Genomic encyclopedia of bacteria and archaea: sequencing a myriad of type strains. - Related Documents




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966901.0000Genomic encyclopedia of bacteria and archaea: sequencing a myriad of type strains. Microbes hold the key to life. They hold the secrets to our past (as the descendants of the earliest forms of life) and the prospects for our future (as we mine their genes for solutions to some of the planet's most pressing problems, from global warming to antibiotic resistance). However, the piecemeal approach that has defined efforts to study microbial genetic diversity for over 20 years and in over 30,000 genome projects risks squandering that promise. These efforts have covered less than 20% of the diversity of the cultured archaeal and bacterial species, which represent just 15% of the overall known prokaryotic diversity. Here we call for the funding of a systematic effort to produce a comprehensive genomic catalog of all cultured Bacteria and Archaea by sequencing, where available, the type strain of each species with a validly published name (currently∼11,000). This effort will provide an unprecedented level of coverage of our planet's genetic diversity, allow for the large-scale discovery of novel genes and functions, and lead to an improved understanding of microbial evolution and function in the environment.201425093819
967010.9998An Approach to In Silico Dissection of Bacterial Intelligence Through Selective Genomic Tools. All the genetic potential and the intelligence a bacteria can showcase in a given environment are embedded in its genome. In this study, we have presented systematic guidelines to understand a bacterial genome with the relevant set of in silico tools using a novel bacteria as an example. This study presents a multi-dimensional approach from genome annotation to tracing genes and their network of metabolism operating in an organism. It also shows how the sequence can be used to mine the enzymes and construction of its 3-dimensional structure so that its functional behavior can be predicted and compared. The discriminating algorithm allows analysis of the promoter region and provides the insight in the regulation of genes in spite of the similarity in its sequences. The ecological niche specific bacterial behavior and adapted altered physiology can be understood through the presence of secondary metabolite, antibiotic resistance genes, and viral genes; and it helps in the valorization of genetic information for developing new biological application/processes. This study provides an in silico work plan and necessary steps for genome analysis of novel bacteria without any rigorous wet lab experiments.201830013271
967120.9998Genome-scale genetic manipulation methods for exploring bacterial molecular biology. Bacteria are diverse and abundant, playing key roles in human health and disease, the environment, and biotechnology. Despite progress in genome sequencing and bioengineering, much remains unknown about the functional organization of prokaryotes. For instance, roughly a third of the protein-coding genes of the best-studied model bacterium, Escherichia coli, currently lack experimental annotations. Systems-level experimental approaches for investigating the functional associations of bacterial genes and genetic structures are essential for defining the fundamental molecular biology of microbes, preventing the spread of antibacterial resistance in the clinic, and driving the development of future biotechnological applications. This review highlights recently introduced large-scale genetic manipulation and screening procedures for the systematic exploration of bacterial gene functions, molecular relationships, and the global organization of bacteria at the gene, pathway, and genome levels.201222517266
966730.9998Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs. Rates of infection with antibiotic-resistant bacteria have increased precipitously over the past several decades, with far-reaching healthcare and societal costs. Recent evidence has established a link between antibiotic resistance genes in human pathogens and those found in non-pathogenic, commensal, and environmental organisms, prompting deeper investigation of natural and human-associated reservoirs of antibiotic resistance. Functional metagenomic selections, in which shotgun-cloned DNA fragments are selected for their ability to confer survival to an indicator host, have been increasingly applied to the characterization of many antibiotic resistance reservoirs. These experiments have demonstrated that antibiotic resistance genes are highly diverse and widely distributed, many times bearing little to no similarity to known sequences. Through unbiased selections for survival to antibiotic exposure, functional metagenomics can improve annotations by reducing the discovery of false-positive resistance and by allowing for the identification of previously unrecognizable resistance genes. In this review, we summarize the novel resistance functions uncovered using functional metagenomic investigations of natural and human-impacted resistance reservoirs. Examples of novel antibiotic resistance genes include those highly divergent from known sequences, those for which sequence is entirely unable to predict resistance function, bifunctional resistance genes, and those with unconventional, atypical resistance mechanisms. Overcoming antibiotic resistance in the clinic will require a better understanding of existing resistance reservoirs and the dissemination networks that govern horizontal gene exchange, informing best practices to limit the spread of resistance-conferring genes to human pathogens.201323760651
956640.9998Computational resources in the management of antibiotic resistance: Speeding up drug discovery. This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.202133892146
966850.9998Genomic and functional techniques to mine the microbiome for novel antimicrobials and antimicrobial resistance genes. Microbial communities contain diverse bacteria that play important roles in every environment. Advances in sequencing and computational methodologies over the past decades have illuminated the phylogenetic and functional diversity of microbial communities from diverse habitats. Among the activities encoded in microbiomes are the abilities to synthesize and resist small molecules, yielding antimicrobial activity. These functions are of particular interest when viewed in light of the public health emergency posed by the increase in clinical antimicrobial resistance and the dwindling antimicrobial discovery and approval pipeline, and given the intimate ecological and evolutionary relationship between antimicrobial biosynthesis and resistance. Here, we review genomic and functional methods that have been developed for accessing the antimicrobial biosynthesis and resistance capacity of microbiomes and highlight outstanding examples of their applications.201727768825
966660.9998The comprehensive antibiotic resistance database. The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.201323650175
940670.9997Proteomics as the final step in the functional metagenomics study of antimicrobial resistance. The majority of clinically applied antimicrobial agents are derived from natural products generated by soil microorganisms and therefore resistance is likely to be ubiquitous in such environments. This is supported by the fact that numerous clinically important resistance mechanisms are encoded within the genomes of such bacteria. Advances in genomic sequencing have enabled the in silico identification of putative resistance genes present in these microorganisms. However, it is not sufficient to rely on the identification of putative resistance genes, we must also determine if the resultant proteins confer a resistant phenotype. This will require an analysis pipeline that extends from the extraction of environmental DNA, to the identification and analysis of potential resistance genes and their resultant proteins and phenotypes. This review focuses on the application of functional metagenomics and proteomics to study antimicrobial resistance in diverse environments.201525784907
955380.9997A 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
408890.9997Expanding the soil antibiotic resistome: exploring environmental diversity. Antibiotic resistance has largely been studied in the context of failure of the drugs in clinical settings. There is now growing evidence that bacteria that live in the environment (e.g. the soil) are multi-drug-resistant. Recent functional screens and the growing accumulation of metagenomic databases are revealing an unexpected density of resistance genes in the environment: the antibiotic resistome. This challenges our current understanding of antibiotic resistance and provides both barriers and opportunities for antimicrobial drug discovery.200717951101
4090100.9997Ancient Resistome. Antibiotic resistance is an ancient biological mechanism in bacteria, although its proliferation in our contemporary world has been amplified through antimicrobial therapy. Recent studies conducted on ancient environmental and human samples have uncovered numerous antibiotic-resistant bacteria and resistance genes. The resistance genes that have been reported from the analysis of ancient bacterial DNA include genes coding for several classes of antibiotics, such as glycopeptides, β-lactams, tetracyclines, and macrolides. The investigation of the resistome of ancient bacteria is a recent and emerging field of research, and technological advancements such as next-generation sequencing will further contribute to its growth. It is hoped that the knowledge gained from this research will help us to better understand the evolution of antibiotic resistance genes and will also be used in drug design as a proactive measure against antibiotic resistance.201627726801
9567110.9997How to discover new antibiotic resistance genes? Antibiotic resistance (AR) is a worldwide concern and the description of AR have been discovered mainly because of their implications in human medicine. Since the recent burden of whole-genome sequencing of microorganisms, the number of new AR genes (ARGs) have dramatically increased over the last decade. Areas covered: In this review, we will describe the different methods that could be used to characterize new ARGs using classic or innovative methods. First, we will focus on the biochemical methods, then we will develop on molecular methods, next-generation sequencing and bioinformatics approaches. The use of various methods, including cloning, mutagenesis, transposon mutagenesis, functional genomics, whole genome sequencing, metagenomic and functional metagenomics will be reviewed here, outlining the advantages and drawbacks of each method. Bioinformatics softwares used for resistome analysis and protein modeling will be also described. Expert opinion: Biological experiments and bioinformatics analysis are complementary. Nowadays, the ARGs described only account for the tip of the iceberg of all existing resistance mechanisms. The multiplication of the ecosystems studied allows us to find a large reservoir of AR mechanisms. Furthermore, the adaptation ability of bacteria facing new antibiotics promises a constant discovery of new AR mechanisms.201930895843
4035120.9997Discovery of novel antibiotic resistance genes through metagenomics. Antibiotic resistance (AR) represents a challenge for the treatment of infectious diseases. Traditionally, antibiotic resistance determinants have been retrieved from culturable bacteria which represent a minor fraction of the total microbial diversity found in natural environments such as soils. In this review, we summarize recent advances in the study of antibiotic resistance using two main culture-independent approaches: sequence-based metagenomics and functional metagenomics.201425564024
4052130.9997Functional 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
4019140.9997Antimicrobial resistance in humans, livestock and the wider environment. Antimicrobial resistance (AMR) in humans is inter-linked with AMR in other populations, especially farm animals, and in the wider environment. The relatively few bacterial species that cause disease in humans, and are the targets of antibiotic treatment, constitute a tiny subset of the overall diversity of bacteria that includes the gut microbiota and vast numbers in the soil. However, resistance can pass between these different populations; and homologous resistance genes have been found in pathogens, normal flora and soil bacteria. Farm animals are an important component of this complex system: they are exposed to enormous quantities of antibiotics (despite attempts at reduction) and act as another reservoir of resistance genes. Whole genome sequencing is revealing and beginning to quantify the two-way traffic of AMR bacteria between the farm and the clinic. Surveillance of bacterial disease, drug usage and resistance in livestock is still relatively poor, though improving, but achieving better antimicrobial stewardship on the farm is challenging: antibiotics are an integral part of industrial agriculture and there are very few alternatives. Human production and use of antibiotics either on the farm or in the clinic is but a recent addition to the natural and ancient process of antibiotic production and resistance evolution that occurs on a global scale in the soil. Viewed in this way, AMR is somewhat analogous to climate change, and that suggests that an intergovernmental panel, akin to the Intergovernmental Panel on Climate Change, could be an appropriate vehicle to actively address the problem.201525918441
9694150.9997Antibiotics as selectors and accelerators of diversity in the mechanisms of resistance: from the resistome to genetic plasticity in the β-lactamases world. Antibiotics and antibiotic resistance determinants, natural molecules closely related to bacterial physiology and consistent with an ancient origin, are not only present in antibiotic-producing bacteria. Throughput sequencing technologies have revealed an unexpected reservoir of antibiotic resistance in the environment. These data suggest that co-evolution between antibiotic and antibiotic resistance genes has occurred since the beginning of time. This evolutionary race has probably been slow because of highly regulated processes and low antibiotic concentrations. Therefore to understand this global problem, a new variable must be introduced, that the antibiotic resistance is a natural event, inherent to life. However, the industrial production of natural and synthetic antibiotics has dramatically accelerated this race, selecting some of the many resistance genes present in nature and contributing to their diversification. One of the best models available to understand the biological impact of selection and diversification are β-lactamases. They constitute the most widespread mechanism of resistance, at least among pathogenic bacteria, with more than 1000 enzymes identified in the literature. In the last years, there has been growing concern about the description, spread, and diversification of β-lactamases with carbapenemase activity and AmpC-type in plasmids. Phylogenies of these enzymes help the understanding of the evolutionary forces driving their selection. Moreover, understanding the adaptive potential of β-lactamases contribute to exploration the evolutionary antagonists trajectories through the design of more efficient synthetic molecules. In this review, we attempt to analyze the antibiotic resistance problem from intrinsic and environmental resistomes to the adaptive potential of resistance genes and the driving forces involved in their diversification, in order to provide a global perspective of the resistance problem.201323404545
4091160.9997Insights into novel antimicrobial compounds and antibiotic resistance genes from soil metagenomes. In recent years a major worldwide problem has arisen with regard to infectious diseases caused by resistant bacteria. Resistant pathogens are related to high mortality and also to enormous healthcare costs. In this field, cultured microorganisms have been commonly focused in attempts to isolate antibiotic resistance genes or to identify antimicrobial compounds. Although this strategy has been successful in many cases, most of the microbial diversity and related antimicrobial molecules have been completely lost. As an alternative, metagenomics has been used as a reliable approach to reveal the prospective reservoir of antimicrobial compounds and antibiotic resistance genes in the uncultured microbial community that inhabits a number of environments. In this context, this review will focus on resistance genes as well as on novel antibiotics revealed by a metagenomics approach from the soil environment. Biotechnology prospects are also discussed, opening new frontiers for antibiotic development.201425278933
4086170.9997Insights into antibiotic resistance through metagenomic approaches. The consequences of bacterial infections have been curtailed by the introduction of a wide range of antibiotics. However, infections continue to be a leading cause of mortality, in part due to the evolution and acquisition of antibiotic-resistance genes. Antibiotic misuse and overprescription have created a driving force influencing the selection of resistance. Despite the problem of antibiotic resistance in infectious bacteria, little is known about the diversity, distribution and origins of resistance genes, especially for the unculturable majority of environmental bacteria. Functional and sequence-based metagenomics have been used for the discovery of novel resistance determinants and the improved understanding of antibiotic-resistance mechanisms in clinical and natural environments. This review discusses recent findings and future challenges in the study of antibiotic resistance through metagenomic approaches.201222191448
4049180.9997The 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
4018190.9997Mobile elements, zoonotic pathogens and commensal bacteria: conduits for the delivery of resistance genes into humans, production animals and soil microbiota. Multiple antibiotic resistant pathogens represent a major clinical challenge in both human and veterinary context. It is now well-understood that the genes that encode resistance are context independent. That is, the same gene is commonly present in otherwise very disparate pathogens in both humans and production and companion animals, and among bacteria that proliferate in an agricultural context. This can be true even for pathogenic species or clonal types that are otherwise confined to a single host or ecological niche. It therefore follows that mechanisms of gene flow must exist to move genes from one part of the microbial biosphere to another. It is widely accepted that lateral (or horizontal) gene transfer (L(H)GT) drives this gene flow. LGT is relatively well-understood mechanistically but much of this knowledge is derived from a reductionist perspective. We believe that this is impeding our ability to deal with the medical ramifications of LGT. Resistance genes and the genetic scaffolds that mobilize them in multiply drug resistant bacteria of clinical significance are likely to have their origins in completely unrelated parts of the microbial biosphere. Resistance genes are increasingly polluting the microbial biosphere by contaminating environmental niches where previously they were not detected. More attention needs to be paid to the way that humans have, through the widespread application of antibiotics, selected for combinations of mobile elements that enhance the flow of resistance genes between remotely linked parts of the microbial biosphere. Attention also needs to be paid to those bacteria that link human and animal ecosystems. We argue that multiply antibiotic resistant commensal bacteria are especially important in this regard. More generally, the post genomics era offers the opportunity for understanding how resistance genes are mobilized from a one health perspective. In the long term, this holistic approach offers the best opportunity to better manage what is an enormous problem to humans both in terms of health and food security.201323641238