Pipeline for Antimicrobial Resistance Gene Quantification from Host Tissue. - Related Documents




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510201.0000Pipeline for Antimicrobial Resistance Gene Quantification from Host Tissue. Antibiotics are frequently used in food production animals to control disease and improve productivity, but this promotes the development of antimicrobial resistance (AMR) and subsequent broader spread of AMR bacteria throughout food chain, endangering the well-being and health of both animals and humans. In humans, the gut microbiome harbors a diverse range of AMR bacteria, known as the resistome. To effectively mitigate AMR in food animals requires first determining the expression and abundance of AMR-related genes in the gut resistome. Currently, such knowledge in regard to food animals is largely lacking. Gut tissue RNA sequencing (GTRS) can capture metabolically active transcripts from both the host and the microbes attached to the gut epithelium. Ideally, AMR genes can be quantified using GTRS data, making it possible to study the relationship between host and microbe. For the majority of these GTRS studies, only host transcriptome changes have been reported, while the microbial AMR remains largely unexamined, mainly due to the lack of easily implementable bioinformatics tools. Here we present a straightforward workflow to accomplish that using common command-line bioinformatics tools. With this pipeline, the host is considered noise, and host data are filtered out from the microbial reads. Transcript quantification of the AMR genes is then performed. The pipeline then continues through AMR transcript quantification, differential gene expression, and SNP analysis. Using open-source tools, we made this analytical pipeline easy to implement and able to generate results ready to be incorporated into publishable reports. Published 2025. This article is a U.S. Government work and is in the public domain in the USA. Basic Protocol: Running the gene quantification pipeline Support Protocol 1: Downloading FASTQ files from the NCBI database Support Protocol 2: Building a genome reference index of the host Support Protocol 3: Differential gene expression analysis Support Protocol 4: Single-nucleotide polymorphism (SNP) analysis.202540145236
429610.9997Twenty-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
510120.9997Identification 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
955330.9997A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved.202134015806
965740.9997Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome. Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We found that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism's direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics treatment and predict how organisms in the gut microbiome will respond to antibiotic administration. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical applications.201829359195
422050.9997Whole genome sequencing for the risk assessment of probiotic lactic acid bacteria. Probiotic bacteria exhibit beneficial effects on human and/or animal health, and have been widely used in foods and fermented products for decades. Most probiotics consist of lactic acid bacteria (LAB), which are used in the production of various food products but have also been shown to have the ability to prevent certain diseases. With the expansion of applications for probiotic LAB, there is an increasing concern with regard to safety, as cases with adverse effects, i.e., severe infections, transfer of antimicrobial resistance genes, etc., can occur. Currently, in vitro assays remain the primary way to assess the properties of LAB. However, such methodologies are not meeting the needs of strain risk assessment on a high-throughput scale, in the context of the evolving concept of food safety. Analyzing the complete genetic information, including potential virulence genes and other determinants with a negative impact on health, allows for assessing the safe use of the product, for which whole-genome sequencing (WGS) of individual LAB strains can be employed. Genomic data can also be used to understand subtle differences in the strain level important for beneficial effects, or protect patents. Here, we propose that WGS-based bioinformatics analyses are an ideal and cost-effective approach for the initial in silico microbial risk evaluation, while the technique may also increase our understanding of LAB strains for food safety and probiotic property evaluation.202335694810
659760.9997Exploiting 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
405170.9997The human microbiome harbors a diverse reservoir of antibiotic resistance genes. The increasing levels of multi-drug resistance in human pathogenic bacteria are compromising our ability to treat infectious disease. Since antibiotic resistance determinants are readily exchanged between bacteria through lateral gene transfer, there is an increasing interest in investigating reservoirs of antibiotic resistance accessible to pathogens. Due to the high likelihood of contact and genetic exchange with pathogens during disease progression, the human microflora warrants special attention as perhaps the most accessible reservoir of resistance genes. Indeed, numerous previous studies have demonstrated substantial antibiotic resistance in cultured isolates from the human microflora. By applying metagenomic functional selections, we recently demonstrated that the functional repertoire of resistance genes in the human microbiome is much more diverse than suggested using previous culture-dependent methods. We showed that many resistance genes from cultured proteobacteria from human fecal samples are identical to resistance genes harbored by human pathogens, providing strong support for recent genetic exchange of this resistance machinery. In contrast, most of the resistance genes we identified with culture independent metagenomic sampling from the same samples were novel when compared to all known genes in public databases. While this clearly demonstrates that the antibiotic resistance reservoir of the large fraction of the human microbiome recalcitrant to culturing is severely under sampled, it may also suggest that barriers exist to lateral gene transfer between these bacteria and readily cultured human pathogens. If we hope to turn the tide against multidrug resistant infections, we must urgently commit to quantitatively characterizing the resistance reservoirs encoded by our diverse human microbiomes, with a particular focus on routes of exchange of these reservoirs with other microbial communities.201021178459
405280.9997Functional metagenomics for the investigation of antibiotic resistance. Antibiotic resistance is a major threat to human health and well-being. To effectively combat this problem we need to understand the range of different resistance genes that allow bacteria to resist antibiotics. To do this the whole microbiota needs to be investigated. As most bacteria cannot be cultivated in the laboratory, the reservoir of antibiotic resistance genes in the non-cultivatable majority remains relatively unexplored. Currently the only way to study antibiotic resistance in these organisms is to use metagenomic approaches. Furthermore, the only method that does not require any prior knowledge about the resistance genes is functional metagenomics, which involves expressing genes from metagenomic clones in surrogate hosts. In this review the methods and limitations of functional metagenomics to isolate new antibiotic resistance genes and the mobile genetic elements that mediate their spread are explored.201424556726
464490.9997Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes. Municipal wastewater provides a representative sample of human fecal waste across a catchment area and contains a wide diversity of microbes. Sequencing wastewater samples provides information about human-associated and medically-important microbial populations, and may be useful to assay disease prevalence and antimicrobial resistance (AMR). Here, we present a study in which we used untargeted metatranscriptomic sequencing on RNA extracted from 275 sewage influent samples obtained from eight wastewater treatment plants (WTPs) representing approximately 16 million people in Southern California between August 2020 - August 2021. We characterized bacterial and viral transcripts, assessed metabolic pathway activity, and identified over 2,000 AMR genes/variants across all samples. Because we did not deplete ribosomal RNA, we have a unique window into AMR carried as ribosomal mutants. We show that AMR diversity varied between WTPs and that the relative abundance of many individual AMR genes/variants increased over time and may be connected to antibiotic use during the COVID-19 pandemic. Similarly, we detected transcripts mapping to human pathogenic bacteria and viruses suggesting RNA sequencing is a powerful tool for wastewater-based epidemiology and that there are geographical signatures to microbial transcription. We captured the transcription of gene pathways common to bacterial cell processes, including central carbon metabolism, nucleotide synthesis/salvage, and amino acid biosynthesis. We also posit that due to the ubiquity of many viruses and bacteria in wastewater, new biological targets for microbial water quality assessment can be developed. To the best of our knowledge, our study provides the most complete longitudinal metatranscriptomic analysis of a large population's wastewater to date and demonstrates our ability to monitor the presence and activity of microbes in complex samples. By sequencing RNA, we can track the relative abundance of expressed AMR genes/variants and metabolic pathways, increasing our understanding of AMR activity across large human populations and sewer sheds.202235982656
4341100.9997Antimicrobial Resistance in Nontyphoidal Salmonella. Non-typhoidal Salmonella is the most common foodborne bacterial pathogen in most countries. It is widely present in food animal species, and therefore blocking its transmission through the food supply is a prominent focus of food safety activities worldwide. Antibiotic resistance in non-typhoidal Salmonella arises in large part because of antibiotic use in animal husbandry. Tracking resistance in Salmonella is required to design targeted interventions to contain or diminish resistance and refine use practices in production. Many countries have established systems to monitor antibiotic resistance in Salmonella and other bacteria, the earliest ones appearing the Europe and the US. In this chapter, we compare recent Salmonella antibiotic susceptibility data from Europe and the US. In addition, we summarize the state of known resistance genes that have been identified in the genus. The advent of routine whole genome sequencing has made it possible to conduct genomic surveillance of resistance based on DNA sequences alone. This points to a new model of surveillance in the future that will provide more definitive information on the sources of resistant Salmonella, the specific types of resistance genes involved, and information on how resistance spreads.201830027887
4036110.9997Man-made microbial resistances in built environments. Antimicrobial resistance is a serious threat to global public health, but little is known about the effects of microbial control on the microbiota and its associated resistome. Here we compare the microbiota present on surfaces of clinical settings with other built environments. Using state-of-the-art metagenomics approaches and genome and plasmid reconstruction, we show that increased confinement and cleaning is associated with a loss of microbial diversity and a shift from Gram-positive bacteria, such as Actinobacteria and Firmicutes, to Gram-negative such as Proteobacteria. Moreover, the microbiome of highly maintained built environments has a different resistome when compared to other built environments, as well as a higher diversity in resistance genes. Our results highlight that the loss of microbial diversity correlates with an increase in resistance, and the need for implementing strategies to restore bacterial diversity in certain built environments.201930814504
5112120.9997Genome-Based Prediction of Bacterial Antibiotic Resistance. Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.201930381421
3977130.9997Assessing the Impact of Heat Treatment of Food on Antimicrobial Resistance Genes and Their Potential Uptake by Other Bacteria-A Critical Review. The dissemination of antibiotic resistance genes (ARGs) is a global health concern. This study identifies and critically reviews the published evidence on whether cooking (heating) food to eliminate bacterial contamination induces sufficient damage to the functionality of ARGs. Overall, the review found that there is evidence in the literature that Antimicrobial Resistant (AMR) bacteria are no more heat resistant than non-AMR bacteria. Consequently, recommended heat treatments sufficient to kill non-AMR bacteria in food (70 °C for at least 2 min, or equivalent) should be equally effective in killing AMR bacteria. The literature shows there are several mechanisms through which functional genes from AMR bacteria could theoretically persist in heat-treated food and be transferred to other bacteria. The literature search found sparce published evidence on whether ARGs may actually persist in food after effective heat treatments, and whether functional genes can be transferred to other bacteria. However, three publications have demonstrated that functional ARGs in plasmids may be capable of persisting in foods after effective heat treatments. Given the global impact of AMR, there is clearly a need for further practical research on this topic to provide sufficient evidence to fully assess whether there is a risk to human health from the persistence of functional ARGs in heat-treated and cooked foods.202134943652
9557140.9996Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge. The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians' diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles.202235625299
9554150.9996A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.202235272349
4300160.9996A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria. Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.202134522024
4226170.9996Assessing the Risk of Probiotic Dietary Supplements in the Context of Antibiotic Resistance. Probiotic bacteria are known to harbor intrinsic and mobile genetic elements that confer resistance to a wide variety of antibiotics. Their high amounts in dietary supplements can establish a reservoir of antibiotic resistant genes in the human gut. These resistant genes can be transferred to pathogens that share the same intestinal habitat thus resulting in serious clinical ramifications. While antibiotic resistance of probiotic bacteria from food, human and animal sources have been well-documented, the resistant profiles of probiotics from dietary supplements have only been recently studied. These products are consumed with increasing regularity due to their health claims that include the improvement of intestinal health and immune response as well as prevention of acute and antibiotic-associated diarrhea and cancer; but, a comprehensive risk assessment on the spread of resistant genes to human health is lacking. Here, we highlight recent reports of antibiotic resistance of probiotic bacteria isolated from dietary supplements, and propose complementary strategies that can shed light on the risks of consuming such products in the context of a global widespread of antibiotic resistance. In concomitant with a broader screening of antibiotic resistance in probiotic supplements is the use of computational simulations, live imaging and functional genomics to harvest knowledge on the evolutionary behavior, adaptations and dynamics of probiotics studied in conditions that best represent the human gut including in the presence of antibiotics. The underlying goal is to enable the health benefits of probiotics to be exploited in a responsible manner and with minimal risk to human health.201728579981
9405180.9996Functional Metagenomic Screening for Antimicrobial Resistance in the Oral Microbiome. A large proportion of bacteria, from a multitude of environments, are not yet able to be grown in the laboratory, and therefore microbiological and molecular biological investigations of these bacteria are challenging. A way to circumvent this challenge is to analyze the metagenome, the entire collection of DNA molecules that can be isolated from a particular environment or sample. This collection of DNA molecules can be sequenced and assembled to determine what is present and infer functional potential, or used as a PCR template to detect known target DNA and potentially unknown regions of DNA nearby those targets; however assigning functions to new or conserved hypothetical, functionally cryptic, genes is difficult. Functional metagenomics allows researchers to determine which genes are responsible for selectable phenotypes, such as resistance to antimicrobials and metabolic capabilities, without the prerequisite needs to grow the bacteria containing those genes or to already know which genes are of interest. It is estimated that a third of the resident species of the human oral cavity is not yet cultivable and, together with the ease of sample acquisition, makes this metagenome particularly suited to functional metagenomic studies. Here we describe the methodology related to the collection of saliva samples, extraction of metagenomic DNA, construction of metagenomic libraries, as well as the description of functional assays that have previously led to the identification of new genes conferring antimicrobial resistance.202134410638
4049190.9996The Plasmidome of Firmicutes: Impact on the Emergence and the Spread of Resistance to Antimicrobials. The phylum Firmicutes is one of the most abundant groups of prokaryotes in the microbiota of humans and animals and includes genera of outstanding relevance in biomedicine, health care, and industry. Antimicrobial drug resistance is now considered a global health security challenge of the 21st century, and this heterogeneous group of microorganisms represents a significant part of this public health issue.The presence of the same resistant genes in unrelated bacterial genera indicates a complex history of genetic interactions. Plasmids have largely contributed to the spread of resistance genes among Staphylococcus, Enterococcus, and Streptococcus species, also influencing the selection and ecological variation of specific populations. However, this information is fragmented and often omits species outside these genera. To date, the antimicrobial resistance problem has been analyzed under a "single centric" perspective ("gene tracking" or "vehicle centric" in "single host-single pathogen" systems) that has greatly delayed the understanding of gene and plasmid dynamics and their role in the evolution of bacterial communities.This work analyzes the dynamics of antimicrobial resistance genes using gene exchange networks; the role of plasmids in the emergence, dissemination, and maintenance of genes encoding resistance to antimicrobials (antibiotics, heavy metals, and biocides); and their influence on the genomic diversity of the main Gram-positive opportunistic pathogens under the light of evolutionary ecology. A revision of the approaches to categorize plasmids in this group of microorganisms is given using the 1,326 fully sequenced plasmids of Gram-positive bacteria available in the GenBank database at the time the article was written.201526104702