difference based on geographic location. Captivity also had an effect on the differences in ARGs composition. Furthermore, we found that the Qinling population exhibited profound dissimilarities of both gut microbiome composition and ARGs (the highest proportion of Clostridium and vancomycin resistance genes) when compared to the other wild and captive populations studies, which was supported by previous giant panda whole-genome sequencing analysis. In this study, we provide an example of a potential consensus pattern regarding host population genetics, symbiotic gut microbiome and ARGs. We revealed that habitat isolation impacts the ARG structure in the gut microbiome of mammals. Therefore, the difference in ARG composition between giant panda populations will provide some basic information for their conservation and management, especially for captive populations.202132812361
509810.9979Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
387520.9979Ecological insights into the microbiology of food using metagenomics and its potential surveillance applications. A diverse array of micro-organisms can be found on food, including those that are pathogenic or resistant to antimicrobial drugs. Metagenomics involves extracting and sequencing the DNA of all micro-organisms on a sample, and here, we used a combination of culture and culture-independent approaches to investigate the microbial ecology of food to assess the potential application of metagenomics for the microbial surveillance of food. We cultured common foodborne pathogens and other organisms including Escherichia coli, Klebsiella/Raoultella spp., Salmonella spp. and Vibrio spp. from five different food commodities and compared their genomes to the microbial communities obtained by metagenomic sequencing following host (food) DNA depletion. The microbial populations of retail food were found to be predominated by psychrotrophic bacteria, driven by the cool temperatures in which the food products are stored. Pathogens accounted for a small percentage of the food metagenome compared to the psychrotrophic bacteria, and cultured pathogens were inconsistently identified in the metagenome data. The microbial composition of food varied amongst different commodities, and metagenomics was able to classify the taxonomic origin of 59% of antimicrobial resistance genes (ARGs) found on food to the genus level, but it was unclear what percentage of ARGs were associated with mobile genetic elements and thus transferable to other bacteria. Metagenomics may be used to survey the ARG burden, composition and carriage on foods to which consumers are exposed. However, food metagenomics, even after depleting host DNA, inconsistently identifies pathogens without enrichment or further bait capture.202539752189
315530.9978In silico mapping of microbial communities and stress responses in a porcine slaughterhouse and pork products through its production chain, and the efficacy of HLE disinfectant. The use of shotgun metagenomic sequencing to understand ecological-level spread of microbes and their genes has provided new insights for the prevention, surveillance and control of microbial contaminants in the slaughterhouse environment. Here, microbial samples were collected from products and surrounding areas though a porcine slaughter process; shotgun metagenomic DNA-sequencing of these samples revealed a high community diversity within the porcine slaughterhouse and pork products, in zones originating from animal arrival through to the sale zones. Bacteria were more prevalent in the first zones, such as arrival- and anesthesia-zones, and DNA viruses were prevalent in the scorching-and-whip zone, animal products and sale zone. Data revealed the dominance of Firmicutes and Proteobacteria phyla followed by Actinobacteria, with a clear shift in the relative abundance of lactic acid bacteria (mainly Lactobacillus sp.) from early slaughtering steps to Proteobacteria and then to viruses suggesting site-specific community compositions occur in the slaughterhouse. Porcine-type-C oncovirus was the main virus found in slaughterhouse, which causes malignant diseases in animals and humans. As such, to guarantee food safety in a slaughterhouse, a better decipher of ecology and adaptation strategies of microbes becomes crucial. Analysis of functional genes further revealed high abundance of diverse genes associated with stress, especially in early zones (animal and environmental surfaces of arrival zone with 57,710 and 40,806 genes, respectively); SOS responsive genes represented the most prevalent, possibly associated with genomic changes responsible of biofilm formation, stringent response, heat shock, antimicrobial production and antibiotic response. The presence of several antibiotic resistance genes suggests horizontal gene transfer, thus increasing the likelihood for resistance selection in human pathogens. These findings are of great concern, with the suggestion to focus control measures and establish good disinfection strategies to avoid gene spread and microbial contaminants (bacteria and viruses) from the animal surface into the food chain and environment, which was achieved by applying HLE disinfectant after washing with detergent.202032846568
322340.9978A cross-sectional comparison of gut metagenomes between dairy workers and community controls. BACKGROUND: As a nexus of routine antibiotic use and zoonotic pathogen presence, the livestock farming environment is a potential hotspot for the emergence of zoonotic diseases and antibiotic resistant bacteria. Livestock can further facilitate disease transmission by serving as intermediary hosts for pathogens before a spillover event. In light of this, we aimed to characterize the microbiomes and resistomes of dairy workers, whose exposure to the livestock farming environment places them at risk for facilitating community transmission of antibiotic resistant genes and emerging zoonotic diseases. RESULTS: Using shotgun sequencing, we investigated differences in the taxonomy, diversity and gene presence of 10 dairy farm workers and 6 community controls' gut metagenomes, contextualizing these samples with additional publicly available gut metagenomes. We found no significant differences in the prevalence of resistance genes, virulence factors, or taxonomic composition between the two groups. The lack of statistical significance may be attributed, in part, to the limited sample size of our study or the potential similarities in exposures between the dairy workers and community controls. We did, however, observe patterns warranting further investigation including greater abundance of tetracycline resistance genes and prevalence of cephamycin resistance genes as well as lower average gene diversity (even after accounting for differential sequencing depth) in dairy workers' metagenomes. We also found evidence of commensal organism association with tetracycline resistance genes in both groups (including Faecalibacterium prausnitzii, Ligilactobacillus animalis, and Simiaoa sunii). CONCLUSIONS: This study highlights the utility of shotgun metagenomics in examining the microbiomes and resistomes of livestock workers, focusing on a cohort of dairy workers in the United States. While our study revealed no statistically significant differences between groups in taxonomy, diversity and gene presence, we observed patterns in antibiotic resistance gene abundance and prevalence that align with findings from previous studies of livestock workers in China and Europe. Our results lay the groundwork for future research involving larger cohorts of dairy and non-dairy workers to better understand the impact of occupational exposure to livestock farming on the microbiomes and resistomes of workers.202439033279
659750.9978Exploiting a targeted resistome sequencing approach in assessing antimicrobial resistance in retail foods. 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.202336991496
322260.9978Differences in gut metagenomes between dairy workers and community controls: a cross-sectional study. BACKGROUND: As a nexus of routine antibiotic use and zoonotic pathogen presence, the livestock farming environment is a potential hotspot for the emergence of zoonotic diseases and antibiotic resistant bacteria. Livestock can further facilitate disease transmission by serving as intermediary hosts for pathogens as they undergo evolution prior to a spillover event. In light of this, we are interested in characterizing the microbiome and resistome of dairy workers, whose exposure to the livestock farming environment places them at risk for facilitating community transmission of antibiotic resistant genes and emerging zoonotic diseases. RESULTS: Using shotgun sequencing, we investigated differences in the taxonomy, diversity and gene presence of the human gut microbiome of 10 dairy farm workers and 6 community controls, supplementing these samples with additional publicly available gut metagenomes. We observed greater abundance of tetracycline resistance genes and prevalence of cephamycin resistance genes in dairy workers' metagenomes, and lower average gene diversity. We also found evidence of commensal organism association with plasmid-mediated tetracycline resistance genes in both dairy workers and community controls (including Faecalibacterium prausnitzii, Ligilactobacillus animalis, and Simiaoa sunii). However, we did not find significant differences in the prevalence of resistance genes or virulence factors overall, nor differences in the taxonomic composition of dairy worker and community control metagenomes. CONCLUSIONS: This study presents the first metagenomics analysis of United States dairy workers, providing insights into potential risks of exposure to antibiotics and pathogens in animal farming environments. Previous metagenomic studies of livestock workers in China and Europe have reported increased abundance and carriage of antibiotic resistance genes in livestock workers. While our investigation found no strong evidence for differences in the abundance or carriage of antibiotic resistance genes and virulence factors between dairy worker and community control gut metagenomes, we did observe patterns in the abundance of tetracycline resistance genes and the prevalence of cephamycin resistance genes that is consistent with previous work.202337215025
772270.9978Genome-resolving metagenomics reveals wild western capercaillies (Tetrao urogallus) as avian hosts for antibiotic-resistance bacteria and their interactions with the gut-virome community. The gut microbiome is a critical component of avian health, influencing nutrient uptake and immune functions. While the gut microbiomes of agriculturally important birds have been studied, the microbiomes of wild birds still need to be explored. Filling this knowledge gap could have implications for the microbial rewilding of captive birds and managing avian hosts for antibiotic-resistant bacteria (ARB). Using genome-resolved metagenomics, we recovered 112 metagenome-assembled genomes (MAGs) from the faeces of wild and captive western capercaillies (Tetrao urogallus) (n = 8). Comparisons of bacterial diversity between the wild and captive capercaillies suggest that the reduced diversity in the captive individual could be due to differences in diet. This was further substantiated through the analyses of 517,657 clusters of orthologous groups (COGs), which revealed that gene functions related to amino acids and carbohydrate metabolisms were more abundant in wild capercaillies. Metagenomics mining of resistome identified 751 antibiotic resistance genes (ARGs), of which 40.7 % were specific to wild capercaillies suggesting that capercaillies could be potential reservoirs for hosting ARG-associated bacteria. Additionally, the core resistome shared between wild and captive capercaillies indicates that birds can acquire these ARG-associated bacteria naturally from the environment (43.1 % of ARGs). The association of 26 MAGs with 120 ARGs and 378 virus operational taxonomic units (vOTUs) also suggests a possible interplay between these elements, where putative phages could have roles in modulating the gut microbiota of avian hosts. These findings can have important implications for conservation and human health, such as avian gut microbiota rewilding, identifying the emerging threats or opportunities due to phage-microbe interactions, and monitoring the potential spread of ARG-associated bacteria from wild avian populations.202337018898
429680.9978Twenty-first century molecular methods for analyzing antimicrobial resistance in surface waters to support One Health assessments. Antimicrobial resistance (AMR) in the environment is a growing global health concern, especially the dissemination of AMR into surface waters due to human and agricultural inputs. Within recent years, research has focused on trying to understand the impact of AMR in surface waters on human, agricultural and ecological health (One Health). While surface water quality assessments and surveillance of AMR have historically utilized culture-based methods, culturing bacteria has limitations due to difficulty in isolating environmental bacteria and the need for a priori information about the bacteria for selective isolation. The use of molecular techniques to analyze AMR at the genetic level has helped to overcome the difficulties with culture-based techniques since they do not require advance knowledge of the bacterial population and can analyze uncultivable environmental bacteria. The aim of this review is to provide an overview of common contemporary molecular methods available for analyzing AMR in surface waters, which include high throughput real-time polymerase chain reaction (HT-qPCR), metagenomics, and whole genome sequencing. This review will also feature how these methods may provide information on human and animal health risks. HT-qPCR works at the nanoliter scale, requires only a small amount of DNA, and can analyze numerous gene targets simultaneously, but may lack in analytical sensitivity and the ability to optimize individual assays compared to conventional qPCR. Metagenomics offers more detailed genomic information and taxonomic resolution than PCR by sequencing all the microbial genomes within a sample. Its open format allows for the discovery of new antibiotic resistance genes; however, the quantity of DNA necessary for this technique can be a limiting factor for surface water samples that typically have low numbers of bacteria per sample volume. Whole genome sequencing provides the complete genomic profile of a single environmental isolate and can identify all genetic elements that may confer AMR. However, a main disadvantage of this technique is that it only provides information about one bacterial isolate and is challenging to utilize for community analysis. While these contemporary techniques can quickly provide a vast array of information about AMR in surface waters, one technique does not fully characterize AMR nor its potential risks to human, animal, or ecological health. Rather, a combination of techniques (including both molecular- and culture-based) are necessary to fully understand AMR in surface waters from a One Health perspective.202133774111
324390.9977Virulence-associated and antibiotic resistance genes of microbial populations in cattle feces analyzed using a metagenomic approach. The bovine fecal microbiota impacts human food safety as well as animal health. Although the bacteria of cattle feces have been well characterized using culture-based and culture-independent methods, techniques have been lacking to correlate total community composition with community function. We used high throughput sequencing of total DNA extracted from fecal material to characterize general community composition and examine the repertoire of microbial genes present in beef cattle feces, including genes associated with antibiotic resistance and bacterial virulence. Results suggest that traditional 16S sequencing using "universal" primers to generate full-length sequence may under represent Acitinobacteria and Proteobacteria. Over eight percent (8.4%) of the sequences from our beef cattle fecal pool sample could be categorized as virulence genes, including a suite of genes associated with resistance to antibiotic and toxic compounds (RATC). This is a higher proportion of virulence genes found in Sargasso sea, chicken cecum, and cow rumen samples, but comparable to the proportion found in Antarctic marine derived lake, human fecal, and farm soil samples. The quantitative nature of metagenomic data, combined with the large number of RATC classes represented in samples from widely different habitats indicates that metagenomic data can be used to track relative amounts of antibiotic resistance genes in individual animals over time. Consequently, these data can be used to generate sample-specific and temporal antibiotic resistance gene profiles to facilitate an understanding of the ecology of the microbial communities in each habitat as well as the epidemiology of antibiotic resistant gene transport between and among habitats.201121167876
4550100.9977Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming. Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species.202235333870
3973110.9977Assessing the impact of sewage and wastewater on antimicrobial resistance in nearshore Antarctic biofilms and sediments. BACKGROUND: Despite being recognised as a global problem, our understanding of human-mediated antimicrobial resistance (AMR) spread to remote regions of the world is limited. Antarctica, often referred to as "the last great wilderness", is experiencing increasing levels of human visitation through tourism and expansion of national scientific operations. Therefore, it is critical to assess the impact that these itinerant visitors have on the natural environment. This includes monitoring human-mediated AMR, particularly around population concentrations such as visitor sites and Antarctic research stations. This study takes a sequencing discovery-led approach to investigate levels and extent of AMR around the Rothera Research Station (operated by the UK) on the Antarctic Peninsula. RESULTS: Amplicon sequencing of biofilms and sediments from the vicinity of Rothera Research Station revealed highly variable and diverse microbial communities. Analysis of AMR genes generated from long-reads Nanopore MinION sequencing showed similar site variability in both drug class and resistance mechanism. Thus, no site sampled was more or less diverse than the other, either in the biofilm or sediment samples. Levels of enteric bacteria in biofilm and sediment samples were low at all sites, even in biofilm samples taken from the station sewage treatment plant (STP). It would appear that incorporation of released enteric bacteria in wastewater into more established biofilms or associations with sediment was poor. This was likely due to the inactivation and vulnerability of these bacteria to the extreme environmental conditions in Antarctica. CONCLUSIONS: Our results suggest minimal effect of a strong feeder source (i.e. sewage effluent) on biofilm and sediment microbial community composition, with each site developing its unique niche community. The factors producing these niche communities need elucidation, alongside studies evaluating Antarctic microbial physiologies. Our data from cultivated bacteria show that they are highly resilient to different environmental conditions and are likely to thrive in a warmer world. Our data show that AMR in the Antarctic marine environment is far more complex than previously thought. Thus, more work is required to understand the true extent of the Antarctic microbiota biodiversity, their associated resistomes and the impact that human activities have on the Antarctic environment.202539833981
7727120.9977Psychrotrophic Bacteria Equipped with Virulence and Colonization Traits Populate the Ice Cream Manufacturing Environment. Several microbial taxa have been associated with food processing facilities, and they might resist by attaching on tools and equipment even after sanitation procedures, producing biofilms that adhere to the surfaces and might embed other microorganisms, including spoilers and pathogens. There is increasing evidence that these communities can be transferred to the final product. To explore the microbial contamination routes in a facility producing ice creams, we collected foods and environmental swabs from industrial surfaces of equipment and tools and performed taxonomic and functional analyses of the microbial DNA extracted from the environmental samples. Our results suggest that complex communities dominated by psychrotrophic bacteria (e.g., Pseudomonas and Acinetobacter spp.) inhabit the food processing environment, and we demonstrate that these communities might be transferred from the surfaces to the products. Functional analysis performed on environmental samples highlighted the presence of several genes linked to antimicrobial resistance and adherence on abiotic surfaces; such genes were more abundant on food contact (FC) than on other surfaces. Metagenome-assembled genomes (MAGs) of Pseudomonas stutzeri showed genes linked with biofilm formation and motility, which are surely linked to colonizing capabilities in the processing lines. The study highlights clear potential advantages of applying microbiome mapping in the food industry for source tracking of microbial contamination and for planning appropriate ad hoc sanitization strategies. IMPORTANCE Several microbial species might permanently establish in food processing facilities, thus contributing to food loss. In fact, food contact surfaces might transfer microorganisms to intermediates and products, potentially representing a hazard to human health. In this work, we provide evidence of the existence of complex microbial communities overcoming sanitation in an ice cream-producing facility. These communities harbored several genes that could potentially lead to attachment to surfaces and antimicrobial resistance. Also, prediction of routes of contamination showed that several potential spoilage taxa might end up in the final product. Importantly, in this work, we show that mapping the environmental microbiome is a high-resolution technique that might help food business operators ensure food quality and safety through detection of potentially hazardous microorganisms.202337432121
3874130.9977Culture-enriched human gut microbiomes reveal core and accessory resistance genes. BACKGROUND: Low-abundance microorganisms of the gut microbiome are often referred to as a reservoir for antibiotic resistance genes. Unfortunately, these less-abundant bacteria can be overlooked by deep shotgun sequencing. In addition, it is a challenge to associate the presence of resistance genes with their risk of acquisition by pathogens. In this study, we used liquid culture enrichment of stools to assemble the genome of lower-abundance bacteria from fecal samples. We then investigated the gene content recovered from these culture-enriched and culture-independent metagenomes in relation with their taxonomic origin, specifically antibiotic resistance genes. We finally used a pangenome approach to associate resistance genes with the core or accessory genome of Enterobacteriaceae and inferred their propensity to horizontal gene transfer. RESULTS: Using culture-enrichment approaches with stools allowed assembly of 187 bacterial species with an assembly size greater than 1 million nucleotides. Of these, 67 were found only in culture-enriched conditions, and 22 only in culture-independent microbiomes. These assembled metagenomes allowed the evaluation of the gene content of specific subcommunities of the gut microbiome. We observed that differentially distributed metabolic enzymes were associated with specific culture conditions and, for the most part, with specific taxa. Gene content differences between microbiomes, for example, antibiotic resistance, were for the most part not associated with metabolic enzymes, but with other functions. We used a pangenome approach to determine if the resistance genes found in Enterobacteriaceae, specifically E. cloacae or E. coli, were part of the core genome or of the accessory genome of this species. In our healthy volunteer cohort, we found that E. cloacae contigs harbored resistance genes that were part of the core genome of the species, while E. coli had a large accessory resistome proximal to mobile elements. CONCLUSION: Liquid culture of stools contributed to an improved functional and comparative genomics study of less-abundant gut bacteria, specifically those associated with antibiotic resistance. Defining whether a gene is part of the core genome of a species helped in interpreting the genomes recovered from culture-independent or culture-enriched microbiomes.201930953542
2543140.9977Capturing the antibiotic resistome of preterm infants reveals new benefits of probiotic supplementation. BACKGROUND: Probiotic use in preterm infants can mitigate the impact of antibiotic exposure and reduce rates of certain illnesses; however, the benefit on the gut resistome, the collection of antibiotic resistance genes, requires further investigation. We hypothesized that probiotic supplementation of early preterm infants (born < 32-week gestation) while in hospital reduces the prevalence of antibiotic resistance genes associated with pathogenic bacteria in the gut. We used a targeted capture approach to compare the resistome from stool samples collected at the term corrected age of 40 weeks for two groups of preterm infants (those that routinely received a multi-strain probiotic during hospitalization and those that did not) with samples from full-term infants at 10 days of age to identify if preterm birth or probiotic supplementation impacted the resistome. We also compared the two groups of preterm infants up to 5 months of age to identify persistent antibiotic resistance genes. RESULTS: At the term corrected age, or 10 days of age for the full-term infants, we found over 80 antibiotic resistance genes in the preterm infants that did not receive probiotics that were not identified in either the full-term or probiotic-supplemented preterm infants. More genes associated with antibiotic inactivation mechanisms were identified in preterm infants unexposed to probiotics at this collection time-point compared to the other infants. We further linked these genes to mobile genetic elements and Enterobacteriaceae, which were also abundant in their gut microbiomes. Various genes associated with aminoglycoside and beta-lactam resistance, commonly found in pathogenic bacteria, were retained for up to 5 months in the preterm infants that did not receive probiotics. CONCLUSIONS: This pilot survey of preterm infants shows that probiotics administered after preterm birth during hospitalization reduced the diversity and prevented persistence of antibiotic resistance genes in the gut microbiome. The benefits of probiotic use on the microbiome and the resistome should be further explored in larger groups of infants. Due to its high sensitivity and lower sequencing cost, our targeted capture approach can facilitate these surveys to further address the implications of resistance genes persisting into infancy without the need for large-scale metagenomic sequencing. Video Abstract.202236008821
4776150.9977Integrate genome-based assessment of safety for probiotic strains: Bacillus coagulans GBI-30, 6086 as a case study. Probiotics are microorganisms that confer beneficial effects on the host; nevertheless, before being allowed for human consumption, their safety must be verified with accurate protocols. In the genomic era, such procedures should take into account the genomic-based approaches. This study aims at assessing the safety traits of Bacillus coagulans GBI-30, 6086 integrating the most updated genomics-based procedures and conventional phenotypic assays. Special attention was paid to putative virulence factors (VF), antibiotic resistance (AR) genes and genes encoding enzymes responsible for harmful metabolites (i.e. biogenic amines, BAs). This probiotic strain was phenotypically resistant to streptomycin and kanamycin, although the genome analysis suggested that the AR-related genes were not easily transferrable to other bacteria, and no other genes with potential safety risks, such as those related to VF or BA production, were retrieved. Furthermore, no unstable elements that could potentially lead to genomic rearrangements were detected. Moreover, a workflow is proposed to allow the proper taxonomic identification of a microbial strain and the accurate evaluation of risk-related gene traits, combining whole genome sequencing analysis with updated bioinformatics tools and standard phenotypic assays. The workflow presented can be generalized as a guideline for the safety investigation of novel probiotic strains to help stakeholders (from scientists to manufacturers and consumers) to meet regulatory requirements and avoid misleading information.201626952108
3231160.9977Diversity analysis and metagenomic insights into antibiotic and metal resistance among Himalayan hot spring bacteriobiome insinuating inherent environmental baseline levels of antibiotic and metal tolerance. OBJECTIVES: Mechanisms of occurrence and expression of antibiotic resistance genes (ARGs) in thermophilic bacteria are still unknown owing to limited research and data. In this research, comparative profiling of ARGs and metal tolerance genes among thermophilic bacteria has been done by functional metagenomic methods. METHODS: Shotgun metagenomic sequence data were generated using Illumina HiSeq 4000. Putative ARGs from the PROKKA predicted genes were identified with the ardbAnno V.1.0 script available from the ARDB (Antibiotic Resistance Genes Database) consortium using the non-redundant resistance genes as a reference. Putative metal resistance genes (MRGs) were identified by using BacMetScan V.1.0. The whole-genome sequencing for bacterial isolates was performed using Illumina HiSeq 4000 sequencing technology with a paired-end sequencing module. RESULTS: Metagenomic analysis showed the dominance of Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes in two hot springs of Sikkim. ARG analysis through shotgun gene sequencing was found to be negative in the case of thermophilic bacteria. However, few genes were detected but they showed maximum similarity with mesophilic bacteria. Concurrently, MRGs were also detected in the metagenome sequence of isolates from hot springs. Detection of MRGs and absence of ARGs investigated by whole-genome sequencing in the reference genome sequence of thermophilic Geobacillus also conveyed the same message. CONCLUSION: The study of ARGs and MRGs (Heavy metal resistance gene) among culturable and non-culturable bacteria from the hot springs of Sikkim via metagenomics showed a preferential selection of MRGs over ARGs. The absence of ARGs also does not support the co-selection of ARGs and MRGs in these environments. This evolutionary selection of metal resistance over antibiotic genes may have been necessary to survive in the geological craters which have an abundance of different metals from earth sediments rather than antibiotics. Furthermore, the selection could be environment driven depending on the susceptibility of ARGs in a thermophilic environments as it reduces the chances of horizontal gene transfer.202032344121
5167170.9977Decreased Antimicrobial Resistance Gene Richness Following Fecal Microbiota, Live-jslm (REBYOTA®) Administration: Post Hoc Analysis of PUNCH CD3. BACKGROUND: The human gastrointestinal microbiome helps maintain vital functions related to overall health, including resistance to pathogen colonization. Disruption of the microbiome, leading to loss of colonization resistance, can be caused by multiple factors, including antimicrobial use. The loss of colonization resistance may lead to establishment or proliferation of opportunistic bacteria that carry genes associated with antimicrobial resistance, potentially increasing the risk of infection by such antimicrobial-resistant bacteria. A potential approach to mitigating this risk involves restoration of healthier microbiota and pathogen colonization resistance. METHODS: A metagenomic sequencing method was used to conduct a post hoc analysis of antibiotic resistance gene richness among fecal samples from participants administered fecal microbiota, live-jslm (REBYOTA; abbreviated as RBL) or placebo in the PUNCH CD3 study (NCT03244644) for the prevention of recurrent Clostridioides difficile infection. RESULTS: At baseline, participants had higher antibiotic resistance gene richness than a representative healthy cohort. Over time, RBL responders had lower antibiotic resistance gene richness at the class, group, and mechanism levels as compared with placebo responders. These differences were evident as early as 1 week after administration and sustained for at least 6 months. RBL responders also had decreased richness of antibiotic resistance genes deemed high risk based on designated bacterial public health threats. CONCLUSIONS: These data support a model in which microbiota-based products, including RBL, may reduce antibiotic resistance gene richness, thereby possibly reducing the risk of antimicrobial-resistant organism infection. TRIAL REGISTRATION: NCT03244644 (https://clinicaltrials.gov/study/NCT03244644; 9 August 2017).202540672762
9661180.9977Pangenomes of human gut microbiota uncover links between genetic diversity and stress response. The genetic diversity of the gut microbiota has a central role in host health. Here, we created pangenomes for 728 human gut prokaryotic species, quadrupling the genes of strain-specific genomes. Each of these species has a core set of a thousand genes, differing even between closely related species, and an accessory set of genes unique to the different strains. Functional analysis shows high strain variability associates with sporulation, whereas low variability is linked with antibiotic resistance. We further map the antibiotic resistome across the human gut population and find 237 cases of extreme resistance even to last-resort antibiotics, with a predominance among Enterobacteriaceae. Lastly, the presence of specific genes in the microbiota relates to host age and sex. Our study underscores the genetic complexity of the human gut microbiota, emphasizing its significant implications for host health. The pangenomes and antibiotic resistance map constitute a valuable resource for further research.202439353429
5101190.9977Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.202540671232