Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome.
A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.
Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.
The human microbiome refers to the community of microorganisms including prokaryotes, viruses and microbial eukaryotes that populate the human body. The National Institutes of Health launched an initiative that focuses describing the diversity of microbial species associated with health and disease. The first phase of this initiative includes the sequencing of hundreds of microbial reference genomes, coupled to metagenomic sequencing from multiple body sites. Here we present results from an initial reference genome sequencing of 178 microbial genomes. From 547,968 predicted polypeptides that correspond to the gene complement of these strains “novel” polypeptides that had both unmasked sequence length > 100 amino acids and no BLASTP match to any non-reference entry in the nr subset were defined. This analysis resulted in a set of 30,867 polypeptides, of which 29,987 (~97%) were unique. In addition, this set of microbial genomes allows for ~ 40% of random sequences from the microbiome of the gastrointestinal tract to be associated with organisms based on the match criteria used. Insights into pan-genome analysis suggest that we are still far from saturating microbial species genetic datasets. In addition, the associated metrics and standards used by the group for quality assurance are presented.
Defining bacterial species remains a challenging problem even for the model bacterium Escherichia coli and has major practical consequences for reliable diagnosis of infectious disease agents and regulations for transport and possession of organisms of economic importance. E. coli traditionally is thought to live within the gastrointestinal tract of humans and other warm-blooded animals and not to survive for extended periods outside its host; this understanding is the basis for its widespread use as a fecal contamination indicator. Here, we report the genome sequences of nine environmentally adapted strains that are phenotypically and taxonomically indistinguishable from typical E. coli (commensal or pathogenic). We find, however, that the commensal genomes encode for more functions that are important for fitness in the human gut, do not exchange genetic material with their environmental counterparts, and hence do not evolve according to the recently proposed fragmented speciation model. These findings are consistent with a more stringent and ecologic definition for bacterial species than the current definition and provide means to start replacing traditional approaches of defining distinctive phenotypes for new species with omics-based procedures. They also have important implications for reliable diagnosis and regulation of pathogenic E. coli and for the coliform cell-counting test.evolution | genomics | species concept
Bacterial genomics has greatly expanded our understanding of microdiversification patterns within a species, but analyses at higher taxonomical levels are necessary to understand and predict the independent rise of pathogens in a genus. We have sampled, sequenced, and assessed the diversity of genomes of validly named and tentative species of the Acinetobacter genus, a clade including major nosocomial pathogens and biotechnologically important species. We inferred a robust global phylogeny and delimited several new putative species. The genus is very ancient and extremely diverse: Genomes of highly divergent species share more orthologs than certain strains within a species. We systematically characterized elements and mechanisms driving genome diversification, such as conjugative elements, insertion sequences, and natural transformation. We found many error-prone polymerases that may play a role in resistance to toxins, antibiotics, and in the generation of genetic variation. Surprisingly, temperate phages, poorly studied in Acinetobacter, were found to account for a significant fraction of most genomes. Accordingly, many genomes encode clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems with some of the largest CRISPR-arrays found so far in bacteria. Integrons are strongly overrepresented in Acinetobacter baumannii, which correlates with its frequent resistance to antibiotics. Our data suggest that A. baumannii arose from an ancient population bottleneck followed by population expansion under strong purifying selection. The outstanding diversification of the species occurred largely by horizontal transfer, including some allelic recombination, at specific hotspots preferentially located close to the replication terminus. Our work sets a quantitative basis to understand the diversification of Acinetobacter into emerging resistant and versatile pathogens.
International audienceThe degree to which molecular epidemiology reveals informationabout the sources and transmission patterns of an outbreakdepends on the resolution of the technology used and the samplesstudied. Isolates of Escherichia coli O104:H4 from the outbreak centeredin Germany in May–July 2011, and the much smaller outbreakin southwest France in June 2011, were indistinguishable by standardtests. We report a molecular epidemiological analysis usingmultiplatform whole-genome sequencing and analysis of multipleisolates from the German and French outbreaks. Isolates from theGerman outbreak showed remarkably little diversity, with onlytwo single nucleotide polymorphisms (SNPs) found in isolates fromfour individuals. Surprisingly, we found much greater diversity (19SNPs) in isolates from seven individuals infected in the French outbreak.The German isolates form a clade within the more diverseFrench outbreak strains. Moreover, five isolates derived from a singleinfected individual from the French outbreak had extremelylimited diversity. The striking difference in diversity between theGerman and French outbreak samples is consistent with severalhypotheses, including a bottleneck that purged diversity in theGerman isolates, variation in mutation rates in the two E. coli outbreakpopulations, or uneven distribution of diversity in the seedpopulations that led to each outbreak
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.