Dataset Information

Microbial community profiling of human saliva using shotgun metagenomic sequencing.

ABSTRACT: Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUS and BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

SUBMITTER: Hasan NA

PROVIDER: S-EPMC4028220 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Microbial community profiling of human saliva using shotgun metagenomic sequencing.

Hasan Nur A NA Young Brian A BA Minard-Smith Angela T AT Saeed Kelly K Li Huai H Heizer Esley M EM McMillan Nancy J NJ Isom Richard R Abdullah Abdul Shakur AS Bornman Daniel M DM Faith Seth A SA Choi Seon Young SY Dickens Michael L ML Cebula Thomas A TA Colwell Rita R RR

PloS one 20140520 5

Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacte ...[more]

PMID: 24846174

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Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Project description:BackgroundThe microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample. Microbes thriving in urban environments may be crucially important for human health, but have received less attention than those of other environments. Ongoing efforts started to target urban microbiomes at a large scale, but the most recent computational methods to profile these metagenomes have never been applied in this context. It is thus currently unclear whether such methods, that have proven successful at distinguishing even closely related strains in human microbiomes, are also effective in urban settings for tasks such as cultivation-free pathogen detection and microbial surveillance. Here, we aimed at a) testing the currently available metagenomic profiling tools on urban metagenomics; b) characterizing the organisms in urban environment at the resolution of single strain and c) discussing the biological insights that can be inferred from such methods.ResultsWe applied three complementary methods on the 1614 metagenomes of the CAMDA 2017 challenge. With MetaMLST we identified 121 known sequence-types from 15 species of clinical relevance. For instance, we identified several Acinetobacter strains that were close to the nosocomial opportunistic pathogen A. nosocomialis. With StrainPhlAn, a generalized version of the MetaMLST approach, we inferred the phylogenetic structure of Pseudomonas stutzeri strains and suggested that the strain-level heterogeneity in environmental samples is higher than in the human microbiome. Finally, we also probed the functional potential of the different strains with PanPhlAn. We further showed that SNV-based and pangenome-based profiling provide complementary information that can be combined to investigate the evolutionary trajectories of microbes and to identify specific genetic determinants of virulence and antibiotic resistances within closely related strains.ConclusionWe show that strain-level methods developed primarily for the analysis of human microbiomes can be effective for city-associated microbiomes. In fact, (opportunistic) pathogens can be tracked and monitored across many hundreds of urban metagenomes. However, while more effort is needed to profile strains of currently uncharacterized species, this work poses the basis for high-resolution analyses of microbiomes sampled in city and mass transportation environments.ReviewersThis article was reviewed by Alexandra Bettina Graf, Daniel Huson and Trevor Cickovski.

Project description:Biogeography (body site) is known to be one of the main factors influencing the composition of the skin microbial community. However, site-associated microbial variability at a fine-scale level was not well-characterized since there was a lack of high-resolution recognition of facial microbiota across kingdoms by shotgun metagenomic sequencing. To investigate the explicit microbial variance in the human face, 822 shotgun metagenomic sequencing data from Han Chinese recently published by our group, in combination with 97 North American samples from NIH Human Microbiome Project (HMP), were reassessed. Metagenomic profiling of bacteria, fungi, and bacteriophages, as well as enriched function modules from three facial sites (forehead, cheek, and the back of the nose), was analyzed. The results revealed that skin microbial features were more alike in the forehead and cheek while varied from the back of the nose in terms of taxonomy and functionality. Analysis based on biogeographic theories suggested that neutral drift with niche selection from the host could possibly give rise to the variations. Of note, the abundance of porphyrin-producing species, i.e., Cutibacterium acnes, Cutibacterium avidum, Cutibacterium granulosum, and Cutibacterium namnetense, was all the highest in the back of the nose compared with the forehead/cheek, which was consistent with the highest porphyrin level on the nose in our population. Sequentially, the site-associated microbiome variance was confirmed in American populations; however, it was not entirely consistent. Furthermore, our data revealed correlation patterns between Propionibacterium acnes bacteriophages with genus Cutibacterium at different facial sites in both populations; however, C. acnes exhibited a distinct correlation with P. acnes bacteriophages in Americans/Chinese. Taken together, in this study, we explored the fine-scale facial site-associated changes in the skin microbiome and provided insight into the ecological processes underlying facial microbial variations.

Project description:Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems.

Project description:BACKGROUND:Due to the cave oligotrophic environment, this habitat presents a challenge for microorganisms to colonize and thrive. However, it has been well documented that microorganisms play important roles in cave development. Survival of microbes in this unique habitat likely involves a broad range of adaptive capabilities. Recently, cave microbiomes all over the world are of great scientific interest. However, the majority of investigations focused mostly on small subunit ribosomal RNA (16S rRNA) gene, leaving the ecological role of the microbial community largely unknown. Here, we are particularly interested in exploring the taxonomic composition and metabolic potential of microorganisms in soil from Manao-Pee cave, a subterranean limestone cave in the western part of Thailand, by using high-throughput shotgun metagenomic sequencing. RESULTS:From taxonomic composition analysis using ribosomal RNA genes (rRNA), the results confirmed that Actinobacteria (51.2%) and Gammaproteobacteria (24.4%) were the dominant bacterial groups in the cave soil community. Metabolic potential analysis, based on six functional modules of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, revealed that functional genes involved in microbial metabolisms are highly represented in this community (40.6%). To better understand how microbes thrive under unfavorable cave condition, we focused on microbial energy metabolism. The results showed that microbial genes involved in oxidative phosphorylation were the most dominant (28.8%) in Manao-Pee cave, and were followed by methane metabolism (20.5%), carbon fixation (16.0%), nitrogen metabolism (14.7%), and sulfur metabolism (6.3%). In addition, microbial genes involved in xenobiotic biodegradation (26 pathways) and in production of secondary metabolites (27 pathways) were also identified. CONCLUSION:In addition to providing information on microbial diversity, we also gained insights into microbial adaptations and survival strategies under cave conditions. Based on rRNA genes, the results revealed that bacteria belonging to the Actinobacteria and Gammaproteobacteria were the most abundant in this community. From metabolic potential analysis, energy and nutrient sources that sustain diverse microbial population in this community might be atmospheric gases (methane, carbon dioxide, nitrogen), inorganic sulfur, and xenobiotic compounds. In addition, the presence of biosynthetic pathways of secondary metabolites suggested that they might play important ecological roles in the cave microbiome.

Project description:COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

Dataset Information

Microbial community profiling of human saliva using shotgun metagenomic sequencing.

Publications

Microbial community profiling of human saliva using shotgun metagenomic sequencing.

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