New Microbial Biodiversity in Marine Sediments

Microbes in marine sediments represent a large portion of the biosphere, and resolving their ecology is crucial for understanding global ocean processes. Single-gene diversity surveys have revealed several uncultured lineages that are widespread in ocean sediments and whose ecological roles are unknown, and advancements in the computational analysis of increasingly large genomic data sets have made it possible to reconstruct individual genomes from complex microbial communities. Using these metagenomic approaches to characterize sediments is transforming our view of microbial communities on the ocean floor and the biodiversity of the planet. In recent years, marine sediments have been a prominent source of new lineages in the tree of life. The incorporation of these lineages into existing phylogenies has revealed that many belong to distinct phyla, including archaeal phyla that are advancing our understanding of the origins of cellular complexity and eukaryotes. Detailed comparisons of the metabolic potentials of these new lineages have made it clear that uncultured bacteria and archaea are capable of mediating key previously undescribed steps in carbon and nutrient cycling.

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Metagenomics Analysis Reveals the Microbial Communities, Antimicrobial Resistance Gene Diversity and Potential Pathogen Transmission Risk of Two Different Landfills in China

Diversity ◽

10.3390/d13060230 ◽

2021 ◽

Vol 13 (6) ◽

pp. 230

Author(s):

Shan Wan ◽

Min Xia ◽

Jie Tao ◽

Yanjun Pang ◽

Fugen Yu ◽

...

Keyword(s):

Antibiotic Resistance ◽

Microbial Communities ◽

Resistance Gene ◽

Resistance Genes ◽

Gene Diversity ◽

Antibiotic Resistance Gene ◽

Antibiotic Resistance Genes ◽

Pathogen Transmission ◽

Transmission Risk ◽

Antimicrobial Resistance Gene

In this study, we used a metagenomic approach to analyze microbial communities, antibiotic resistance gene diversity, and human pathogenic bacterium composition in two typical landfills in China. Results showed that the phyla Proteobacteria, Bacteroidetes, and Actinobacteria were predominant in the two landfills, and archaea and fungi were also detected. The genera Methanoculleus, Lysobacter, and Pseudomonas were predominantly present in all samples. sul2, sul1, tetX, and adeF were the four most abundant antibiotic resistance genes. Sixty-nine bacterial pathogens were identified from the two landfills, with Klebsiella pneumoniae, Bordetella pertussis, Pseudomonas aeruginosa, and Bacillus cereus as the major pathogenic microorganisms, indicating the existence of potential environmental risk in landfills. In addition, KEGG pathway analysis indicated the presence of antibiotic resistance genes typically associated with human antibiotic resistance bacterial strains. These results provide insights into the risk of pathogens in landfills, which is important for controlling the potential secondary transmission of pathogens and reducing workers’ health risk during landfill excavation.

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A gene co-association network regulating gut microbial communities in a Duroc pig population

Microbiome ◽

10.1186/s40168-020-00994-8 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Antonio Reverter ◽

Maria Ballester ◽

Pamela A. Alexandre ◽

Emilio Mármol-Sánchez ◽

Antoni Dalmau ◽

...

Keyword(s):

Microbial Communities ◽

Candidate Genes ◽

Relative Abundance ◽

Association Studies ◽

Single Gene ◽

Host Genome ◽

Genome Wide Association Studies ◽

Vaccine Response ◽

Microbiome Composition ◽

Host Genetic

Abstract Background Analyses of gut microbiome composition in livestock species have shown its potential to contribute to the regulation of complex phenotypes. However, little is known about the host genetic control over the gut microbial communities. In pigs, previous studies are based on classical “single-gene-single-trait” approaches and have evaluated the role of host genome controlling gut prokaryote and eukaryote communities separately. Results In order to determine the ability of the host genome to control the diversity and composition of microbial communities in healthy pigs, we undertook genome-wide association studies (GWAS) for 39 microbial phenotypes that included 2 diversity indexes, and the relative abundance of 31 bacterial and six commensal protist genera in 390 pigs genotyped for 70 K SNPs. The GWAS results were processed through a 3-step analytical pipeline comprised of (1) association weight matrix; (2) regulatory impact factor; and (3) partial correlation and information theory. The inferred gene regulatory network comprised 3561 genes (within a 5 kb distance from a relevant SNP–P < 0.05) and 738,913 connections (SNP-to-SNP co-associations). Our findings highlight the complexity and polygenic nature of the pig gut microbial ecosystem. Prominent within the network were 5 regulators, PRDM15, STAT1, ssc-mir-371, SOX9 and RUNX2 which gathered 942, 607, 588, 284 and 273 connections, respectively. PRDM15 modulates the transcription of upstream regulators of WNT and MAPK-ERK signaling to safeguard naive pluripotency and regulates the production of Th1- and Th2-type immune response. The signal transducer STAT1 has long been associated with immune processes and was recently identified as a potential regulator of vaccine response to porcine reproductive and respiratory syndrome. The list of regulators was enriched for immune-related pathways, and the list of predicted targets includes candidate genes previously reported as associated with microbiota profile in pigs, mice and human, such as SLIT3, SLC39A8, NOS1, IL1R2, DAB1, TOX3, SPP1, THSD7B, ELF2, PIANP, A2ML1, and IFNAR1. Moreover, we show the existence of host-genetic variants jointly associated with the relative abundance of butyrate producer bacteria and host performance. Conclusions Taken together, our results identified regulators, candidate genes, and mechanisms linked with microbiome modulation by the host. They further highlight the value of the proposed analytical pipeline to exploit pleiotropy and the crosstalk between bacteria and protists as significant contributors to host-microbiome interactions and identify genetic markers and candidate genes that can be incorporated in breeding program to improve host-performance and microbial traits.

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High Performance Computational Analysis of Large-scale Proteome Data Sets to Assess Incremental Contribution to Coverage of the Human Genome

Journal of Proteome Research ◽

10.1021/pr400181q ◽

2013 ◽

Vol 12 (6) ◽

pp. 2858-2868 ◽

Cited By ~ 29

Author(s):

Nadin Neuhauser ◽

Nagarjuna Nagaraj ◽

Peter McHardy ◽

Sara Zanivan ◽

Richard Scheltema ◽

...

Keyword(s):

Human Genome ◽

High Performance ◽

Large Scale ◽

Computational Analysis ◽

Data Sets

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Random Sampling Process Leads to Overestimation of β-Diversity of Microbial Communities

mBio ◽

10.1128/mbio.00324-13 ◽

2013 ◽

Vol 4 (3) ◽

Cited By ~ 45

Author(s):

Jizhong Zhou ◽

Yi-Huei Jiang ◽

Ye Deng ◽

Zhou Shi ◽

Benjamin Yamin Zhou ◽

...

Keyword(s):

Microbial Communities ◽

Microbial Ecology ◽

Random Sampling ◽

Sampling Theory ◽

Microbial Community Analysis ◽

Mathematical Framework ◽

Ecological Studies ◽

Microbial Biodiversity ◽

Sequencing Errors ◽

Β Diversity

ABSTRACT The site-to-site variability in species composition, known as β-diversity, is crucial to understanding spatiotemporal patterns of species diversity and the mechanisms controlling community composition and structure. However, quantifying β-diversity in microbial ecology using sequencing-based technologies is a great challenge because of a high number of sequencing errors, bias, and poor reproducibility and quantification. Herein, based on general sampling theory, a mathematical framework is first developed for simulating the effects of random sampling processes on quantifying β-diversity when the community size is known or unknown. Also, using an analogous ball example under Poisson sampling with limited sampling efforts, the developed mathematical framework can exactly predict the low reproducibility among technically replicate samples from the same community of a certain species abundance distribution, which provides explicit evidences of random sampling processes as the main factor causing high percentages of technical variations. In addition, the predicted values under Poisson random sampling were highly consistent with the observed low percentages of operational taxonomic unit (OTU) overlap (<30% and <20% for two and three tags, respectively, based on both Jaccard and Bray-Curtis dissimilarity indexes), further supporting the hypothesis that the poor reproducibility among technical replicates is due to the artifacts associated with random sampling processes. Finally, a mathematical framework was developed for predicting sampling efforts to achieve a desired overlap among replicate samples. Our modeling simulations predict that several orders of magnitude more sequencing efforts are needed to achieve desired high technical reproducibility. These results suggest that great caution needs to be taken in quantifying and interpreting β-diversity for microbial community analysis using next-generation sequencing technologies. IMPORTANCE Due to the vast diversity and uncultivated status of the majority of microorganisms, microbial detection, characterization, and quantitation are of great challenge. Although large-scale metagenome sequencing technology such as PCR-based amplicon sequencing has revolutionized the studies of microbial communities, it suffers from several inherent drawbacks, such as a high number of sequencing errors, biases, poor quantitation, and very high percentages of technical variations, which could greatly overestimate microbial biodiversity. Based on general sampling theory, this study provided the first explicit evidence to demonstrate the importance of random sampling processes in estimating microbial β-diversity, which has not been adequately recognized and addressed in microbial ecology. Since most ecological studies are involved in random sampling, the conclusions learned from this study should also be applicable to other ecological studies in general. In summary, the results presented in this study should have important implications for examining microbial biodiversity to address both basic theoretical and applied management questions.

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A Microbial Perspective on the Grand Challenges in Comparative Animal Physiology

mSystems ◽

10.1128/msystems.00146-17 ◽

2018 ◽

Vol 3 (2) ◽

Cited By ~ 4

Author(s):

Kevin D. Kohl

Keyword(s):

Microbial Communities ◽

Vertical Integration ◽

Temporal Integration ◽

Data Sets ◽

Grand Challenges ◽

Animal Physiology ◽

Physiological Processes ◽

Microbe Interactions ◽

Animal Hosts ◽

Host Microbe Interactions

ABSTRACTInteractions with microbial communities can have profound influences on animal physiology, thereby impacting animal performance and fitness. Therefore, it is important to understand the diversity and nature of host-microbe interactions in various animal groups (invertebrates, fish, amphibians, reptiles, birds, and mammals). In this perspective, I discuss how the field of host-microbe interactions can be used to address topics that have been identified as grand challenges in comparative animal physiology: (i) horizontal integration of physiological processes across organisms, (ii) vertical integration of physiological processes across organizational levels within organisms, and (iii) temporal integration of physiological processes during evolutionary change. Addressing these challenges will require the use of a variety of animal models and the development of systems approaches that can integrate large, multiomic data sets from both microbial communities and animal hosts. Integrating host-microbe interactions into the established field of comparative physiology represents an exciting frontier for both fields.

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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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CAMISIM: Simulating metagenomes and microbial communities

10.1101/300970 ◽

2018 ◽

Cited By ~ 4

Author(s):

Adrian Fritz ◽

Peter Hofmann ◽

Stephan Majda ◽

Eik Dahms ◽

Johannes Dröge ◽

...

Keyword(s):

Microbial Communities ◽

De Novo ◽

Real Data ◽

Small Data ◽

Data Sets ◽

Sequencing Data ◽

Taxonomic Profiling ◽

Benchmark Data ◽

Sequencing Technologies ◽

Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

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Temperature response of denitrification and anammox reveals the adaptation of microbial communities to in situ temperatures in permeable marine sediments that span 50° in latitude

Biogeosciences Discussions ◽

10.5194/bgd-10-14595-2013 ◽

2013 ◽

Vol 10 (9) ◽

pp. 14595-14626 ◽

Cited By ~ 3

Author(s):

A. Canion ◽

J. E. Kostka ◽

T. M. Gihring ◽

M. Huettel ◽

J. E. E. van Beusekom ◽

...

Keyword(s):

Microbial Communities ◽

Marine Sediments ◽

Metabolic Response ◽

Anammox Bacteria ◽

Advective Flow ◽

Permeable Sediments ◽

N Removal ◽

In Situ Conditions ◽

Wide Range

Abstract. Despite decades of research on the physiology and biochemistry of nitrate/nitrite-respiring microorganisms, little is known regarding their metabolic response to temperature, especially under in situ conditions. The temperature regulation of microbial communities that mediate anammox and denitrification was investigated in near shore permeable sediments at polar, temperate, and subtropical sites with annual mean temperatures ranging from −5 to 23 °C. Total N2 production rates were determined using the isotope pairing technique in intact core incubations under diffusive and simulated advection conditions and ranged from 2 to 359 μmol N m−2 d−1. For the majority of sites studied, N2 removal was 2 to 7 times more rapid under advective flow conditions. Anammox comprised 6 to 14% of total N2 production at temperate and polar sites and was not detected at the subtropical site. Potential rates of denitrification and anammox were determined in anaerobic slurries in a temperature gradient block incubator across a temperature range of −1 to 42 °C. The highest optimum temperature (Topt) for denitrification was 36 °C and was observed in subtropical sediments, while the lowest Topt of 21 °C was observed at the polar site. Seasonal variation in the Topt was observed at the temperate site with values of 26 and 34 °C in winter and summer, respectively. The Topt values for anammox were 9 and 26 °C at the polar and temperate sites, respectively. The results demonstrate adaptation of denitrifying communities to in situ temperatures in permeable marine sediments across a wide range of temperatures, whereas marine anammox bacteria may be predominately psychrophilic to psychrotolerant. To our knowledge, we provide the first rates of denitrification and anammox from permeable sediments of a polar permanently cold ecosystem. The adaptation of microbial communities to in situ temperatures suggests that the relationship between temperature and rates of N removal is highly dependent on community structure.

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Microbial dark carbon fixation fueled by nitrate enrichment

10.1101/2021.08.24.457596 ◽

2021 ◽

Author(s):

Joseph H. Vineis ◽

Ashley N. Bulseco ◽

Jennifer L. Bowen

Keyword(s):

Microbial Communities ◽

Marine Sediments ◽

Salt Marshes ◽

Carbon Fixation ◽

Niche Partitioning ◽

Coastal Marine Sediments ◽

Functional Relationships ◽

Coastal Marine ◽

Dark Carbon Fixation ◽

The Impact

Anthropogenic nitrate amendment to coastal marine sediments can increase rates of heterotrophic mineralization and autotrophic dark carbon fixation (DCF). DCF may be favored in sediments where organic matter is biologically unavailable, leading to a microbial community supported by chemoautotrophy. Niche partitioning among DCF communities and adaptations for nitrate metabolism in coastal marine sediments remain poorly characterized, especially within salt marshes. We used genome-resolved metagenomics, phylogenetics, and comparative genomics to characterize the potential niche space, phylogenetic relationships, and adaptations important to microbial communities within nitrate enriched sediment. We found that nitrate enrichment of sediment from discrete depths between 0-25 cm supported both heterotrophs and chemoautotrophs that use sulfur oxidizing denitrification to drive the Calvin-Benson-Bassham (CBB) or reductive TCA (rTCA) DCF pathways. Phylogenetic reconstruction indicated that the nitrate enriched community represented a small fraction of the phylogenetic diversity contained in coastal marine environmental genomes, while pangenomics revealed close evolutionary and functional relationships with DCF microbes in other oligotrophic environments. These results indicate that DCF can support coastal marine microbial communities and should be carefully considered when estimating the impact of nitrate on carbon cycling in these critical habitats.

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Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

Metabarcoding and Metagenomics ◽

10.3897/mbmg.3.36236 ◽

2019 ◽

Vol 3 ◽

Cited By ~ 1

Author(s):

Shruthi Magesh ◽

Viktor Jonsson ◽

Johan Bengtsson-Palme

Keyword(s):

Microbial Communities ◽

Point Mutations ◽

Software Tool ◽

Metagenomic Data ◽

Data Sets ◽

Resistance Mutations ◽

Shotgun Metagenomics ◽

Key Factor ◽

Detection Of Mutations ◽

And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

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