Layered structure of bacterial and archaeal communities and their in situ activities in anaerobic granules.

Abstract

The microbial community structure and spatial distribution of microorganisms and their in situ activities in anaerobic granules were investigated by 16S rRNA gene-based molecular techniques and microsensors for CH(4), H(2), pH, and the oxidation-reduction potential (ORP). The 16S rRNA gene-cloning analysis revealed that the clones related to the phyla Alphaproteobacteria (detection frequency, 51%), Firmicutes (20%), Chloroflexi (9%), and Betaproteobacteria (8%) dominated the bacterial clone library, and the predominant clones in the archaeal clone library were affiliated with Methanosaeta (73%). In situ hybridization with oligonucleotide probes at the phylum level revealed that these microorganisms were numerically abundant in the granule. A layered structure of microorganisms was found in the granule, where Chloroflexi and Betaproteobacteria were present in the outer shell of the granule, Firmicutes were found in the middle layer, and aceticlastic Archaea were restricted to the inner layer. Microsensor measurements for CH(4), H(2), pH, and ORP revealed that acid and H(2) production occurred in the upper part of the granule, below which H(2) consumption and CH(4) production were detected. Direct comparison of the in situ activity distribution with the spatial distribution of the microorganisms implied that Chloroflexi contributed to the degradation of complex organic compounds in the outermost layer, H(2) was produced mainly by Firmicutes in the middle layer, and Methanosaeta produced CH(4) in the inner layer. We determined the effective diffusion coefficient for H(2) in the anaerobic granules to be 2.66 x 10(-5) cm(2) s(-1), which was 57% in water.

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Title
A layered structure of bacterial and archaeal communities and
their in situ activities in anaerobic granules
Author(s)Satoh, Hisashi; Miura, Yuki; Tsushima, Ikuo; Okabe, Satoshi
CitationApplied and Environmental Microbiology, 73(22): 7300-7307
Issue Date2007-11
Doc URLhttp://hdl.handle.net/2115/45291
Right
Typearticle (author version)
Additional
Information
Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP

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A layered structure of bacterial and archaeal communities and
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their in situ activities in anaerobic granules
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Running title: A layered structure in anaerobic granules
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By
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Hisashi Satoh, Yuki Miura, Ikuo Tsushima, and Satoshi Okabe*
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Department of Urban and Environmental Engineering, Graduate school of Engineering,
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Hokkaido University, North-13, West-8, Sapporo 060-8628, Japan.
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* Corresponding Author
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Satoshi OKABE
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Department of Urban and Environmental Engineering, Graduate School of Engineering,
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Hokkaido University, North-13, West-8, Sapporo 060-8628, Japan.
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Tel.: +81-(0)11-706-6266
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Fax.: +81-(0)11-706-6266
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E-mail: sokabe@eng.hokudai.ac.jp
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ABSTRACT
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The microbial community structure and spatial distribution of microorganisms and
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their in situ activities in anaerobic granules were investigated by 16S rRNA gene-based
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molecular techniques and microsensors for CH4, H2, pH, and oxidation-reduction potential
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(ORP). The 16S rRNA gene-cloning analysis revealed that the clones related to the phyla
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Alphaproteobacteria (detection frequency of 51%), Firmicutes (20%), Chloroflexi (9%),
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and Betaproteobacteria (8%) dominated the bacterial clone library and the predominant
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clones in the archaeal clone library were affiliated with Methanosaeta (73%). In situ
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hybridization with the oligonucleotide probes at the phylum level revealed that these
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microorganisms were numerically abundant in the granule. A layered structure of
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microorganisms was found in the granule, where Chloroflexi and Betaproteobacteria were
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present in the outer shell of the granule, Firmicutes was found in the middle layer, and
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aceticlastic Archaea was restricted to the inner layer. Microsensor measurements for CH4,
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H2, pH, and ORP revealed that acid and H2 production occurred in the upper part of the
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granule, below which H2 consumption and CH4 production were detected. Direct
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comparison of the in situ activity distribution with the spatial distribution of the
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microorganisms implied that Chloroflexi contributed to degradation of complex organic
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compounds in the outermost layer, H2 was produced mainly by Firmicutes in the middle
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layer, and Methanosaeta produced CH4 in the inner layer. We could also determine the
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effective diffusion coefficient for H2 in the anaerobic granules to be 2.66 × 10–5 cm2 s–1,
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which was 57% in water.
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INTRODUCTION
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The upflow anaerobic sludge blanket (UASB) reactors are commonly used in the
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treatment of high-strength municipal and industrial wastewaters. Their design permits the
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retention of a greater amount of active biomass (known as granules) in comparison with
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other anaerobic reactors. These anaerobic granules harbor several metabolic groups of
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microorganisms involved in the anaerobic degradation of complex organic compounds,
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including hydrolytic, fermentative, syntrophic, and methanogenic microorganisms. These
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different trophic groups of anaerobes closely and coordinately interact each other within the
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granules and convert complex organic compounds in wastewaters into methane (CH4) and
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carbon dioxide (CO2). The microbial communities of anaerobic granules treating different
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wastewaters have been investigated by culture-independent 16S rRNA gene-based
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molecular analyses (14, 22, 36), which allowed one to obtain more complete inventories of
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microorganisms in anaerobic granules. The results have shown that anaerobic granules
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consisted of a phylogenetically diverse group of microorganisms; however, the majority of
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them have not yet been cultivated (14, 22, 36).
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The application of fluorescence in situ hybridization (FISH) with specific
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oligonucleotide probes further allows us to determine the abundance and in situ spatial
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distribution of specific phylogenetic groups in anaerobic granules (14, 20, 35, 41). By using
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the FISH technique, distinct multi-layered structures of different phylogenetic groups of
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microorganisms have been determined in different anaerobic granules cultivated on
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different substrates (20, 22, 35). These well-organized unique structures are thought to be a
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result of sequential degradation of complex organic compounds by each different trophic
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group within the granules, although it has not yet been confirmed experimentally.
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In addition, spatial distribution of the microorganisms obtained from FISH analysis
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does not necessarily reflect microbial activity distributions. This is because the majority of
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microorganisms have not yet been cultivated, and phylogeny and phenotypes are not
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necessarily congruent with physiology. The spatial distributions of in situ metabolic
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functions in anaerobic granules have been poorly understood mainly due to a lack of
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specific analytical tools with a sufficient spatial resolution. Only a few studies have
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analyzed the distributions of glucose-degrading, fermentative, and sulfate-reducing
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activities in UASB granules by using microsensors for glucose, pH, and hydrogen sulfide
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(H2S) (13, 21, 42). In these studies, CH4 production rates were, however, not directly
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measured with microsensors. Santegoeds et al. (34) used CH4 biosensors and H2S
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microsensors to directly measure in situ methanogenic and sulfate-reducing activities in
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anaerobic granules, and then related them to the spatial distributions of methanogenic and
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sulfate-reducing bacterial populations. They demonstrated a distinct layered structure of
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microbial activities, in which sulfate reduction occurred in the outer layer, whereas CH4
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production was found in the center of the granules. However, they did not investigate the in
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situ distributions of fermentative and syntrophic populations and their activities in the
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granules, and hence the overall conversion mechanism of complex organic compounds to
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CH4 within granules has not been elucidated.
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To investigate the overall anaerobic conversion mechanism of organic compounds to
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CH4 within anaerobic granules, we first analyzed the bacterial and archaeal community
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structures by 16S rRNA gene-cloning analysis, and then the spatial distribution of important
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phylogenetic groups in the granules was determined by fluorescence in situ hybridization
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(FISH). Second, we applied the microsensors for CH4, H2, pH, and oxidation-reduction
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potential (ORP) to determine the spatial distribution of the in situ fermentative
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(H+-producing), syntrophic (H2-producing), and methanogenic activities in the granules.
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The microbial activity distribution was directly compared with the spatial distribution of the
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microorganisms.
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MATERIALS AND METHODS
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Sludge source.
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Anaerobic granular sludge was collected from the bottom of a lab-scale upflow
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anaerobic sludge blanket (UASB) reactor (height, 50 cm; diameter, 5 cm) operated at 35°C.
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The reactor was inoculated with 0.7 liter of anaerobic granular sludge obtained from a
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full-scale UASB reactor treating the wastewater from an isomerized sugar-processing plant.
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The lab-scale reactor was fed with a synthetic medium at an average organic loading rate of
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1.67 g chemical oxygen demand (COD) l–1 day–1 and a hydraulic residence time (HRT) of
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8.2 h. The synthetic medium contained skim powdered milk (1,250 mg l–1) as carbon and
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energy sources, NaHCO3 (1,000 mg l–1), K2HPO4 (50 mg l–1), and the mineral solution (4).
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Granule samples were obtained from the lab-scale UASB reactor after 1 year of operation.
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The water qualities of the effluent during the sampling period were as follows (average ±
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standard deviation, n = 31): COD, 30 ± 9 mg l–1; COD removal rate, 95 ± 1%; ORP, -230 ±
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20 mV; gas production rate, 0.10 ± 0.02 liter h–1; CH4 content in gas, 55 ± 15%; CO2 content
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in gas, 11 ± 4%; N2 content in gas, 34 ± 17%; H2 content in gas, 2300 ± 3300 ppm; acetate,
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165 ± 81 µM; propionate, 77 ± 46 µM; and butyrate, 5.5 ± 7.7 µM. Other volatile fatty acids
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(butyrate, isobutyrate, and formate) were sometimes detected at trace levels.
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DNA extraction and PCR amplification.
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The granule samples were homogenized in the sterilized, distilled water and
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approximately 1.0-ml subsamples were subjected to DNA extraction. Total DNA was
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extracted from the homogenized granules with a FastDNA spin kit for soil (Bio 101;
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Qbiogene, Inc., Carlsbad, CA) as described in the manufacturer’s instructions. 16S rRNA
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gene fragments were amplified from the extracted total DNA with Taq DNA polymerase
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(TaKaRa Bio, Inc., Ohtsu, Japan) by using a primer set of 11f (19) and 1492r (39) for
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bacterial community, and a primer set of A109f (40) and 1492r (39) for archaeal community.
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The PCR products were electrophoresed on a 1% (wt/vol) agarose gel and purified with a
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WIZARD PCR Preps DNA purification system (Promega). To reduce the possible PCR bias,
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the 16S rRNA gene was amplified in 6 to 10 PCR tubes and all tubes were combined for the
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next cloning step.
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Cloning and sequencing of the 16S rRNA genes and phylogenetic analysis.
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The purified PCR products were ligated into a qCR-XL-TOPO vector with a TOPO
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XL PCR cloning kit (Invitrogen, Carlsbad, CA). The ligated products were transformed into
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TOP10-competent Escherichia coli cells (Invitrogen). Plasmids were extracted from the
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cloned cells and purified with a Wizard Plus Minipreps DNA purification system (Promega).
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Nucleotide sequencing was performed with an automatic sequencer (3100 Avant genetic
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analyzer; Applied Biosystems). All sequences were checked for chimeric artifacts by using
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a CHECK_CHIMERA program from the Ribosomal Database Project (24). Partial
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sequences (approximately 500 bp) were compared with similar sequences of the reference
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organisms by a BLAST search (1). Sequences with 97% or greater similarity were grouped
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into operational taxonomic units (OTUs) by a SIMILARITY_MATRIX program from the
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Ribosomal Database Project (24). Nearly complete sequencing of the 16S rRNA gene of
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each representative OTU was performed, and the sequences were aligned with a CLUSTAL
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W package (37). The Molecular Evolutionary Genetics Analysis (MEGA) program, version
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3.1, was used to construct phylogenetic trees based on the neighbor-joining (33) and
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maximum-parsimony methods. Bootstrap resampling analysis of 1,000 replicates was
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performed to estimate the degree of confidence in tree topologies.
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Fixation and cryosectioning of granule samples.
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Granule samples were fixed in 4% paraformaldehyde solution for 8 h at 4°C, washed
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three times with phosphate-buffered saline (10 mM sodium phosphate buffer, 130 mM
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sodium chloride [pH 7.2]), and embedded in Tissue-Tek OCT compound (Sakura Finetek,
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Torrance, CA) overnight to infiltrate the OCT compound into the granule, as described
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previously (29). After rapid freezing at -21°C, 20-µm-thick slices were prepared with a
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cryostat (Reichert-Jung Cryocut 1800, Leica, Bensheim, Germany).
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Fluorescent in situ hybridization.
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In situ hybridization was performed according to the procedure described by Amann
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(2) and Okabe et al. (29). The following 16S and 23S rRNA-targeted oligonucleotide probes
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were used: EUB338 (3), EUB338 II (8), EUB338 III (8), ARC915 (31), ALF968 (28),
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BET42a (25), GNSB-941 (16), HGC69A (32), LGC354A (27), LGC354B (27), LGC354C
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(27), MB1174 (31), MG1200 (31), MS821 (31), and MX825 (31). The probes were labeled
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with fluorescein isothiocyanate (FITC) or tetramethylrhodamine 5-isothiocyanate (TRITC).
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The phylum-specific probes were applied simultaneously with the ARC915 probe specific
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for Archaea. A model LSM510 confocal laser-scanning microscope (CLSM, Carl Zeiss,
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Oberkochen, Germany) equipped with an Ar ion laser (488 nm) and HeNe laser (543 nm)
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was used.
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Microsensor measurements.
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For microsensor measurements, the granules with ca. 2 mm in diameter were selected
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and positioned using five insect needles in the flow cell reactor (4.0 liter) that was filled
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with the synthetic medium used for the lab-scale UASB reactor at 35°C. The medium in the
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flow cell reactor was kept anaerobic by adding a reducing agent (thioglycolic acid at 100 mg
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l–1) and by continuous bubbling with N2, which also resulted in sufficient mixing of the
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medium. The ORP of the medium was ca. -200 mV. An average liquid velocity, judged from
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movement of suspended particles, was ca. 5 mm s–1. The granules were acclimated in the
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medium for at least 6 h before measurement to ensure that steady-state profiles were
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obtained. The in situ steady-state concentration profiles of CH4, H2, pH, and ORP in the
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granules were measured using microsensors as described by Okabe et al. (30). At least three
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concentration profiles were measured for each chemical species. For practical reasons, a
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concentration profile was measured only once in a granule, therefore, concentration profiles
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of different chemical species were measured in different granules.
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Microscale biosensors for CH4 were constructed as described by Damgaard and
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Revsbech (11). Because all measurements were performed under anoxic conditions, an
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oxygen-scavenging guard capillary (12) was not applied. Hence, the CH4 microsensor was
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assembled from an oxygen microsensor, a gas capillary, and a medium capillary. The tip
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diameters of the microsensors were 50-100 µm. A culture of the methane-oxidizing
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bacterium, Methylosinus trichosporium (ATCC 49243), was injected into the medium
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capillary. The principle of the CH4 microsensor is based on a counter diffusion of oxygen
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and CH4. CH4 diffusing through the membrane of the medium capillary is consumed by the
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the methane-oxidizing bacteria with a concomitant decrease of oxygen inside the reaction
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space (i.e., the medium capillary). Oxygen consumed is detected by the internal oxygen
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microsensor and translated into a CH4 partial pressure by calibration. Calibration was
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routinely performed before and after a measurement by placing a CH4 microsensor in a
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calibration chamber (100 ml) into which CH4 and N2 gases were continuously blown at
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known flow rates at 35°C. After the sensor signal stabilized, the signal was monitored and
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the CH4 concentration was measured by a gas chromatography. CH4 concentration was
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changed stepwise by changing the flow rates of CH4 and N2 gases. This procedure was
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repeated over the full range of 0 to 100% CH4 saturation. The response to 0% CH4 saturation
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(i.e., 100% N2 saturation) was typically between 70 and 90 pA, and 90% response times
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were ca. 200 s. The response was linear (r2 > 0.95) over the range of 0 to 100% CH4
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saturation. The slope of the calibration carves was ca. 20 pA per atm of CH4 (see Fig. S1 in
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the supplemental material). Sensor life span was ca. 2 weeks. If the slope of the calibration
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curve was less than 10 pA per atm of CH4, the microsensor was discarded.
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Fig. S1. A typical calibration curve for a microscale biosensor for methane. The solid line
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indicates linear regression. The equation of the straight line was y = -19 x + 90 with r2 =
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0.98.
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Polarographic H2 microsensors were constructed as described by Ebert and Brune (15).
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The tip diameters of the microsensors were ca. 10 µm, 90% response times were less than 2
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s, and the detection limit was ca. 1 µM. Calibration was routinely performed by immersing
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a microsensor in a calibration chamber filled with the synthetic medium which continuously
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bubbled with H2 and N2 gases. The H2 concentration in the medium was measured by a gas
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chromatography. H2 concentration was changed stepwise by changing the flow rates of H2
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and N2 gases. ORP microsensors, which were made from a platinum wire coated with a glass
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micropipette, were constructed and calibrated as described by Jang et al. (18). All ORP data
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reported in this paper were the potential difference measured between an ORP microsensor
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and the Ag/AgCl reference electrode. Potentiometric pH microsensors were constructed,
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calibrated, and used according to the protocol as described by Okabe et al. (29).
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Microbial activity calculations.
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Net volumetric CH4 and H2 production rates (R(CH4) and R(H2), respectively) in the
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granule were estimated from the steady-state concentration profiles of CH4 and H2 by using
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Fick’s second law of diffusion as previously described by Santegoeds et al. (34) and
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Lorenzen et al. (23). Furthermore, the total H2 production rate from the granule was
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calculated using Fick's first law of diffusion (29). Molecular diffusion coefficients (Dw 25) of
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1.49 × 10–5 cm2 s–1 for CH4 and 4.50 × 10–5 cm2 s–1 for H2 in water at 25°C were used for the
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calculations (7). Molecular diffusion coefficients in water at 35°C (Dw 35) were calculated
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according to the Stokes-Einstein relationship (7). Furthermore, effective diffusion
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coefficient (Deff) of these compounds in the granules were estimated by correcting Dw with
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the ratio of the diffusivities in granules and in water (57%), as determined with the H2
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microsensor in this study (see Discussion).
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Estimation of effective diffusion coefficient (Deff).
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Granules with a diameter of ca. 2 mm were selected and deactivated in pure
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chloroform for 10 min. After being rinsed, the deactivated granule was mounted in the flow
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cell reactor containing the synthetic medium for microsensor measurements at 35°C, and a
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H2 microsensor was positioned in the center of the granule with a micromanipulator. After
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preincubation for at least 30 min, H2 gas was bubbled. The evolution of the H2 concentration
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in the center of the granule was monitored over time in triplicate, and the average Deff was
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calculated according to the protocol as described by Cronenberg and van den Heuvel (6).
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Chemical analyses.
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The concentrations of chemical oxygen demand (COD) were determined according to
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the standard methods (5). Volatile fatty acids were determined by high-performance liquid
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chromatography (LC-10AD system; Shimadzu Co., Kyoto, Japan) equipped with a
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Shimadzu Shim-pack SCR-102H column (0.8 by 30 cm) after filtration with 0.2-µm-pore
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size membranes (Advantec Co., Ltd., Tokyo, Japan). CH4, H2, and CO2 in gaseous samples
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were determined by gas chromatography (GC-14B; Shimadzu Co., Kyoto, Japan) equipped
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with a thermal-conductivity detector and a Shincarbon-ST column (Shimadzu Co., Kyoto,
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Japan). ORP and pH were directly determined using an ORP and a pH electrode,
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respectively.
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Nucleotide sequence accession numbers.
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The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequences of
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the clones used for the phylogenetic analysis are AB329636 to AB329664.
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Results
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Bacterial and archaeal community structures.
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16S rRNA gene clone libraries of Bacteria and Archaea were constructed from the
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anaerobic granule samples taken from the UASB reactor. From the Bacteria clone library
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105 clones were randomly selected and the partial sequences of about 500 bp were analyzed.
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In total, the clones were grouped into 25 OTUs on the basis of more than 97% sequence
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similarity within an OTU. Then, a nearly complete 16S rRNA gene sequence of a
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representative clone of each OTU was analyzed. The phylogenetic trees were constructed by
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neighbor-joining and maximum-parsimony methods, and both methods resulted in
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essentially the same topology. The phylogenetic tree inferred by the neighbor-joining
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method is shown in Fig. 1. The distribution of cloned sequences among phyla is as follows:
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Alphaproteobacteria, 51%; Firmicutes, 20%; Chloroflexi, 9%; Betaproteobacteria, 8%;
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Actinobacteria, 4%; Deltaproteobacteria, 3%; Cyanobacteria, 2%; Gammaproteobacteria,
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2%; Epsilonproteobacteria, 1%; and Bacteroidetes, 1%. The most frequently detected
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clones (40 of 105 clones) were represented by OTU 8, which was closely related to the
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Sphingomonas rhizogenes (99.8% sequence similarity).
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Fig. 1. Phylogenetic tree representing the affiliation of the 16S rRNA clone sequences of
Bacteria retrieved from granule samples (OTU#). The tree was generated by using nearly
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full length of 16S rRNA gene sequences and the neighbor-joining method. The scale bar
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represents 5% estimated divergence. The numbers at the nodes are bootstrap values
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(1,000 replicates) with more than 50% bootstrap support.
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