Functional Metagenomics Characterization of an Anaerobic Saltwater Bioreactor

Methanogens are restricted to a few genera of Archaea, however they have great importance in the carbon cycle, impacting climactic considerations, and also find a role in renewable energy in the form of biogas. Here, we examine the microbial contribution to the production of methane in a sargassum fed anaerobic saltwater bioreactor, which are poorly characterized compared to fresh water bioreactors, using a comprehensive functional metagenomics approach. Despite abundant production of methane, we detected a low proportion of Archaea in the system using 16S rRNA community profile analyses. We address the low representation using an additional 16S rRNA analysis of shotgun data and a consideration of CO2:CH4production. Using a novel network alignment and tree building approach, we measured similarity between the meta-metabolic capabilities of different anaerobic microbial communities. The saltwater bioreactor samples clustered together, validating the approach and providing a method of determining meta-metabolic similarity between microbial communities, with a range of potential applications. We also introduce a number of additional approaches for examining and interpreting meta-metabolic network topology. The low abundance of methanogens appears as a common property of such anaerobic systems and likely reflects the relatively poor energetics of methanogens, while examination of key enzymes confirms that hydrogen producing bacteria are the major fermentative guild. Our results indicate that the use of readily available seawater and marine macroalgae is a promising approach to the production of biogas as a source of renewable energy.

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Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

10.1101/340653 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arghavan Bahadorinejad ◽

Ivan Ivanov ◽

Johanna W Lampe ◽

Meredith AJ Hullar ◽

Robert S Chapkin ◽

...

Keyword(s):

16S Rrna ◽

Sample Size ◽

Microbial Communities ◽

State Of The Art ◽

Metagenomic Data ◽

Data Sets ◽

Sequencing Data ◽

Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

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Characterization of the depth-related changes in the microbial communities in Lake Hovsgol sediment by 16S rRNA gene-based approaches

The Journal of Microbiology ◽

10.1007/s12275-007-0189-1 ◽

2008 ◽

Vol 46 (2) ◽

pp. 125-136 ◽

Cited By ~ 20

Author(s):

Young-Do Nam ◽

Youlboong Sung ◽

Ho-Won Chang ◽

Seong Woon Roh ◽

Kyoung-Ho Kim ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Microbial Communities ◽

Rrna Gene ◽

Lake Hovsgol

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Characterization of efficient aerobic denitrifiers isolated from two different sequencing batch reactors by 16S-rRNA analysis

Journal of Bioscience and Bioengineering ◽

10.1263/jbb.103.563 ◽

2007 ◽

Vol 103 (6) ◽

pp. 563-567 ◽

Cited By ~ 26

Author(s):

Ping Wang ◽

Xiuting Li ◽

Mufei Xiang ◽

Qian Zhai

Keyword(s):

16S Rrna ◽

Batch Reactors ◽

Sequencing Batch Reactors ◽

16S Rrna Analysis

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Characterization of oral bacteria in the tongue coating of patients with halitosis using 16S rRNA analysis

Acta Odontologica Scandinavica ◽

10.1080/00016357.2020.1754459 ◽

2020 ◽

Vol 78 (7) ◽

pp. 541-546 ◽

Cited By ~ 1

Author(s):

Akiko Oshiro ◽

Takashi Zaitsu ◽

Masayuki Ueno ◽

Yoko Kawaguchi

Keyword(s):

16S Rrna ◽

Oral Bacteria ◽

Tongue Coating ◽

16S Rrna Analysis

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Massively parallel screening of synthetic microbial communities

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1900102116 ◽

2019 ◽

Vol 116 (26) ◽

pp. 12804-12809 ◽

Cited By ~ 43

Author(s):

Jared Kehe ◽

Anthony Kulesa ◽

Anthony Ortiz ◽

Cheri M. Ackerman ◽

Sri Gowtham Thakku ◽

...

Keyword(s):

Microbial Communities ◽

Massively Parallel ◽

Phenotypic Characterization ◽

Interspecies Interactions ◽

Additional Species ◽

Plant Symbiont ◽

Complementary Approach ◽

Potential Applications ◽

Synthetic Microbial Communities

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiontHerbaspirillum frisingensein a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.

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Taxonomic Studies of Predatory Bdellovibrios Based on 16S rRNA Analysis, Ribotyping and the hit Locus and Characterization of Isolates from the Gut of Animals

Systematic and Applied Microbiology ◽

10.1078/0723-2020-00042 ◽

2001 ◽

Vol 24 (3) ◽

pp. 385-394 ◽

Cited By ~ 53

Author(s):

Dominik Schwudke ◽

Eckhard Strauch ◽

Monika Krueger ◽

Bernd Appel

Keyword(s):

16S Rrna ◽

16S Rrna Analysis ◽

Taxonomic Studies

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Characterization of Microbial Communities Removing Nitrogen Oxides from Flue Gas: the BioDeNOx Process

Applied and Environmental Microbiology ◽

10.1128/aem.71.10.6345-6352.2005 ◽

2005 ◽

Vol 71 (10) ◽

pp. 6345-6352 ◽

Cited By ~ 30

Author(s):

Rajkumari Kumaraswamy ◽

Udo van Dongen ◽

J. Gijs Kuenen ◽

Wiebe Abma ◽

Mark C. M. van Loosdrecht ◽

...

Keyword(s):

Nitrogen Oxides ◽

16S Rrna ◽

Microbial Communities ◽

Flue Gas ◽

Iron Reduction ◽

Denaturing Gradient Gel Electrophoresis ◽

Pilot Scale ◽

Rrna Gene ◽

16S Rrna Analysis ◽

Gradient Gel Electrophoresis

ABSTRACT BioDeNOx is an integrated physicochemical and biological process for the removal of nitrogen oxides (NOx) from flue gases. In this process, the flue gas is purged through a scrubber containing a solution of Fe(II)EDTA2−, which binds the NOx to form an Fe(II)EDTA·NO2− complex. Subsequently, this complex is reduced in the bioreactor to dinitrogen by microbial denitrification. Fe(II)EDTA2−, which is oxidized to Fe(III)EDTA− by oxygen in the flue gas, is regenerated by microbial iron reduction. In this study, the microbial communities of both lab- and pilot-scale reactors were studied using culture-dependent and -independent approaches. A pure bacterial strain, KT-1, closely affiliated by 16S rRNA analysis to the gram-positive denitrifying bacterium Bacillus azotoformans, was obtained. DNA-DNA homology of the isolate with the type strain was 89%, indicating that strain KT-1 belongs to the species B. azotoformans. Strain KT-1 reduces Fe(II)EDTA·NO2− complex to N2 using ethanol, acetate, and Fe(II)EDTA2− as electron donors. It does not reduce Fe(III)EDTA−. Denaturing gradient gel electrophoresis analysis of PCR-amplified 16S rRNA gene fragments showed the presence of bacteria closely affiliated with members of the phylum Deferribacteres, an Fe(III)-reducing group of bacteria. Fluorescent in situ hybridization with oligonucleotide probes designed for strain KT-1 and members of the phylum Deferribacteres showed that the latter were more dominant in both reactors.

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