Development of a "genome-proxy" microarray for profiling marine microbial communities, and its application to a time series in Monterey Bay, California

AbstractAlthough numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data (http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

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Time-series analyses of Monterey Bay coastal microbial picoplankton using a ‘genome proxy’ microarray

Environmental Microbiology ◽

10.1111/j.1462-2920.2010.02314.x ◽

2010 ◽

Vol 13 (1) ◽

pp. 116-134 ◽

Cited By ~ 24

Author(s):

Virginia I. Rich ◽

Vinh D. Pham ◽

John Eppley ◽

Yanmei Shi ◽

Edward F. DeLong

Keyword(s):

Time Series ◽

Monterey Bay ◽

A Genome ◽

Time Series Analyses

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Enhanced CO2 concentrations change the structure of Antarctic marine microbial communities

Marine Ecology Progress Series ◽

10.3354/meps11742 ◽

2016 ◽

Vol 552 ◽

pp. 93-113 ◽

Cited By ~ 27

Author(s):

AT Davidson ◽

J McKinlay ◽

K Westwood ◽

PG Thomson ◽

R van den Enden ◽

...

Keyword(s):

Microbial Communities ◽

Co2 Concentrations ◽

Antarctic Marine ◽

Marine Microbial Communities

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Faculty Opinions recommendation of Design and testing of 'genome-proxy' microarrays to profile marine microbial communities.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1099318.555548 ◽

2008 ◽

Author(s):

Jacques Ravel

Keyword(s):

Microbial Communities ◽

Marine Microbial Communities ◽

Design And Testing

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A time series investigation of the oxygen isotopic composition of dissolved inorganic phosphate in Monterey Bay, California

Limnology and Oceanography ◽

10.4319/lo.2006.51.5.2370 ◽

2006 ◽

Vol 51 (5) ◽

pp. 2370-2379 ◽

Cited By ~ 15

Author(s):

Karen McLaughlin ◽

Francisco Chavez ◽

J. Timothy Pennington ◽

Adina Paytan

Keyword(s):

Time Series ◽

Isotopic Composition ◽

Inorganic Phosphate ◽

Oxygen Isotopic Composition ◽

Monterey Bay ◽

Oxygen Isotopic

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Extraction of Prokaryotic Genomic DNA from Marine Microbial Communities Suitable for Amplification Using the Polymerase Chain Reaction

Internationale Revue der gesamten Hydrobiologie und Hydrographie ◽

10.1002/iroh.19950800222 ◽

1995 ◽

Vol 80 (2) ◽

pp. 351-360 ◽

Cited By ~ 1

Author(s):

James O. McInerney ◽

Lynn Paskins ◽

Donal Eardly ◽

John W. Patching ◽

Richard Powell

Keyword(s):

Polymerase Chain Reaction ◽

Microbial Communities ◽

Genomic Dna ◽

Chain Reaction ◽

Polymerase Chain ◽

Marine Microbial Communities

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An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

10.1101/080697 ◽

2016 ◽

Author(s):

Kenta Suzuki ◽

Katsuhiko Yoshida ◽

Yumiko Nakanishi ◽

Shinji Fukuda

Keyword(s):

Time Series ◽

Microbial Communities ◽

Microbial Processes ◽

Interaction Networks ◽

List Type ◽

Metagenomic Sequencing ◽

Ecological Interactions ◽

Microbial Interaction ◽

Ecological Interaction ◽

Dataset Size

AbstractMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.We developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.The complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

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Patterns of extracellular enzyme activities among pelagic marine microbial communities: implications for cycling of dissolved organic carbon

Aquatic Microbial Ecology ◽

10.3354/ame038135 ◽

2005 ◽

Vol 38 ◽

pp. 135-145 ◽

Cited By ~ 55

Author(s):

C Arnosti ◽

S Durkin ◽

WH Jeffrey

Keyword(s):

Organic Carbon ◽

Dissolved Organic Carbon ◽

Microbial Communities ◽

Enzyme Activities ◽

Extracellular Enzyme ◽

Extracellular Enzyme Activities ◽

Marine Microbial Communities

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Time-series genome-centric analysis unveils bacterial response to operational disturbance in activated sludge

10.1101/565770 ◽

2019 ◽

Cited By ~ 1

Author(s):

María Victoria Pérez ◽

Leandro D. Guerrero ◽

Esteban Orellana ◽

Eva L. Figuerola ◽

Leonardo Erijman

Keyword(s):

Growth Rate ◽

Time Series ◽

Wastewater Treatment ◽

Activated Sludge ◽

Microbial Communities ◽

Wastewater Treatment Plant ◽

Treatment Plant ◽

Operating Conditions ◽

The Face

ABSTRACTUnderstanding ecosystem response to disturbances and identifying the most critical traits for the maintenance of ecosystem functioning are important goals for microbial community ecology. In this study, we used 16S rRNA amplicon sequencing and metagenomics to investigate the assembly of bacterial populations in a full-scale municipal activated sludge wastewater treatment plant over a period of three years, including a period of nine month of disturbance, characterized by short-term plant shutdowns. Following the reconstruction of 173 metagenome-assembled genomes, we assessed the functional potential, the number of rRNA gene operons and thein situgrowth rate of microorganisms present throughout the time series. Operational disturbances caused a significant decrease in bacteria with a single copy of the ribosomal RNA (rrn) operon. Despite only moderate differences in resource availability, replication rates were distributed uniformly throughout time, with no differences between disturbed and stable periods. We suggest that the length of the growth lag phase, rather than the growth rate, as the primary driver of selection under disturbed conditions. Thus, the system could maintain its function in the face of disturbance by recruiting bacteria with the capacity to rapidly resume growth under unsteady operating conditions.IMPORTANCEIn this work we investigated the response of microbial communities to disturbances in a full-scale activated sludge wastewater treatment plant over a time-scale that included periods of stability and disturbance. We performed a genome-wide analysis, which allowed us the direct estimation of specific cellular traits, including the rRNA operon copy number and the in situ growth rate of bacteria. This work builds upon recent efforts to incorporate growth efficiency for the understanding of the physiological and ecological processes shaping microbial communities in nature. We found evidence that would suggest that activated sludge could maintain its function in the face of disturbance by recruiting bacteria with the capacity to rapidly resume growth under unsteady operating conditions. This paper provides relevant insights into wastewater treatment process, and may also reveal a key role for growth traits in the adaptive response of bacteria to unsteady environmental conditions.

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