The 100K Pathogen Genome Project is producing draft and closed genome sequences from diverse pathogens. This project expanded globally to include a snapshot of global bacterial genome diversity. The genomes form a sequence database that has a variety of uses from systematics to public health. Copyright © 2017 Weimer.
The genus Thiomargarita includes the world’s largest bacteria. But as uncultured organisms, their physiology, metabolism, and basis for their gigantism are not well understood. Thus, a genomics approach, applied to a single Candidatus Thiomargarita nelsonii cell was employed to explore the genetic potential of one of these enigmatic giant bacteria. The Thiomargarita cell was obtained from an assemblage of budding Ca. T. nelsonii attached to a provannid gastropod shell from Hydrate Ridge, a methane seep offshore of Oregon, USA. Here we present a manually curated genome of Bud S10 resulting from a hybrid assembly of long Pacific Biosciences and short Illumina sequencing reads. With respect to inorganic carbon fixation and sulfur oxidation pathways, the Ca. T. nelsonii Hydrate Ridge Bud S10 genome was similar to marine sister taxa within the family Beggiatoaceae. However, the Bud S10 genome contains genes suggestive of the genetic potential for lithotrophic growth on arsenite and perhaps hydrogen. The genome also revealed that Bud S10 likely respires nitrate via two pathways: a complete denitrification pathway and a dissimilatory nitrate reduction to ammonia pathway. Both pathways have been predicted, but not previously fully elucidated, in the genomes of other large, vacuolated, sulfur-oxidizing bacteria. Surprisingly, the genome also had a high number of unusual features for a bacterium to include the largest number of metacaspases and introns ever reported in a bacterium. Also present, are a large number of other mobile genetic elements, such as insertion sequence (IS) transposable elements and miniature inverted-repeat transposable elements (MITEs). In some cases, mobile genetic elements disrupted key genes in metabolic pathways. For example, a MITE interrupts hupL, which encodes the large subunit of the hydrogenase in hydrogen oxidation. Moreover, we detected a group I intron in one of the most critical genes in the sulfur oxidation pathway, dsrA. The dsrA group I intron also carried a MITE sequence that, like the hupL MITE family, occurs broadly across the genome. The presence of a high degree of mobile elements in genes central to Thiomargarita’s core metabolism has not been previously reported in free-living bacteria and suggests a highly mutable genome.
Productivity of ruminant livestock depends on the rumen microbiota, which ferment indigestible plant polysaccharides into nutrients used for growth. Understanding the functions carried out by the rumen microbiota is important for reducing greenhouse gas production by ruminants and for developing biofuels from lignocellulose. We present 410 cultured bacteria and archaea, together with their reference genomes, representing every cultivated rumen-associated archaeal and bacterial family. We evaluate polysaccharide degradation, short-chain fatty acid production and methanogenesis pathways, and assign specific taxa to functions. A total of 336 organisms were present in available rumen metagenomic data sets, and 134 were present in human gut microbiome data sets. Comparison with the human microbiome revealed rumen-specific enrichment for genes encoding de novo synthesis of vitamin B12, ongoing evolution by gene loss and potential vertical inheritance of the rumen microbiome based on underrepresentation of markers of environmental stress. We estimate that our Hungate genome resource represents ~75% of the genus-level bacterial and archaeal taxa present in the rumen.
Lactic acid bacteria (LAB) are important for human health. However, the relative abundance of LAB in complex samples, such as fecal samples, is low and their presence and diversity (at the species level) is understudied. Therefore, we designed LAB-specific primer pairs based on 16S rRNA gene consensus sequences from 443 species of LAB from seven genera. The LAB strains selected were genetically similar and known to play a role in human health. Prior to primer design, we obtained consistent sequences for the primer-binding sites by comparing the 16S rRNA gene sequences, manually identifying single-stranded primers and modifying these primers using degenerate bases. We assembled primer pairs with product sizes of >400 bp. Optimal LAB-specific primers were screened using three methods: PCR amplification, agarose gel electrophoresis and single-molecule real-time (SMRT) sequencing analysis. During the SMRT analysis procedure, we focused on sequence reads and diversity at the species level of target LAB in three fecal samples, using the universal bacterium primer 27f/1492r as a reference control. We created a phylogenetic tree to confirm the ability of the best candidate primer pair to differentiate amongst species. The results revealed that LAB-specific primer L5, with a product size of 750 bp, could generate 3222, 2552, and 3405 sequence reads from fecal Samples 1, 2, and 3. This represented 14, 13 and 10% of all target LAB sequence reads, respectively, compared with 2, 0.8, and 0.8% using the 27f/1492r primer. In addition, L5 detected LAB that were in low abundance and could not be detected using the 27f/1492r primer. The phylogenetic tree based on the alignments between the forward and reverse primer of L5 showed that species within the seven target LAB genera could be distinguished from each other, confirming L5 is a powerful tool for inferring phylogenetic relationships amongst LAB species. In conclusion, L5 is a LAB-specific primer that can be used for high-throughput sequencing and identification of taxa to the species level, especially in complex samples with relatively low LAB content. This enables further research on LAB population diversity in complex ecosystem, and on relationships between LAB and their hosts.
Soil acidification is accelerated by anthropogenic and agricultural activities, which could significantly affect global methane cycles. However, detailed knowledge of the genomic properties of methanotrophs adapted to acidic soils remains scarce. Using metagenomic approaches, we analyzed methane-utilizing communities enriched from acidic forest soils with pH 3 and 4, and recovered near-complete genomes of proteobacterial methanotrophs. Novel methanotroph genomes designated KS32 and KS41, belonging to two representative clades of methanotrophs (Methylocystis of Alphaproteobacteria and Methylobacter of Gammaproteobacteria), were dominant. Comparative genomic analysis revealed diverse systems of membrane transporters for ensuring pH homeostasis and defense against toxic chemicals. Various potassium transporter systems, sodium/proton antiporters, and two copies of proton-translocating F1F0-type ATP synthase genes were identified, which might participate in the key pH homeostasis mechanisms in KS32. In addition, the V-type ATP synthase and urea assimilation genes might be used for pH homeostasis in KS41. Genes involved in the modification of membranes by incorporation of cyclopropane fatty acids and hopanoid lipids might be used for reducing proton influx into cells. The two methanotroph genomes possess genes for elaborate heavy metal efflux pumping systems, possibly owing to increased heavy metal toxicity in acidic conditions. Phylogenies of key genes involved in acid adaptation, methane oxidation, and antiviral defense in KS41 were incongruent with that of 16S rRNA. Thus, the detailed analysis of the genome sequences provides new insights into the ecology of methanotrophs responding to soil acidification.
The programs GMAP and GSNAP, for aligning RNA-Seq and DNA-Seq datasets to genomes, have evolved along with advances in biological methodology to handle longer reads, larger volumes of data, and new types of biological assays. The genomic representation has been improved to include linear genomes that can compare sequences using single-instruction multiple-data (SIMD) instructions, compressed genomic hash tables with fast access using SIMD instructions, handling of large genomes with more than four billion bp, and enhanced suffix arrays (ESAs) with novel data structures for fast access. Improvements to the algorithms have included a greedy match-and-extend algorithm using suffix arrays, segment chaining using genomic hash tables, diagonalization using segmental hash tables, and nucleotide-level dynamic programming procedures that use SIMD instructions and eliminate the need for F-loop calculations. Enhancements to the functionality of the programs include standardization of indel positions, handling of ambiguous splicing, clipping and merging of overlapping paired-end reads, and alignments to circular chromosomes and alternate scaffolds. The programs have been adapted for use in pipelines by integrating their usage into R/Bioconductor packages such as gmapR and HTSeqGenie, and these pipelines have facilitated the discovery of numerous biological phenomena.
DNA sequencing of PCR-amplified marker genes, especially but not limited to the 16S rRNA gene, is perhaps the most common approach for profiling microbial communities. Due to technological constraints of commonly available DNA sequencing, these approaches usually take the form of short reads sequenced from a narrow, targeted variable region, with a corresponding loss of taxonomic resolution relative to the full length marker gene. We use Pacific Biosciences single-molecule, real-time circular consensus sequencing to sequence amplicons spanning the entire length of the 16S rRNA gene. However, this sequencing technology suffers from high sequencing error rate that needs to be addressed in order to take full advantage of the longer sequence. Here, we present a method to model the sequencing error process using a generalized pair hidden Markov chain model and estimate bacterial abundances in microbial samples. We demonstrate, with simulated and real data, that our model and its associated estimation procedure are able to give accurate estimates at the species (or subspecies) level, and is more flexible than existing methods like SImple Non-Bayesian TAXonomy (SINTAX).
The soil-plant ecosystem harbors an immense microbial diversity that challenges investigative approaches to study traits underlying plant-microbe association. Studies solely based on culture-dependent techniques have overlooked most microbial diversity. Here we describe the concomitant use of culture-dependent and -independent techniques to target plant-beneficial microbial groups from the sugarcane microbiome. The community-based culture collection (CBC) approach was used to access microbes from roots and stalks. The CBC recovered 399 unique bacteria representing 15.9% of the rhizosphere core microbiome and 61.6-65.3% of the endophytic core microbiomes of stalks. By cross-referencing the CBC (culture-dependent) with the sugarcane microbiome profile (culture-independent), we designed a synthetic community comprised of naturally occurring highly abundant bacterial groups from roots and stalks, most of which has been poorly explored so far. We then used maize as a model to probe the abundance-based synthetic inoculant. We show that when inoculated in maize plants, members of the synthetic community efficiently colonize plant organs, displace the natural microbiota and dominate at 53.9% of the rhizosphere microbial abundance. As a result, inoculated plants increased biomass by 3.4-fold as compared to uninoculated plants. The results demonstrate that abundance-based synthetic inoculants can be successfully applied to recover beneficial plant microbes from plant microbiota.
The incidence of the autoimmune disease, type 1 diabetes (T1D), has increased dramatically over the last half century in many developed countries and is particularly high in Finland and other Nordic countries. Along with genetic predisposition, environmental factors are thought to play a critical role in this increase. As with other autoimmune diseases, the gut microbiome is thought to play a potential role in controlling progression to T1D in children with high genetic risk, but we know little about how the gut microbiome develops in children with high genetic risk for T1D. In this study, the early development of the gut microbiomes of 76 children at high genetic risk for T1D was determined using high-throughput 16S rRNA gene sequencing. Stool samples from children born in the same hospital in Turku, Finland were collected at monthly intervals beginning at 4-6 months after birth until 2.2 years of age. Of those 76 children, 29 seroconverted to T1D-related autoimmunity (cases) including 22 who later developed T1D, the remaining 47 subjects remained healthy (controls). While several significant compositional differences in low abundant species prior to seroconversion were found, one highly abundant group composed of two closely related species, Bacteroides dorei and Bacteroides vulgatus, was significantly higher in cases compared to controls prior to seroconversion. Metagenomic sequencing of samples high in the abundance of the B. dorei/vulgatus group before seroconversion, as well as longer 16S rRNA sequencing identified this group as Bacteroides dorei. The abundance of B. dorei peaked at 7.6 months in cases, over 8 months prior to the appearance of the first islet autoantibody, suggesting that early changes in the microbiome may be useful for predicting T1D autoimmunity in genetically susceptible infants. The cause of increased B. dorei abundance in cases is not known but its timing appears to coincide with the introduction of solid food.
The study of mycobiota remains relatively unexplored due to the lack of sufficient available reference strains and databases compared to those of bacterial microbiome studies. Deep sequencing of Internal Transcribed Spacer (ITS) regions is the de facto standard for fungal diversity analysis. However, results are often biased because of the wide variety of sequence lengths in the ITS regions and the complexity of high-throughput sequencing (HTS) technologies. In this study, a curated ITS database, ntF-ITS1, was constructed. This database can be utilized for the taxonomic assignment of fungal community members. We evaluated the efficacy of strategies for mycobiome analysis by using this database and characterizing a mock fungal community consisting of 26 species representing 15 genera using ITS1 sequencing with three HTS platforms: Illumina MiSeq (MiSeq), Ion Torrent Personal Genome Machine (IonPGM), and Pacific Biosciences (PacBio). Our evaluation demonstrated that PacBio’s circular consensus sequencing with greater than 8 full-passes most accurately reconstructed the composition of the mock community. Using this strategy for deep-sequencing analysis of the gut mycobiota in healthy Japanese individuals revealed two major mycobiota types: a single-species type composed of Candida albicans or Saccharomyces cerevisiae and a multi-species type. In this study, we proposed the best possible processing strategies for the three sequencing platforms, of which, the PacBio platform allowed for the most accurate estimation of the fungal community. The database and methodology described here provide critical tools for the emerging field of mycobiome studies.
Analyzing human as well as animal microbiota composition has gained growing interest because structural components and metabolites of microorganisms fundamentally influence all aspects of host physiology. Originally dominated by culture-dependent methods for exploring these ecosystems, the development of molecular techniques such as high throughput sequencing has dramatically increased our knowledge. Because many studies of the microbiota are based on the bacterial 16S ribosomal RNA (rRNA) gene targets, they can, at least in principle, be compared to determine the role of the microbiome composition for developmental processes, host metabolism, and physiology as well as different diseases. In our review, we will summarize differences and pitfalls in current experimental protocols, including all steps from nucleic acid extraction to bioinformatical analysis which may produce variation that outweighs subtle biological differences. Future developments, such as integration of metabolomic, transcriptomic, and metagenomic data sets and standardization of the procedures, will be discussed. © The Author 2015. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.
As more is understood regarding the human microbiome, it is increasingly important for nurse scientists and healthcare practitioners to analyze these microbial communities and their role in health and disease. 16S rRNA sequencing is a key methodology in identifying these bacterial populations that has recently transitioned from use primarily in research to having increased utility in clinical settings.The objectives of this review are to (a) describe 16S rRNA sequencing and its role in answering research questions important to nursing science; (b) provide an overview of the oral, lung, and gut microbiomes and relevant research; and (c) identify future implications for microbiome research and 16S sequencing in translational nursing science.Sequencing using the 16S rRNA gene has revolutionized research and allowed scientists to easily and reliably characterize complex bacterial communities. This type of research has recently entered the clinical setting, one of the best examples involving the use of 16S sequencing to identify resistant pathogens, thereby improving the accuracy of bacterial identification in infection control. Clinical microbiota research and related requisite methods are of particular relevance to nurse scientists-individuals uniquely positioned to utilize these techniques in future studies in clinical settings.
There are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing, and there is a need to adapt short read tools to long read datasets.We describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods.All presented methods are implemented in the open source edition of MEGAN, and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community.This article was reviewed by Nicola Segata together with Moreno Zolfo, Pete James Lockhart and Serghei Mangul.This work extends the applicability of the widely-used metagenomic analysis software MEGAN to long reads. Our study suggests that the presented LAST+MEGAN-LR pipeline is sufficiently fast and accurate.
Lung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier integrity and increases susceptibility to infections. Herein, we hypothesize that somatic mutations together with cigarette smoke generate a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from 33 controls and 143 cancer cases, we conduct 16S ribosomal RNA (rRNA) bacterial gene sequencing, with RNA-sequencing data from lung cancer cases in The Cancer Genome Atlas serving as the validation cohort.Overall, we demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue. In squamous cell carcinoma specifically, a separate group of taxa are identified, in which Acidovorax is enriched in smokers. Acidovorax temporans is identified within tumor sections by fluorescent in situ hybridization and confirmed by two separate 16S rRNA strategies. Further, these taxa, including Acidovorax, exhibit higher abundance among the subset of squamous cell carcinoma cases with TP53 mutations, an association not seen in adenocarcinomas.The results of this comprehensive study show both microbiome-gene and microbiome-exposure interactions in squamous cell carcinoma lung cancer tissue. Specifically, tumors harboring TP53 mutations, which can impair epithelial function, have a unique bacterial consortium that is higher in relative abundance in smoking-associated tumors of this type. Given the significant need for clinical diagnostic tools in lung cancer, this study may provide novel biomarkers for early detection.
Acquisition of genes through horizontal gene transfer (HGT) allows microbes to rapidly gain new capabilities and adapt to new or changing environments. Identifying widespread HGT regions within multispecies microbiomes can pinpoint the molecular mechanisms that play key roles in microbiome assembly. We sought to identify horizontally transferred genes within a model microbiome, the cheese rind. Comparing 31 newly sequenced and 134 previously sequenced bacterial isolates from cheese rinds, we identified over 200 putative horizontally transferred genomic regions containing 4733 protein coding genes. The largest of these regions are enriched for genes involved in siderophore acquisition, and are widely distributed in cheese rinds in both Europe and the US. These results suggest that HGT is prevalent in cheese rind microbiomes, and that identification of genes that are frequently transferred in a particular environment may provide insight into the selective forces shaping microbial communities.
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