FEMS Microbiology Ecology, 92, 2016, fiw087
doi: 10.1093/femsec/fiw087
Advance Access Publication Date: 27 April 2016
Research Article
RESEARCH ARTICLE
Natural decay process affects the abundance
and community structure of Bacteria and Archaea
in Picea abies logs
J. M. Rinta-Kanto1,∗ , H. Sinkko1 , T. Rajala2,† , W. A. Al-Soud3 , S. J. Sørensen3 ,
M. V. Tamminen4 and S. Timonen1
1
University of Helsinki, Department of Food and Environmental Sciences, Division of Microbiology, Viikinkaari
9, 00014 Helsinki, Finland, 2 Natural Resources Institute Finland, Jokiniemenkuja 1, 01370 Vantaa, Finland,
3
Department of Biology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen Ø, Denmark and
4
Swiss Federal Institute of Technology Zurich, Universitätstrasse 8-22, 8006 Zurich, Switzerland
∗
Corresponding author: University of Helsinki, Department of Food and Environmental Sciences, Division of Microbiology, Viikinkaari 9, 00014 Helsinki,
Finland. Tel: +358-2941-59283; E-mail: [email protected]
†
This paper is dedicated to the memory of Tiina Rajala who passed away in August of 2015.
One sentence summary: Decaying Norway spruce logs provide habitat for diverse communities of Bacteria and Archaea.
Editor: Wietse de Boer
ABSTRACT
Prokaryotes colonize decaying wood and contribute to the degradation process, but the dynamics of prokaryotic
communities during wood decay is still poorly understood. We studied the abundance and community composition of
Bacteria and Archaea inhabiting naturally decaying Picea abies logs and tested the hypothesis that the variations in archaeal
and bacterial abundances and community composition are coupled with environmental parameters related to the decay
process. The data set comprises >500 logs at different decay stages from five geographical locations in south and central
Finland. The results show that Bacteria and Archaea are an integral and dynamic component of decaying wood biota. The
abundances of bacterial and archaeal 16S rRNA genes increase as wood decay progresses. Changes in bacterial community
composition are clearly linked to the loss of density of wood, while specific fungal–bacterial interactions may also affect the
distribution of bacterial taxa in decaying wood. Thaumarchaeota were prominent members of the archaeal populations
colonizing decaying wood, providing further evidence of the versatility and cosmopolitan nature of this phylum in the
environment. The composition and dynamics of the prokaryotic community suggest that they are an active component of
biota that are involved in processing substrates in decaying wood material.
Keywords: Picea abies; Bacteria; Archaea; abundance; sequencing
INTRODUCTION
Lignin and cellulose are degraded through the actions of fungi
and prokaryotes during the wood decay process, resulting in
gradual changes in the chemical and physical quality of wood
(Clausen 1996; Rajala et al. 2012). Ultimately the degradation pro-
cess leads to the release of carbon and other nutrients from the
tree biomass back into the food web. Wood decay plays an important role in the global carbon cycle: it is a major pathway of
releasing sequestered carbon from forests into the atmosphere.
Between the years 1990 and 2005 in European forests, 533
Received: 22 September 2015; Accepted: 24 April 2016
C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
teragrams of carbon, corresponding to about 70% of sequestered
carbon, was released through wood decay (Bellassen and Luyssaert 2014). A thorough understanding of the biotic components
involved in wood decomposition is essential, for example, for
evaluating the effects of forest management practices in carbon
cycling.
Diverse communities of Bacteria colonize decaying wood
(Clausen 1996; Lynd et al. 2002; Zhang, Yang and Tu 2008;
Valaskova et al. 2009; Hervé et al. 2014; Sun et al. 2014; Hoppe et al.
2015). Fungi are considered as the primary decomposers of wood
due to their capability of producing enzymes to degrade the major wood polymers lignin, cellulose and hemicellulose (Clausen
1996; Noll and Jirjis 2012). Despite bactericidal compounds secreted by some ligninolytic fungi (Valaskova et al. 2009), bacterial
colonization generally takes place early on in the decay process
(Clausen 1996; de Boer et al. 2005). Wood decomposing Bacteria live in close interaction with fungi, and they may contribute
to the wood decay process by degrading the major wood polymers cellulose and lignin (Clausen 1996; Lynd et al. 2002; Brown
and Chang 2014), and by processing low molecular mass sugars
and small aromatic compounds that are released by ligninocellulolytic fungi (Valaskova et al. 2009; Noll and Jirjis 2012). Bacteria
are able to compete with fungi for resources because bacterial
degradation of wood polymers and uptake of fungal metabolites are not necessarily dependent on the availability of oxygen (Schellenberger, Kolb and Drake 2010). Intact wood is rich
in carbohydrates not readily available to many bacteria and is
poor in nitrogen, which may limit decomposition because bacterial and fungal enzyme complexes require a high amount of
nitrogen in their structure (Noll and Jirjis 2012). Nitrogen-fixing
Bacteria provide a vital supply of nitrogen for wood-degrading
prokaryotes and fungi in an otherwise nitrogen-poor environment (Weisshaupt, Pritzkow and Noll 2011; Hoppe et al. 2014).
Thus far, the abundance of wood-colonizing Bacteria has not
been assessed in decaying wood, and only a few studies have
addressed the composition of bacterial communities in naturally decaying deadwood in the environment (Zhang, Yang and
Tu 2008; Hoppe et al. 2014, 2015). Hoppe et al. (2015) showed by
comparing microbial communities of decaying Picea abies and
Fagus sylvatica logs that microbial communities are shaped by
the host tree species as well as by some physico-chemical properties of the substrate, such as remaining mass, moisture, pH
and C/N ratio.
Archaeal colonization of living tree tissue by methanogenic
Archaea was initially documented over 40 years ago (Zeikus and
Ward 1974; Zeikus and Henning 1975). After the initial findings,
reports of methane emissions from living trees have further
supported the idea of wood-colonizing methanogenic Archaea
(Mukhin and Voronin 2008; Beckmann et al. 2011), albeit some
saprotrophic fungi may also be contributing to methane emissions either directly or by hosting Archaea (Lenhart et al. 2012).
So far, non-methanogenic Archaea have been found in decaying timber in an abandoned mine (Beckmann et al. 2011), but
their presence in natural wood has not been widely studied.
Methane of fungal or archaeal origin is hypothesized to serve
as a substrate in methylotrophic bacterial communities found
in decaying wood (Folman et al. 2008; Lenhart et al. 2012; Hoppe
et al. 2015). To our knowledge, studies assessing the abundance
of Archaea or composition of archaeal communities in deadwood have not been published to date. The broader role of
Archaea in the wood decay process and in wood-inhabiting
microbial communities remains to be elucidated through genomic studies. For example, cellulase and xylanase enzymes
have been discovered in extremophilic Archaea (Wainø and
Table 1. Sampling sites and number of pooled DNA samples analysed
per site.
Sampling
site
Location
Sample
code
Number of pooled
DNA samples per site
Sipoo
Lapinjärvi
Loppi
Petäjäjärvi
Vesijako
60◦ 28 N, 25◦ 12 E
60◦ 39 N, 26◦ 7 E
60◦ 48 N, 24◦ 10 E
61◦ 55 N, 23◦ 35 E
61◦ 21 N, 25◦ 7 E
SI
LJ
LO
PJ
VJ
10
12
12
8
10
Ingvorsen 2003; Graham et al. 2011), but it is not known whether
these enzymes can also be found in temperate Archaea.
The study presented here is the first one to estimate bacterial and archaeal abundance, and to identify taxonomic groups
of wood-colonizing archaea in naturally decaying P. abies logs.
The purpose of this study was to investigate variations in
archaeal and bacterial abundances and community composition in relation to specific environmental parameters related to the decay process. We tested the hypotheses that the
abundance and community structure of Bacteria and Archaea
are related to substrate quality (density, content of nitrogen,
lignin, water and ethanol extractives) and to fungal community structure in dead P. abies logs at different stages of natural
decay.
MATERIALS AND METHODS
Collection of wood samples, DNA extraction
and physical–chemical analyses
Wood samples from decaying Picea abies logs were collected
from five spruce-dominated, unmanaged forest sites in autumn of 2008 and 2009 as described by Rajala et al. (2012)
(Table 1). Because the natural decay of spruce logs takes 6–8
decades in the boreal climate zone (Mäkinen et al. 2006), samples were collected at the same time from logs representing
equally all different decay stages (Mäkinen et al. 2006; Rajala et al.
2012).
DNA was extracted from the wood samples with the
E.Z.N.A.TM SP Plant DNA Mini kit (Omega Bio-tek, Inc., USA) (Rajala et al. 2010, 2012). Homogenization and cell lysis were carried
R cell disrupter (Qbiogene, France) and inout using a FastPrep
cubating at 65◦ C for 60 min. Otherwise, the manufacturer’s instructions were followed. If needed, DNA samples were further
purified with PEG precipitation (Vainio, Korhonen and Hantula
1998). Fungal taxonomy and community analyses were completed in previous studies (Rajala et al. 2012). Physical and chemical analyses for wood samples to determine density, content of
nitrogen, lignin, water and ethanol extractives, as well as the
analysis of fungal diversity were performed using methods described in detail in Rajala et al. (2012). Initially, one DNA sample originated from one log, and these individual DNA samples were sorted according to wood density within each study
site. Subsequently, 10 DNA samples with similar wood densities
were pooled (Supplementary Fig. S1). Thus, each DNA sample
analysed in this study represents 10 logs, so the whole data set
comprised 520 logs. Environmental parameters associated with
pooled samples represent the mean values of corresponding
parameters from the individual DNA samples (Supplementary
Table S1).
Rinta-Kanto et al.
qPCR
The abundances of 16S rRNA genes of Bacteria, Archaea and
group 1.1c Thaumarchaeota in wood samples were determined
using quantitative PCR (qPCR). All qPCR reactions were run in
triplicate, and control reactions, where DNA template was replaced with an equal volume of ultrapure water, were run in
duplicate. For eubacterial 16S rRNA gene quantification, 25 μL
PCR reactions consisted of 1× Maxima SYBR Green Master Mix
(Thermo Fisher Scientific), 0.3 μM (final concentration) of each
primer Eub338 and Eub518 (Fierer et al. 2005), 5 μL of diluted
template DNA and ultrapure water up to 25 μL. For generating
the standard curve, a 16S rRNA gene fragment was PCR amplified from DNA extracted from a pure culture of Burkholderia glathei using primers 25F and 1492R (Hurek, Wagner and
Reinhold-Hurek 1997). The fragment was ligated into a pJet
2.1 cloning vector (Thermo Scientific) and cloned using standard procedures outlined in GeneJet cloning kit (Thermo Scientific). Plasmid DNA was purified using GeneJet Plasmid Miniprep
Kit (Thermo Scientific) and quantified with a Nanodrop spectrophotometer (Thermo Scientific). A 10-fold dilution series of
the plasmid, ranging from 3 × 106 to 30 copies per reaction,
was used to generate a standard curve. For archaeal 16S rRNA
gene quantification, each qPCR reaction mixture consisted of 1×
Maxima SYBR Green Master Mix, 0.9 μM (final concentration)
of each primer Arch349F and Arch806R (Takai and Horikoshi
2000), 5 μL of diluted template DNA and nuclease-free water up to 20 μL. To quantify group 16S rRNA gene copies of
1.1c Thaumarchaeota, we used primer combination FFS-Uni 5 AGGAGAGATGGCTTAAAGGGG-3 (Jurgens and Saano 1999) and
385R 5 -GGATTAACCTCRTCACGCTTTCG-3 (Lehtovirta, Prosser
and Nicol 2009). Quantitative PCR was run in 25 μL reactions consisting of 1× Maxima SYBR Green Master Mix, 0.8 μM (final concentration) of each primer, 5 μL of template DNA and ultrapure
water up to 25 μL.
The qPCR program for archaeal and thaumarchaeotal assays
consisted of initial denaturation at 95◦ C for 10 min, 40 cycles of
95◦ C for 15 s, and 60◦ C for 1 min followed by a melting curve
analysis. Standard curves were generated using a dilution series of a commercially prepared plasmid consisting of a vector
pUC57 (length 2710 bp) and an 894 bp insert (GenScript), which
was synthetized according to the DNA sequence of a 16S rRNA
gene fragment belonging to an uncultivated 1.1c-group Thaumarchaeon (NCBI accession number AM903348.1). The concentrations of standards ranged from 3 × 106 to 3 × 102 copies per reaction. Quantitative PCR reactions were run and data were initially
collected and analysed using ABI 7300 (Life Technologies) and
Bio-Rad CFX96 detection system (Bio-Rad Laboratories) instruments and the instruments’ software. All qPCR products were
verified by checking the melt curves and by running one of the
triplicate reactions on an ethidium bromide (0.2 μg ml−1 ) stained
1.2% agarose gel.
Sequencing
Prokaryotic community composition was studied through sequencing of qPCR products (prepared as described above). Samples, which had detectable quantities of archaeal 16S rRNA
genes, representing high, medium and low densities of wood
from each sampling area, were selected for sequencing (total
of 12 samples). The 454-sequencing adapters and barcodes of
10 nucleotides, which were recommended by Roche for building and sequencing of DNA fragments, were ligated into purified
3
qPCR products in secondary PCR reactions. Reactions consisted
of 2 μL of purified original PCR product, 1× Maxima SYBR Green
qPCR Master Mix, 0.6 μM of each fusion primer and sterile water
up to 25 μL. PCR was run at 95◦ C for 10 min, 20 cycles of 95◦ C
for 15 s, 56◦ C for 1 min, followed by 72◦ C for 5 min and 70◦ C
for 3 min. After termination of the program, the reactions were
transferred immediately on ice. The PCR products were purified
by running them on a 1.5% agarose gel prepared with 1× TAE
buffer and stained with ethidium bromide (0.2 μg ml−1 ). DNA
bands were excised from the gel and purified using GeneJET gel
extraction kit (Thermo Scientific). DNA was quantified using a
Qubit 2.0 fluorometer with Qubit dsDNA HS kit (Life Technologies, Thermo Fisher Scientific). DNA libraries prepared from different samples were pooled at equimolar concentration (4 × 106
copies μL–1 ). The samples were sequenced on one of two regions
of a Titanium 70 × 75 PicoTiterPlate using a GS FLX sequencing system (Roche, Germany) following the manufacturer’s
instructions.
Analysis of sequences
Sequences were analysed using Qiime, version 1.8 (Caporaso
et al. 2010b). Raw 454-sequencing reads were denoised using denoise wrapper (Reeder and Knight 2010) and quality filtered with
split libraries fastq.py command using the following parameters for Bacteria: minimum length of sequences 160 nucleotides,
0 errors in barcodes, maximum 2 mismatches to primer sequences, maximum 6 ambiguous bases, maximum length of a
homopolymer 6 nucleotides; and for Archaea: minimum length
of sequences 360 nucleotides, 0 errors in barcodes, maximum
2 mismatches to primer sequences, maximum 6 ambiguous
bases, maximum length of a homopolymer 8 nucleotides. Reverse primers were located and removed, along with any sequence following them. Trimmed reads passing quality filtering were clustered into OTUs using pick open reference otus.py
workflow command with reverse strand matching. Sequence
clustering was done at 97% similarity level using the Uclust
algorithm (Edgar 2010). Representative sequences from each
OTU were aligned using the PyNAST algorithm (Caporaso et al.
2010a) against Greengenes core reference alignment (DeSantis
et al. 2006) and checked for presence of chimeras using Chimera
Slayer (Haas et al. 2011). Taxonomic classification for putative
bacterial sequences was done using RDP classifier (Wang et al.
2007) against Greengenes reference database, version 13.8 (McDonald et al. 2012; Werner et al. 2012). The same OTU picking procedure was used for putative archaeal sequences, except taxonomic classification was done using the BLAST algorithm (Altschul et al. 1990) and Silva database, release 111 as
a reference database (Pruesse et al. 2007). For calculating richness (observed species) a rarefaction was done at 895 sequences
for bacterial 16S sequence libraries and at 251 sequences for archaeal 16S libraries in Qiime 1.8, using the single rarefaction.py
command. Even sampling at the selected depth for archaeal sequence libraries eliminated three samples with ≤251 sequences
from the data set. Richness estimates were calculated using alpha diversity.py command in Qiime v. 1.8.
Nucleotide sequence accession numbers
Raw sequence data have been deposited at the National Center of Biotechnology Information’s Sequence Read Archive under
BioProject PRJNA285725.
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
Data analyses
Generalized linear mixed-effects modeling was used to model
the relationships between bacterial and archaeal 16S rRNA gene
copy abundances (n = 51) and wood density. The model, in which
the number of 16S rRNA gene copies was fitted with density as
a fixed effect, and density and sampling area as random effects,
was constructed in the R statistical environment (R Core Team
2014) using the function glmer in the package lme4 (Bates et al.
2014). A generalized linear mixed model was also used to identify whether selected environmental factors (content of nitrogen, ethanol and water extractives, lignin and fungal richness)
had an effect on the 16S gene copy abundances of Bacteria and
Archaea (n = 51). The models were constructed in R environment, using the function glm in the package Stats (R Core Team
2014). The same method was also used to analyse correlation
between bacterial and archaeal OTU richness and wood density.
Relationships between prokaryotic community composition
and wood density as well as other environmental parameters
were analysed by fitting gradients of individual parameters with
community data, using non-parametric smoothing. The parameters were fitted one by one with the first and second principal
coordinate scores, which were produced by principal coordinate
analysis (PCoA) of the square root transformed 16S rRNA gene
sequence abundances. The fitting of the parameters was performed using the function ordisurf in the R package vegan (Oksanen et al. 2015). The relationship between community composition and wood density was also analysed through comparison
of Bray–Curtis distances using the Mantel test, with command
compare distance matrices.py in Qiime version 1.8 (Caporaso
et al. 2010b).
Non-parametric smoothing was also used to study the relationships of bacterial and fungal taxa along the wood density
gradient. Additionally, Spearman rank correlations were calculated between relative abundances of fungal orders and bacterial classes in each sample to indicate potential patterns in
co-occurrence of bacterial and fungal groups. A heatmap with
hierarchical clustering was generated to visualize the correlation coefficients using heatmap.2 function in gplots package in
R program (Warnes et al. 2015). Spearman rank correlations were
also calculated between relative wood density and relative abundance of fungal orders (based on the abundance of taxonomically classified fungal OTUs in each sample, determined in Rajala et al. 2012) and bacterial classes in each sample.
In all statistical analyses, P-values less than or equal to 0.05
were considered as significant. Details of data analyses can be
found in Supplementary data.
RESULTS
Abundance of bacterial and archaeal 16S sequences
in wood samples
Bacterial 16S rRNA gene sequences were detected by qPCR in
all studied wood samples, except in one high-density sample
(Supplementary Table S1). The greatest abundances of bacterial
16S rRNA gene copies were found among samples with the lowest densities (most decayed), up to 5 × 1010 copies g−1 , while
abundances as low as 5.8 × 106 copies g−1 were present in highdensity (least decayed) samples (Fig. 1a). Archaeal 16S rRNA gene
copy abundances in wood samples ranged from 3.5 × 107 to 3.1
× 105 copies g−1 (Fig. 1b). Similar to Bacteria, the highest archaeal 16S rRNA gene copy abundances were found among the
most decayed, and the lowest in the least decayed wood sam-
Figure 1. The effect of wood density on bacterial (a) and archaeal (b) 16S gene
copy abundance. Original (unscaled values) are depicted in the graph. Sampling
sites are indicated with different symbols and the colored lines represent fitted
regression lines for each sampling area. Parameters of generalized linear mixed
model for Bacteria (a): b (slope) for density (fixed effect): –13.121 (SE: 0.013), variance for the slope across the sampling areas (random effect) 9.667 (SD: 3.109).
Corresponding parameters for Archaea (b): slope (b) for fixed effect (density):
–33.564 (SE: 0.332) and for random effect: 1137.7 (SD: 33.729). For model calculations, qPCR data were scaled down 100-fold. Each qPCR quantity, entered into
the model, is a mean quantity of three technical replicate reactions.
ples. Archaeal 16S rRNA gene copies were undetectable or their
quantity was below the detection limit (approximately 3 × 104
copies g−1 ) of the qPCR assay in 22 out of 34 samples with densities ≥0.204 kg dm−3 (Supplementary Table S1). Quantifiable
amounts of Thaumarchaeota group 1.1c 16S rRNA gene fragments
were detected only in the most decayed wood samples, at densities ≤0.209 kg dm−3 , with abundances varying from 7.5 × 105
to 2.0 × 107 copies g−1 (Supplementary Table S1).
Rinta-Kanto et al.
Table 2. Generalized linear mixed model analysis relating environmental parameters to bacterial 16S rRNA gene copy numbers.
Main effects
Interactions
Variable
Slope (SE)
Nitrogen (N)
Ethanol
extractives (EE)
Fungal diversity
(FD)
7.739 (2.120)
0.394 (0.358)
0.000751
0.278100
− 2.340 (0.715)
0.002203
8.301 (1.938)
0.545 (0.153)
0.628 (0.171)
0.000112
0.000990
0.000676
N:FD
EE:FD
FD:Water
extractives (WE)
N:EE:FD
N:FD:WE
EE:FD:WE
N:EE:FD:WE
− 1.575 (0.359)
− 1.806 (0.450)
− 0.129 (0.037)
0.329 (0.086)
P
8.23 × 10−5
0.000254
0.001274
0.000424
Table 3. Generalized linear model analysis relating environmental
parameters to archaeal 16S rRNA gene copy numbers.
Variable
Main effects
Interactions
Nitrogen (N)
Ethanol
extractives (EE)
Water
extractives (WE)
Fungal diversity
(FD)
N:EE
N:WE
EE:WE
N:FD
EE:FD
WE:FD
N:EE:WE
N:EE:FD
N:WE:FD
N:EE:WE:FD
Slope (SE)
P
615.904 (169.077)
10.586 (2.990)
0.000843
0.001124
19.780 (6.075)
0.002464
8.168 (2.849)
0.006890
− 82.958 (22.870)
− 12.824 (36.437)
− 1.552 (0.697)
− 57.218 (18.787)
− 0.421 (0.232)
− 1.267 (0.536)
16.121 (4.808)
7.412 (2.532)
11.337 (3.959)
− 1.416 (0.507)
0.000880
0.001942
0.032187
0.004327
0.078010
0.023464
0.001893
0.005887
0.006946
0.008331
5
A generalized linear mixed model determined that bacterial
16S rRNA gene copy abundance increased significantly along
with decreasing wood density (P < 2 × 10−16 ) (Fig. 1a). In this
model, density alone (fixed effect) explains 56% of variation in
bacterial 16S rRNA copy numbers. When the variation in density
in different sampling areas and the effect of sampling area on
the number of bacterial 16S rRNA gene copy numbers (random
effects) are accounted for, the model explains 63% of the variation. A generalized linear mixed model for the effect of wood
density on archaeal 16S rRNA gene copy abundance showed that
density alone explains 53% of variation in archaeal 16S gene
copy abundance (P < 2 × 10−16 ) (Fig. 1b). When the variation in
density in different sampling areas and the effect of sampling
area on the number of archaeal 16S rRNA gene copy numbers
are taken into account, the model explains 64% of the variation.
A generalized linear mixed model was also used to test
whether the selected environmental factors—content of nitrogen, ethanol extractives, water extractives and fungal
diversity—affect the 16S gene copy abundances of Bacteria and
Archaea. The final model indicated that nitrogen content and
fungal diversity were factors that correlated with the bacterial 16S rRNA gene abundance most. Fungal diversity, together
with the concentration of ethanol extractives, water extractives
and nitrogen, form statistically significant 2-, 3- and 4-way interactions with bacterial 16S rRNA gene abundance (Table 2).
This model explains 76% of variation in the observed bacterial
16S rRNA gene abundances. The final model testing the effect
of environmental parameters of archaeal 16S rRNA gene abundance data showed that all individual parameters as well as their
combinations in 2-, 3-, and 4-way interactions (except the 2way interaction with ethanol extractives and fungal diversity)
were statistically significant (Table 3), explaining 67% of variation in abundances of archaeal 16S rRNA gene abundances in our
samples.
Prokaryotic community composition in wood samples
The 454-sequencing of bacterial 16S gene fragments yielded 16
535 reads that were classified as Bacteria (Table 4). The highest
percentage of taxonomically classified bacterial 16S sequences
belonged to the classes of Alphaproteobacteria and Acidobacteria,
regardless of the density of wood samples (Fig. 2). Approximately
13% of bacterial sequences were not classifiable to any known
Table 4. Number of bacterial and archaeal 16S rRNA gene sequences, OTUs and richness estimate in sequence libraries.
Bacteria 16S
Sample
LJ-1
LJ-7
LJ-10
LO-1
LO-6
LO-11
SI-1
SI-5
PJ-1
PJ-8
VJ-1
VJ-10
a
b
Archaea 16S
Sequences classified
as bacterial
OTUs
Observed
speciesa
Sequences classified
as archaeal
OTUs
Observed
speciesb
1070
1519
1813
1153
1443
895
1515
1347
1451
1330
1688
1311
296
316
337
302
334
167
350
336
334
279
376
286
280
223
227
253
238
167
239
256
248
218
240
239
574
no data
1
2793
361
251
376
32
911
0
4752
3946
11
no data
1
34
12
10
8
3
14
0
47
11
9
no data
nd
13
11
10
8
nd
9
nd
22
7
Estimate was calculated after rarefaction at depth of 895 sequences.
Estimate was calculated after rarefaction at depth of 251 sequences. nd: not determined.
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
Figure 2. Relative abundance of bacterial classes in wood samples. Samples are grouped by density and the percentages for each class represent the mean values of
the grouped samples.
Table 5. Relative abundances of archaeal phyla in wood samples
(grouped by density).
Density (kg dm−3 )
Phylum; class
Euryarchaeota;
Thermoplasmata
Thaumarchaeota; South
African Gold Mine Gp1
(SAGMCG-1)
Thaumarchaeota; terrestrial
group
0.43–0.37
(n = 4)
0.28–0.23
(n = 2)
0.14–0.13
(n = 5)
0
0
2.0
0
0
0.2
100
100
97.8
taxon; their closest matches were sequences in unclassified environmental clones (data not shown). The 454-sequencing of the
archaeal 16S rRNA gene yielded 13 997 reads that passed the
quality filtering step and the chimera check, and were classified as Archaea (Table 4). The vast majority of the sequences
were classified as Thaumarchaeota (South African Gold Mine
Group 1 and terrestrial group), while sequences belonging to Euryarchaeota (Thermoplasmata) were found just in two samples from
most decayed logs (Table 5). Sequencing of the qPCR products
also served as a quality control for the qPCR results for Archaea.
In our samples, the proportion of unspecific products of bacterial origin was higher in samples with low relative abundance
of Archaea by qPCR. Because qPCR quantification is done during the exponential amplification stage, the qPCR derived 16S
abundances are probably less affected by unspecific PCR products than sequencing results, which were based on sequencing
of the end products of qPCR reactions, which were run for 40
cycles. Ideally, products for sequencing would be collected after
a smaller number of cycles, to avoid non-specific amplification
and other PCR artifacts (e.g. Ahn et al. 2012).
Relationships between environmental factors
and prokaryotic community composition
Wood density affected significantly the bacterial community
composition (Mantel test, P = 0.022, Mantel r = 0.305). The
Rinta-Kanto et al.
7
Figure 3. (a, b) Relationships between bacterial community composition and (a) wood density (b) ethanol extractives. Non-linear gradients of these parameters were
fitted with principal coordinates, based on Bray–Curtis dissimilarity between samples (sample coordinates not plotted). Most abundant bacterial classes (classes with
relative abundance >0.1% of 16S rRNA sequences) were plotted using species scores derived from the principal coordinate scores. For the presentation, the species
scores were scaled down 10-fold. In both plots, the first and second axes (Dim 1 and Dim 2) explained 44.9% and 18.4% of the variation, respectively.
generalized linear model showed that bacterial community
richness increased significantly with decreasing wood density
(P = 0.021), and that wood density explained 42% of variation in community richness. For Archaea, the generalized linear model showed that OTU richness increased with decreasing wood density, but the effect of wood density on community richness was not significant (P = 0.386). The model
explains only 15% of variation in richness and the archaeal
community composition was not significantly linked to the
change in wood density either (Mantel test, P = 0.647, Mantel
r = 0.117).
Relationships between relative abundances of bacterial taxa
and environmental parameters were non-linear. Principal coordinates fitted with environmental parameters showed that,
of the major bacterial classes, Alphaproteobacteria and Acidobac-
teria were associated with conditions found in less decayed
wood: high density, low content of ethanol extractives, nitrogen and lignin (Fig. 3a and b, Supplementary Fig. S3a and b),
while Acidimicrobiia and Actinobacteria were associated with conditions found in advanced decay stages: low density, high nitrogen, lignin and ethanol extractive concentrations. The division was not so clear in relation to water extractive compounds, as water extractive compound concentrations showed
weaker correlation with density than other environmental parameters used in the analysis (Supplementary Fig. S2). However,
Alphaproteobacteria were associated with low water extractive
compound concentrations, whereas Acidobacteria, Acidimicrobiia
and Actinobacteria were associated with higher concentrations,
which were measured at later stages of decay (Supplementary
Fig. S3c).
8
FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
Figure 4. Co-occurrence patterns of fungal orders and bacterial classes in relation to wood density. Spearman rank correlations were calculated between relative
abundances of bacterial classes and fungal orders. The P-value is ≤ 0.05 if the absolute value of the correlation coefficient is ≥0.506 and P ≤ 0.01 if the absolute value of
the correlation coefficient is ≥0.712. Symbols after taxonomic IDs represent Spearman rank correlations between relative abundance of the taxonomic unit and wood
density.
Covariation between relative abundance of bacterial
classes and fungal orders
Principal coordinates fitted with wood density (Supplementary
Fig. S4) suggest that Alphaproteobacteria and many Actinobacteria occur in less decayed samples together with several fungal
orders, including known cellulose and lignin degraders, such
as Rhytismatales and Hymenochaetales. Fungal orders containing ectomycorrhizal fungi, e.g. Atheliales, Cantharellales and
Telephorales, were situated at the low end of the density gradient together with bacterial classes Acidimicrobiia, Deltaproteobacteria, Phycisphaerae, Saprospirae and Solibacteres. Clustering
of Spearman rank correlation coefficients calculated between
relative abundances of fungal orders and bacterial classes indicate that fungal–bacterial interactions may also be a factor
influencing the distribution and abundance of prokaryotes in
decaying wood (Fig. 4). Relative abundances of some fungal orders and bacterial taxa correlated with each other regardless of
their independent correlations with wood density. This could indicate synergistic interaction, e.g. Telephorales and Mortierellales. Conversely, some fungal order–bacterial taxa correlations
may indicate inhibitory interactions, e.g. Auriculariales and
Filobasidiales.
DISCUSSION
The present study shows that the decay process changes wood
logs gradually into an environment more hospitable to prokaryotes, indicated by the increase in abundance and richness of
prokaryotes with decreasing density of wood. The observed
trends in prokaryotic populations were not dependent on the
sampling area. Bacteria dominated the prokaryotic community
in our wood samples. Based on the abundance of 16S rRNA
genes, Archaea formed up to 0.4% of total abundance of prokaryotes. Nonspecific amplification of bacterial 16S rRNA genes with
Archaea-specific primers may have inflated the number of detected archaeal gene copies in this study. However, based on
our results, the percentage of Archaea in wood is comparable to
the proportion of Archaea commonly found in terrestrial environments, where they form 0.01–7% of prokaryotic populations
(Fierer et al. 2012).
Nitrogen content of wood correlated significantly with
the abundance of bacterial and archaeal 16S rRNA gene
copies, further suggesting that nitrogen availability may be
a key driver of bacterial community composition (Hoppe
et al. 2015), and of the abundance of prokaryotes in this
environment.
Rinta-Kanto et al.
Alphaproteobacteria, Acidobacteria, Actinobacteria and Acidimicrobiia had the highest relative abundances within the bacterial
communities at all decay stages. These classes represent phyla
(Proteobacteria, Acidobacteria and Actinobacteria) that have been
commonly found in bacterial communities colonizing wood or
tree litter (Zhang, Yang and Tu 2008; Valaskova et al. 2009; Noll
and Jirjis 2012; Sun et al. 2014; Hoppe et al. 2015). Our models
suggest that classes Alphaproteobacteria and Acidobacteria colonize wood at early decay stages, whereas bacteria belonging to
phylum Actinobacteria (classes Actinobacteria and Acidimicrobiia)
were associated with conditions found in later stages of decay:
low density with high concentrations of nitrogen, water and
ethanol extractives and lignin. Several known nitrogen-fixing
bacterial taxa belong to Alphaproteobacteria, and many potentially diazotrophic Alphaproteobacteria (e.g. Rhizobiales) have been
previously found colonizing P. abies especially at intermediate
stages of decay (Hoppe et al. 2014, 2015). Our models associated
Alphaproteobacteria with low-nitrogen conditions at early decay
stage, but their high relative abundance in later stages indicates
that besides nitrogen fixation, they may also have other roles in
a deadwood colonizing bacterial community.
While physical and chemical factors are important drivers
of bacterial community composition in wood, interactions with
the fungal community must also be considered. Previous studies
have clearly shown that the presence of wood degrading fungi
strongly affected the composition of the bacterial community
(Folman et al. 2008; Hervé et al. 2014, 2016; Hoppe et al. 2014). Our
current study revealed significant correlations between bacterial and archaeal abundance and fungal diversity. Correlations
between relative abundances of several bacterial and fungal
taxa suggest that these groups form a complex and dynamic
network of interactions shaped by synergistic and inhibitory
relationships.
The majority of the archaeal 16S sequences found in our
samples were related to terrestrial Thaumarchaeota, and only a
small fraction were potentially methanogenic Thermoplasmata.
This distribution is similar to what has been found in acidic
organic soils in general (Fierer et al. 2012), in mycorrhizospeheres of boreal forest trees, in an archaeal community colonizing
timber and in an acidic fen (Bomberg and Timonen 2007, 2009;
Hamberger et al. 2008; Beckmann et al. 2011). In this study, Thaumarchaeota of group 1.1c were abundant in most decayed wood
samples. This specific group of Archaea is frequently detected
in mycorrhizospheres of boreal forest trees, and it has been
shown that partnership between ectomycorrhizal fungi and Archaea may facilitate archaeal colonization in this environment
(Bomberg and Timonen 2007). Our data support this view, as ectomycorrhizal fungi become more abundant in the fungal population at later stages of decay (Rajala et al. 2012). Considering
our sequencing effort, the yield of archaeal sequences was low
and the sequencing depth was not sufficient to cover the entire
archaeal diversity, and therefore the three classes of Archaea
detected in this study may represent only the most abundant
classes present in this environment. The contribution of different archaeal groups to the wood decay process is still unknown.
Archaeal 16S rRNA gene copy abundance showed significant correlations with all tested environmental parameters, reflecting
the complex interactions between biotic and abiotic factors in
in P. abies logs.
In conclusion, the results of this study indicate that dead
Picea abies logs are an important habitat for Bacteria and Archaea
in boreal forests. Bacterial community composition is clearly influenced by physical and chemical properties of the substrate,
indicating that they are an active component of the wood-
9
colonizing biota. Interactions with the wood degrading fungal
community likely play a role in shaping the bacterial communities. Currently we know little about the metabolic characteristics of the members of wood-colonizing prokaryotic communities. Functional profiling of the communities in future studies
will help to understand how the amount and residence time of
deadwood in the forest affect the cycling of nutrients, e.g. carbon and nitrogen sequestration and release, on the ecosystem
level.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
The authors wish to thank Taina Pennanen for providing the
cover photograph.
FUNDING
This work was supported by Academy of Finland [grant number
131819].
Conflict of interest. None declared.
REFERENCES
Altschul SF, Gish W, Miller W et al. Basic local alignment search
tool. J Mol Biol 1990;215:403–10.
Ahn J-H, Kim B-Y, Song J, Weon H-Y. Effects of PCR cycle number and DNA polymerase type on the 16S rRNA gene pyrosequencing analysis of bacterial communities. J Microbiol
2012;50:1071–74.
Bates D, Maechler M, Bolker B et al. lme4: Linear MixedEffects Models Using Eigen and S4. R package version 1.17. 2014. http://CRAN.R-project.org/package=lme4 (1 March
2015, date last accessed).
Beckmann S, Krueger M, Engelen B et al. Role of Bacteria, Archaea
and Fungi involved in methane release in abandoned coal
mines. Geomicrobiol J 2011;28:347–58.
Bellassen V, Luyssaert S. Managing forests in uncertain times.
Nature 2014;506:153–5.
Bomberg M, Timonen S. Distribution of Cren- and Euryarchaeota
in Scots pine mycorrhizospheres and boreal forest humus.
Microb Ecol 2007;54:406–16.
Bomberg M, Timonen S. Effect of tree species and mycorrhizal
colonization on the archaeal population of boreal forest rhizospheres. Appl Environ Microbiol 2009;75:308–15.
Brown ME, Chang MCY. Exploring bacterial lignin degradation.
Curr Opin Chem Biol 2014;19:1–7.
Caporaso JG, Bittinger K, Bushman FD et al. PyNAST: a flexible
tool for aligning sequences to a template alignment. Bioinformatics 2010a;26:266–7.
Caporaso JG, Kuczynski J, Stombaugh J et al. QIIME allows analysis of high-throughput community sequencing data. Nat
Methods 2010b;7:335–6.
Clausen CA. Bacterial associations with decaying wood: a review.
Int Biodeterior Biodegradation 1996;37:101–7.
de Boer W, Folman LB, Summerbell RC et al. Living in a fungal
world: impact of fungi on soil bacterial niche development.
FEMS Microbiol Rev 2005;29:795–811.
10
FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
DeSantis TZ, Hugenholtz P, Larsen N et al. Greengenes, a
chimera-checked 16S rRNA gene database and workbench
compatible with ARB. Appl Environ Microbiol 2006;72:5069–72.
Edgar RC. Search and clustering orders of magnitude faster than
BLAST. Bioinformatics 2010;26:2460–1.
Fierer N, Jackson JA, Vilgalys R et al. Assessment of soil microbial
community structure by use of taxon-specific quantitative
PCR assays. Appl Environ Microbiol 2005;71:4117–20.
Fierer N, Leff JW, Adams BJ et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci U S A 2012;109:21390–5.
Folman LB, Gunnewiek P, Boddy L et al. Impact of white-rot
fungi on numbers and community composition of bacteria
colonizing beech wood from forest soil. FEMS Microbiol Ecol
2008;63:181–91.
Graham JE, Clark ME, Nadler DC et al. Identification and characterization of a multidomain hyperthermophilic cellulase from an archaeal enrichment. Nat Commun 2011. DOI:
10.1038/ncomms1373.
Haas BJ, Gevers D, Earl AM et al. Chimeric 16S rRNA sequence
formation and detection in Sanger and 454-pyrosequenced
PCR amplicons. Genome Res 2011;21:494–504.
Hamberger A, Horn MA, Dumont MG et al. Anaerobic consumers
of monosaccharides in a moderately acidic fen. Appl Environ
Microbiol 2008;74:3112–20.
Hervé V, Ketter E, Pierrat J-C et al. Impact of Phanerochaete
chrysosporium on the functional diversity of bacterial
communities associated with decaying wood. PLoS One
2016;11:e0147100.
Hervé V, Le Roux X, Uroz S et al. Diversity and structure of bacterial communities associated with Phanerochaete chrysosporium during wood decay. Environ Microbiol 2014;16:2238–52.
Hoppe B, Kahl T, Karasch P et al. Network analysis reveals ecological links between N-fixing bacteria and wood-decaying
fungi. PLoS One 2014, DOI: 10.1371/journal.pone.0088141.
Hoppe B, Kruger D, Kahl T et al. A pyrosequencing insight into
sprawling bacterial diversity and community dynamics in
decaying deadwood logs of Fagus sylvatica and Picea abies.
Sci Rep 2015, DOI: 10.1038/srep09456.
Hurek T, Wagner B, Reinhold-Hurek B. Identification of N2 -fixing
plant- and fungus-associated Azoarcus species by PCR-based
genomic fingerprints. Appl Environ Microbiol 1997;63:4331–9.
Jurgens G, Saano A. Diversity of soil Archaea in boreal forest before, and after clear-cutting and prescribed burning. FEMS
Microbiol Ecol 1999;29:205–13.
Lehtovirta LE, Prosser JI, Nicol GW. Soil pH regulates the abundance and diversity of Group 1.1c Crenarchaeota. FEMS Microbiol Ecol 2009;70:367–76.
Lenhart K, Bunge M, Ratering S et al. Evidence for methane production by saprotrophic fungi. Nat Commun 2012;3:1046.
Lynd LR, Weimer PJ, van Zyl WH et al. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev
2002;66:506–77.
McDonald D, Price MN, Goodrich J et al. An improved Greengenes
taxonomy with explicit ranks for ecological and evolutionary
analyses of bacteria and archaea. ISME J 2012;6:610–8.
Mäkinen H, Hynynen J, Siitonen J et al. Predicting the decomposition of Scots pine, Norway spruce, and birch stems in Finland. Ecol Appl 2006;16:1865–79.
Mukhin VA, Voronin PY. A new source of methane in boreal
forests. Appl Biochem Microbiol 2008;44:297–9.
Noll M, Jirjis R. Microbial communities in large-scale wood piles
and their effects on wood quality and the environment. Appl
Microbiol Biotechnol 2012;95:551–63.
Oksanen J, Blanchet FG, Kindt R et al. vegan: Community Ecology Package. R package version 2.2-1. 2015. http://CRAN.
R-project.org/package=vegan (31 March 2015, date last accessed).
Pruesse E, Quast C, Knittel K et al. SILVA: a comprehensive
online resource for quality checked and aligned ribosomal
RNA sequence data compatible with ARB. Nucleic Acids Res
2007;35:7188–96.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing,
2014.
Rajala T, Peltoniemi M, Pennanen T et al. Relationship between
wood-inhabiting fungi determined by molecular analysis
(denaturing gradient gel electrophoresis) and quality of decaying logs. Can J For Res 2010;40:2384–97.
Rajala T, Peltoniemi M, Pennanen T et al. Fungal community dynamics in relation to substrate quality of decaying Norway
spruce (Picea abies L. Karst.) logs in boreal forests. FEMS Microbiol Ecol 2012;81:494–505.
Reeder J, Knight R. Rapidly denoising pyrosequencing amplicon
reads by exploiting rank-abundance distributions. Nat Methods 2010;7:668–9.
Schellenberger S, Kolb S, Drake HL. Metabolic responses of novel
cellulolytic and saccharolytic agricultural soil Bacteria to oxygen. Environ Microbiol 2010;12:845–61.
Sun H, Terhonen E, Kasanen R et al. Diversity and community
structure of primary wood-inhabiting bacteria in boreal forest. Geomicrobiol J 2014;31:315–24.
Takai K, Horikoshi K. Rapid detection and quantification of
members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol 2000;66:5066–
72.
Vainio EJ, Korhonen K, Hantula J. Genetic variation in Phlebiopsis
gigantea as detected with random amplified microsatellite
(RAMS) markers. Mycol Res 1998;102:187–92.
Valaskova V, de Boer W, Gunnewiek P et al. Phylogenetic composition and properties of bacteria coexisting with the fungus
Hypholoma fasciculare in decaying wood. ISME J 2009;3:1218–
21.
Wainø M, Ingvorsen K. Production of beta-xylanase and betaxylosidase by the extremely halophilic archaeon Halorhabdus
utahensis. Extremophiles 2003;7:87–93.
Wang Q, Garrity GM, Tiedje JM et al. Naive Bayesian classifier for
rapid assignment of rRNA sequences into the new bacterial
taxonomy. Appl Environ Microbiol 2007;73:5261–7.
Warnes GR, Bolker B, Bonebakker L et al. gplots: Various R programming Tools for Plotting Data. R package
version 2.16.0. 2015. http://CRAN.R-project.org/package=
gplots (27 January 2015, date last accessed).
Weisshaupt P, Pritzkow W, Noll M. Nitrogen metabolism of
wood decomposing basidiomycetes and their interaction
with diazotrophs as revealed by IRMS. Int J Mass Spectrom
2011;307:225–31.
Werner JJ, Koren O, Hugenholtz P et al. Impact of training sets
on classification of high-throughput bacterial 16s rRNA gene
surveys. ISME J 2012;6:94–103.
Zeikus JG, Henning DL. Methanobacterium arbophilicum sp. nov. An
obligate anaerobe isloated from wetwood if living trees. Antonie Van Leeuwenhoek 1975;41:543–52.
Zeikus JG, Ward JC. Methane formation in living trees: a microbial origin. Science 1974;184:1181–3.
Zhang HB, Yang MX, Tu R. Unexpectedly high bacterial diversity in decaying wood of a conifer as revealed by a molecular
method. Int Biodeterior Biodegradation 2008;62:471–4.