Supplementary Material: Text
Diversity analysis methods. Treeclimber (Schloss and Handelsman 2006a) was
used to test the hypothesis that the overall structure of two communities is statistically the
same. P values indicate the probability that a similar association between phylogeny and
sample origin would occur by chance (Schloss and Handelsman 2006a). The mothur
software package (Schloss et al. 2009) was used to calculate rarefaction curves, Chao1
(Chao 1984) species diversity of each depth, as well as cross-comparisons of community
structure using abundance-based Jaccard (Yue et al. 2001) and Theta (Yue and Clayton
2005) diversity indices. Both individual community Chao1 estimates, and estimates of
the Chao1 diversity shared between two communities (shared Chao1) were calculated.
The abundance-based Jaccard index measures the intersection of the combined
assemblage of phylotypes, and can be considered the probability that given a phylotype is
present in one assemblage, it is present in both assemblages (Schloss and Handelsman
2006b). By contrast, ThetaYC corrects for the differences in relative abundance of
phylotypes in each assemblage, taking into account both the incidence of shared
phylotypes and the similarity of their relative abundances in each assemblage. Thus, it
can be considered a percentile measure of the similarity of community structures.
Archaeal and Bacterial species richness. The Archaeal and Bacterial clone
libraries differed widely in the species diversity as estimated by rarefaction analysis
(Figure S2) and Chao1 indices (Table S3A,B). For bacterial community structure
comparisons, operational taxonomic units (OTU) were based on 3% (Stackebrandt and
Goebel 1994) rather than 1% sequence divergence used for the Archaea (Stackebrandt
and Ebers 2009), due to the high sequence richness of the bacterial dataset. Archaeal
diversity appeared to be fully sampled by four to ten 1% OTUs for all depths. In
contrast, rarefaction curves of Bacteria did not reach saturation at any sediment depth
(Figure S2). Chao1 estimates showed a similar trend and indicated 3 to 10 archaeal
species at 1% OTU definition, but several tens to several hundreds of bacterial 3% OTU
definition (Table S3B). There are three possible explanations for this conspicuous
difference in bacterial and archaeal diversity:
1) If these results are a reasonable approximation of the true diversity of the
Archaea and Bacteria in situ, highly restricted archaeal diversity might indicate that the
oligotrophic sediment biome represents an extreme habitat for Archaea, where only a few
specialists thrive. This is consistent with the observation that the SPG12 archaea are
distinct from the archaeal lineages abundant at organic-rich margin sites (e.g., Biddle et
al. 2006, 2008; Inagaki et al. 2006; Fry et al. 2008); in these habitats, they constitute a
major or even the dominant portion of the overall microbial biomass (Lipp et al. 2008).
2) If Archaea occur in significantly lower abundance than Bacteria at SPG12,
their low DNA and target gene concentration leads to stochastically biased PCR
amplifications, diminishing the species diversity detectable in clone libraries. Yet given a
conservative 25% recovery of cellular DNA from ~10 mL of sediment containing
350,000 to 10,000,000 cells per mL of sediment, and using 2% of this recovered DNA in
each PCR reaction, at least 21,000 bacterial and archaeal 16S rRNA gene molecules
should be present in each PCR (Table S4). Assuming the 2/5 ratio of Crenarchaeota to
Bacteria found in the abyssal North Pacific water column (Karner et al. 2001), this
translates into a minimum archaeal 16S gene number of ca. 4000 per PCR. To minimize
the potential effect of PCR bias on clone library diversity, three PCR reactions were
combined for each cloning procedure, PCR cycle number was kept as low as possible,
nested PCR was avoided, and multiple precautions were taken to maximize DNA
extraction yields (see Methods). The potential number of genomes per PCR reaction,
based on sample cell counts and the assumption of 25% DNA extraction efficiency, did
not suggest a direct relationship between PCR DNA concentrations and clone library
composition and diversity (Table S4). The impact of sediment-derived PCR inhibitors on
clone library diversity is yet unknown.
3) A third possibility, not mutually exclusive of the previous two, is that the
archaeal 16S rRNA genes recovered from SPG12 may have substitutions in key primer
sites, with only rare phylotypes being amenable to PCR using 8f/1492r primers. Highly
selective PCR amplification of a few matching archaeal phylotypes from otherwise
mismatched groups would produce nearly identical archaeal sequences multiple times
from different depths, possibly under widely varying geochemical regimes (Figure 1;
Table S2). For example, the DSEG-2 phylotype SPG12_401_411_A1, comprising ~38%
of clones from 401-411 cmbsf, also made up 5% of clones at 210-220 cmbsf; and the
MBG-A2 phylotypes were found in abundance at both 273-283 and 343-353 cmbsf. The
frequent recovery of identical archaeal phylotypes from different samples and PCR
reactions is hard to explain by stochastic PCR bias only, but would be congruent with
primer bias. Mismatched PCR primers might be one of the reasons why representatives
of SPG12 archaeal lineages are rare in public databases (Table S1). More generally, the
effects of primer bias can be seen in the divergent outcomes of mutually independent
archaeal 16S rRNA gene PCR analyses of the same subsurface sediments (Teske and
Sørensen 2008). Yet, the 16S rRNA gene primer pair used in this study had been adjusted
for greater archaeal coverage by shortening the forward primer sequence from E.coli
positions 8 to 27, to positions 8 to 24 (Teske et al. 2002), to avoid mismatches in these
critical nucleotides at the 3’-end of this frequently targeted priming region that were
repeatedly found in database searches especially among euryarcheota (A. Teske,
unpublished results). In two field tests, the modified archaeal primer pair recovered a new
euryarchaeotal lineage (Teske et al. 2002), subsequently named the Guaymas
Euryarchaeotal Group (Dhillon et al. 2005).
Ultimately, the explanation for the low Archaeal diversity may in fact be a
combination of all three alternatives described above. Newly developed, multiple PCR
primer sets, as well as PCR-independent methods such as whole-genome amplification
(Biddle et al. 2008), are needed to characterize the unique microbial communities in
oligotrophic marine deep sediments. At this stage, the SPG12 results showing low
archaeal species diversity, and the resulting archaeal comparative statistics should be
interpreted with caution.
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Supplementary material: Figures
Figure S1. Neighbor-Joining phylogeny of all full-length 3% OTU representatives for
SPG12 Planktomycetes (A), Chloroflexi (B), and other bacterial phyla (C, D, E), based
on ~1200-bp alignments of 16S rRNA sequences. A maximum-composite likelihood
substitution model with gamma-corrected site rates was used in MEGA4. Support for
clades was estimated by 1000 interior branch test replicates. 3% representative
phylotypes defined on partial reads for the 153-163 depth layer were not included in
phylogenies (Table S2). References for previously defined uncultured clades are as
follows: Fig. S1(A): CPla-3, Köhler et al. (2008). Fig S1(B): SAR202, Giovannoni et al.
(1996); SAR202 subclusters, Morris et al. (2004); Chloroflexi clades I-IV, Inagaki et al.
(2006). S1(C): NKB19, Li et al. (1999), Hugenholtz et al. (2002); SAR324, Wright et al.
(1997); Termite Group I, Ohkuma and Kudo (1996); OP8, Hugenholtz et al. (1998); NTB2, Reed et al. (2002). Fig. S1(E): SAR11, Giovannoni et al. (1990); SARmet-5, Teske et
al. (2011); JTB255, Li et al. (2009). Fig. S1(D): SAR406, Gordon and Giovannoni
(1996); WS3, Dojka et al. (1998); NT-B9, Reed et al. (2002); BRC1 (Derakshani et al.
2001).
Figure S2. Rarefaction analysis at 1% OTU resolution for Archaea (gray symbols) and
Bacteria (black symbols).
Table S1. Representative phylotypes for archaea in the SPG12 sediment column, based
on 1% OTU definition. The accession number, the best BLAST hit, the phylogenetic
classification, and the number of clones in different sediment horizons are listed for each
phylotype.
Table S2. Representative phylotypes for bacteria in the SPG 12 sediment column, based
on 3% OTU definition. The accession number, phylogenetic classification, and the
number of clones in different sediment horizons are listed for each phylotype.
Table S3. Archaeal and Bacterial diversity indices for SPG12 clone libraries. Furthestneighbor diversity analyses for SPG12 Archaea (A) and Bacteria (B) were calculated in
mothur (Schloss et al. 2009), using the furthest-neighbor approach and an OTU definition
of 3%. For comparative indices, the first row shows the shared Chao1 estimated
diversity (Chao 1984), the second row lists abundance-based Jaccard (Yue et al. 2001),
and the third ThetaYC similarity (Yue and Clayton 2005). Also listed for Bacteria is the
Shannon diversity index and the Pielou evenness index, both calculated using phylum as
the relevant taxonomic unit. .
Table S4. Relative comparison of potential DNA concentrations in PCR reactions, based
on sediment cell densities, volume of sediment extracted, and volume of PCR extract
used per PCR reaction. 3% OTU bacterial and 1% OTU archaeal Chao1 diversity is
provided for comparison. 25% DNA extraction efficiency is assumed for the purposes of
this comparison.
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