Microbial community successional patterns in beach sands impacted
by the Deepwater Horizon oil spill.
L. M. Rodriguez-R, W. A. Overholt, C. Hagan, M. Huettel, J. E. Kostka,
and K. T. Konstantinidis
SUPPLEMENTARY INFORMATION
SUPPORTING METHODS
Sample collection and nucleic acid extractions
During the 2010 sampling trips, a 10×1×1 meter trench was excavated,
samples were collected using cutoff 5 mL syringes exuded into 15 mL Falcon tubes,
immediately frozen on dry ice, and stored at -80ºC, as described in detail by Kostka et
al. (2011). Samples from June 2011 were collected using sediment cores, sectioned at
10 cm intervals, homogenized, and immediately frozen on dry ice. Total genomic
DNA was extracted using a MoBio Powersoil DNA extraction kit (MoBio
Laboratories, Carlsbad, CA) according to the manufacturer’s protocol. Due to low
DNA yields, DNA extracts were pooled according to depth and similar degree of
oiling (Table 1). DNA libraries were prepared and sequenced by the Genomics
Sequencing Facility at Los Alamos National Laboratory, using Illumina TruSeq DNA
library prep and Illumina HiSeq 2000, with a paired-end strategy and reads of 102 bp
in length.
16S rRNA gene amplicon analysis and annotation
16S rRNA gene amplicon libraries were prepared on both pooled and original
samples. Genomic DNA was sequenced at the Argonne National Laboratory using
Illumina MiSeq with paired-end strategy and reads of 151bp in length, using custom
sequencing primers and procedures as previously described (Caporaso et al., 2012).
Raw 16S rRNA gene amplicons were processed in the software package QIIME
v1.7.0 (Caporaso et al., 2010). Reads were truncated if three consecutive bases had a
PHRED score of less than 20, and only reads greater than 113 bases were retained.
Reads less than 60% similar to any sequence in the Greengenes release 13-5 database
(DeSantis et al., 2006) were discarded. Sequences were clustered into OTUs at 97%
2
similarity using UCLUST (Edgar, 2010). All OTUs that represented < 0.005% of the
total sequences were discarded to minimize the effect of sequence errors and focus on
the dominant OTUs (Bokulich et al., 2013). Representative sequences of each OTU
were classified using the RDP Classifier at 50% confidence (Wang et al., 2007). The
number of OTUs were subsequently rarefied using the R package vegan (Oksanen et
al., 2013). The final OTU counts at 10,000 reads were used to compute and project
complexity curves using preseq (Daley & Smith, 2013) with increasing step size from
2,000 to 10,000 sequences until convergent models were found. Samples that didn’t
produce a convergent model at 10,000 sequences step size were ignored in the
projection.
Sequence trimming, community coverage, and metagenomic assembly
Metagenomic sequences were trimmed using SolexaQA (Cox et al., 2010)
with a PHRED score cutoff of 20 and a minimum fragment length of 50 bp. Only
coupled reads with both sisters longer than 50 bp after trimming were used further.
Reads including Illumina adaptors in the 3’ end were also discarded. Average
coverage of the community was estimated and projected using Nonpareil, an
algorithm that determines coverage based on the level of redundancy of the sequence
reads of metagenomes, with default parameters (Rodriguez-R & Konstantinidis,
2014b). Each metagenomic dataset was assembled following the procedures outlined
in (Luo et al., 2012). Briefly, sequences were assembled with Velvet (Zerbino &
Birney, 2008) and SOAP de novo (Li et al., 2010) using k-mer values ranging from 21
to 63. Three assemblies that maximized the N50 and/or the number of reads used
were selected from each method, and combined using Newbler.
3
Annotation of metagenomic fragments
Coding sequences were predicted on contigs larger than 500 bp using
MetaGeneMark.hmm with default parameters (Zhu et al., 2010) and functionally
annotated based on protein-level searches against the SwissProt database (Wu et al.,
2006) using the Gene Ontology (GO) terms (Ashburner et al., 2000), and against the
SEED database using the Subsystems categories (Overbeek et al., 2005). Query genes
were assigned the functional annotation of their best-match in SwissProt containing
GO terms or in SEED containing Subsystem categories, based on blastp (Altschul et
al., 1990) searches with a minimum bit-score for a match of 60. The bacterial and
archaeal taxonomy of the contigs was assessed using MyTaxa (Luo et al., 2014), with
default parameters. The input file to MyTaxa was the blastp results of the query genes
against the predicted proteins of all closed and draft prokaryotic genomes available in
NCBI as of January 2014, with minimum score of 50 bits and alignment length ≥ 80%
of the metagenomic protein and only the top-5 hits per query being considered. The
fraction of sequences originating from each domain was estimated by directly
comparing sequencing reads against UniRef50 (Suzek et al., 2007) using PAUDA
(Huson & Xie, 2013) with a minimum score of 60 bits. The assembled genes were
used to estimate the minimum generation time using growthpred (Vieira-Silva &
Rocha, 2010) with parameters “-b -m -c 0 -r -i -s” separately for each metagenome.
Gene functional and taxonomic profiles
Functionally and taxonomically annotated genes from different samples were
pooled together. The abundance of each gene on each dataset (i.e., the sequencing
depth of a gene) was estimated by the number of reads mapped on the genes. Read
4
mapping was performed using BLAT with default parameters (Kent, 2002),
considering only the best match with alignment length ≥ 80bp and identity ≥ 97%.
Annotation terms and taxa with significantly different abundance between groups of
samples were identified using a negative binomial test as implemented in the DESeq2
package (Anders & Huber, 2010). The profiles of eukaryotic taxonomy were
estimated based on putative 18S rRNA reads identified by Metaxa (Bengtsson et al.,
2011) with e-value below 0.1. To taxonomically identify eukaryotic sequences, a
reference 18S rRNA gene tree was first built using high-quality aligned sequences
(pintail, sequence, and alignment qualities above 90) from the SILVA reference
database r114 (Quast et al., 2012) and FastTree v2.1.7 with GTR model for
nucleotides (Price et al., 2010). The tree was next pruned to reduce complexity by
iteratively collapsing closely related leaf nodes into single nodes (10 cycles), using a
custom R script and the picante library (Kembel et al., 2010). The putative 18S rRNA
gene-encoding metagenomic reads identified by Metaxa were next aligned to the
reference 18S rRNA gene alignment using MAFFT’s addfragments option with 6mer
pairs (Katoh et al., 2002) and placed onto the reference tree with pplacer v1.1.alpha14
(Matsen et al., 2010). The taxonomy of the reads was identified using the Silva
taxonomy and ancillary custom scripts and the tools pplacer and taxtastic (Matsen et
al., 2010). Complete taxonomic profiles were visualized using Krona (Ondov et al.,
2011).
The profiles of eukaryotic taxonomy were estimated based on putative 18S
rRNA reads identified by Metaxa (Bengtsson et al., 2011) with e-value below 0.1. To
taxonomically identify eukaryotic sequences, a reference 18S rRNA gene tree was
first built using high-quality aligned sequences (pintail, sequence, and alignment
qualities above 90) from the SILVA reference database r114 (Quast et al., 2012) and
5
FastTree v2.1.7 with GTR model for nucleotides (Price et al., 2010). The tree was
next pruned to reduce complexity by iteratively collapsing closely related leaf nodes
into single nodes (10 cycles), using a custom R script (enve.prune.dist from
enveomics.R; http://github.com/lmrodriguezr/enveomics) and the picante library
(Kembel et al., 2010). The putative 18S rRNA gene-encoding metagenomic reads
identified by Metaxa were next aligned to the reference 18S rRNA gene alignment
using MAFFT’s addfragments option with 6mer pairs (Katoh et al., 2002) and placed
onto the reference tree with pplacer v1.1.alpha14 (Matsen et al., 2010). The taxonomy
of the reads was identified using the Silva taxonomy and ancillary custom scripts and
the tools pplacer and taxtastic (Matsen et al., 2010). Complete taxonomic profiles
were visualized using Krona (Ondov et al., 2011).
Genome equivalents quantification
In order to measure the average number of genes per cell with a given
functional annotation (genome equivalents), a set of universally conserved singlecopy genes were identified among the assembled gene sequences from the
metagenomes. All genes were compared against a collection of 101 HMMs (Dupont
et al., 2012), mainly derived from the Genome Property database, entry GenProp0799
“bacterial core gene set, exactly 1 per genome” (Haft et al., 2005), using HMMER3
(http://hmmer.janelia.org/) with default settings and trusted cutoff. Ten models were
eliminated because more than one model represented the same gene family (rpoC,
pheT, proS, and glyS) or represented extremely conserved families (era and tRNA
synthase class I). The median sequencing depth (in reads/bp) of the remaining 91
models was used as the normalizing factor for each dataset. The sequencing depth of
genes with a given annotation (see below) was estimated on each dataset (in
6
reads/bp), added up, and divided by the normalizing factor of the corresponding
dataset.
Hydrocarbon analyses
The analyses of the crude oil hydrocarbons in the sediment samples followed
the recommended EPA standard procedures. Hydrocarbons were extracted from
homogenized sediment sections using an automated Buchi Soxhlet extraction system
according to EPA method 3541. The extraction with a hexane:acetone 1:1 v/v mixture
was followed by a rinsing step with the same solvent mixture. The solvents containing
the extracts were concentrated using a Turbovap-II rapid solvent evaporator (55ºC) to
less than 5 mL, and then transferred to cyclohexane by adding 20 mL of that solvent.
Concentration was continued until the sample was reduced to roughly a volume of 2
mL that then was cleaned according to the EPA 3630C method. The cleaned
hydrocarbon extracts were analyzed on an Agilent 7890A Series GC (Column:
Agilent HP-5MS, 30 m × 250 μm × 0.25 μm) with Agilent 7890A FID. To determine
the concentrations of aromatic hydrocarbons, samples were injected into an Agilent
7890A Series GC (Column: Agilent DB-EUPAH, 20 m × 180 μm × 0.14 μm) coupled
to an Agilent 7000 triple quadrupole MS system.
7
SUPPLEMENTARY FIGURES
A
Nonpareil curves for metagenomes
Estimated average coverage (%)
100
90
80
70
60
50
40
30
pre (4)
A, B, C (3)
D (1)
20
10
E, F, G (3)
H (1)
I, J (4)
0
100 M
500 M
97% OTUs (x 1,000)
B
1G
5G
Sequencing effort (bp)
10 G
50 G
100 G
Rarefaction curves for 16S rRNA gene amplicons
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
pre (5)
A (2)
B (3)
100
D (3)
E (5)
G (2)
1K
H (2)
I (7)
J (7)
10 K
100 K
1M
10 M
Sequencing effort (Reads)
Supplementary Figure S1: Sequence coverage of the datasets used in this study.
(A) Nonpareil curves for the metagenomic datasets in this study. The abundanceweighted average coverage was estimated based on the redundancy of the reads for
different sequencing efforts (solid lines) using the Nonpareil algorithm (Rodriguez-R
& Konstantinidis, 2014b). The inter-quartile range of 1,024 replicates is presented as
shaded areas, and the estimated coverage of the entire datasets is presented as empty
circles. The curves were projected using a cumulative gamma distribution, and the
convergent models are presented as dashed lines. The horizontal red line indicates
8
95% average coverage. Nonpareil diversity index for pre-oil samples ranged between
20.35 and 20.92 (average 20.73), for oiled samples ranged between 20.65 and 21.31
(average 21.07), and for recovered samples ranged between 22.03 and 22.63 (average
22.39), indicating and increase in sequence complexity from pre-oil to oiled samples
and from oiled to recovered samples. (B) Rarefaction curves for 97% OTUs in 16S
rRNA gene amplicon datasets were constructed using the vegan R package (Oksanen
et al., 2013). The colored areas indicate the 95% confidence intervals. Curves were
projected using preseq (Anders & Huber, 2010), and convergent models are presented
as dashed lines. Samples are named after the corresponding metagenomic dataset, and
the number of libraries (replicates) for each group is indicated in parenthesis
(legends). All samples were colored following table 1: Pre-oil samples (green), Oiled
samples from July 2010 (mauve), Weathered oil samples from July 2010 (red), Oiled
samples from October 2010 (sea green), Weathered oil samples from October 2010
(pink), and recovered samples (olive). Note that samples with lower Nonpareil curves
(A; olive), indicating higher diversity, correspond to those with highest estimated
richness (B).
9
0 ●0 ●0
0
●
May-2010 ●
Pre-spill
5 ●0
Jul−2010 ●
Pre-spill ●15 ●25
15
●
35
●
35
●
0
●
35●
●
35
Jul−2010
Oiled
15
●
55
● 55
●
40
●
Jun−2011
Recovered
60
●35● 50
35
● ●55
●
35
40
●●
30
40
55 ●
●
● 35
45
●
40
●
●
Oct−2010
Oiled
Jul-2010
Weathered oil
● 50
55
●
50
●
Oct−2010
Weathered oil
Supplementary Figure S2: Sampling depth played a limited role in structuring
beach microbial communities relative to oiling. Non-metric multidimensional
scaling plot generated from weighted-UNIFRAC distances based on 16S rRNA OTUs
defined at 97% identity (2D-stress 14%). Samples were colored by groups following
Table 1, with the additional group Jul-02-10 (Pre-spill) in light olive. Depth (cm) for
each sample is indicated with red text. The graph shows that the presence/absence of
oil had the largest effect on microbial community structure, followed by sampling
date. Depth played a relatively small role in structuring beach sand microbial
communities (ADONIS partial R2 0.03) compared to oiling status and sampling date
10
(ADONIS partial R2 0.754). For instance, samples collected on June 14th, 2011 reveal
surficial and deep sands are more similar to each other than to the oiled layers. The
pre-oiled metagenomes used in the analysis were obtained from the May, 2010
samples.
11
E pre
7e-6
0.08
2e-4
2e-6
1e-4
2e-12
pre
pre
pre
pre
A
A
B
B
B
D
D
E
E
E
E
E
G
G
H
H
I
I
I
I
I
I
I
J
J
J
J
J
J
J
0.007
0.6
5e-6
pre
pre
pre
pre
A
A
B
B
B
D
D
E
E
E
E
E
G
G
H
H
I
I
I
I
I
I
I
J
J
J
J
J
J
J
0.7
7e-4
97% 16S rRNA OTUs
0
100
200
300
Diversity (equivalent GO terms)
D pre
0.004
pre
pre
pre
pre
A
A
B
B
B
D
D
E
E
E
E
E
G
G
H
H
I
I
I
I
I
I
I
J
J
J
J
J
J
J
0.006
C pre
0.002
0.002
0
100
200
300
400
Diversity (equivalent subsystems)
0.53
0.16
S1
S2
S3
S4
A
B
C
D
E
F
G
H
I600
I606
J598
J604
0.01
0.003
GO: molecular function
B
S2
S3
S4
A
B
C
D
E
F
G
H
I600
I606
J598
J604
0.12
Subsystems
A S1
27,948
0
1,000
2,000
Diversity (equivalent OTUs)
0
104
2x104 0
Richness (OTUs)
0.1
0.2
Evenness
0.3
Supplementary Figure S3: Effect of community recovery in taxonomic and
functional diversity. The horizontal colored bars represent the α component of
functional (A-B) and taxonomic (C-E) diversity, and the grey horizontal bars
represent the corresponding γ component. Comparisons between groups were
performed using two-sided t-tests, and the p-values are reported for the following
comparisons: Pre-oil vs Oiled, Oiled vs Recovered, and Oiled from July 2010 vs
Oiled from October 2010. Samples were colored by groups following Table 1.
12
A 24
52
22
Minimum doubling time (h)
20
18
14
12
10
6
1192
446 384 362
4
0
Average genome size (Mbp)
474
8
2
B
38
16
331
92
767 653
603 632
47
736
S1 S2 S3 S4 A B C D E F G H
49
I
I
J
J
50
40
30
20
10
5
4
3
2
1
Supplementary Figure S4: Estimated minimum doubling time and average
genome size of the microbial communities sampled in this study. The minimum
doubling time was estimated using growthpred (Vieira-Silva & Rocha, 2010) and the
values are presented for each individual metagenomic sample. Open circles indicate
average value, and the bars indicate the range of mean ± 1.96 × standard deviation
(approximating the 95% confidence interval). The number of identified ribosomal
13
genes is indicated below the bars. Samples were colored by groups following Table 1.
The estimated optimal temperature was in the range 29-37ºC (34.2 ± 2.5 ºC), except
for J604 (40ºC). The average genome size was estimated based on the detected
abundance of conserved single-copy genes in the bacterial and archeal fraction. Open
circles indicate median value between estimators (single-copy genes) and the bars
indicate the range of the 95% confidence interval. In both panels, the horizontal lines
indicate the average per group of samples.
14
SUPPLEMENTARY FILES
Supplementary File S1: Assembly, gene prediction, and read mapping statistics
per dataset. This file (File_S1.Stats.xlsx) is a spreadsheet with a single tab in
Microsoft Excel format.
Supplementary File S2: Distribution of bacterial and archaeal taxa in the studied
metagenomes. This self-contained website presents an interactive view of the
different bacterial (hues of red) and archaeal (hues of green) taxa in the metagenomic
samples in the form of hierarchical pie charts. The size of the sections indicates the
abundance (as number of reads) in the datasets based on the classification of reads
derived from MyTaxa. The different samples can be explored in the top-left list. This
file (File_S2.BacArc-OS.html) was produced with Krona (Ondov et al., 2011).
Supplementary File S3: Distribution of eukaryotic taxa in the studied
metagenomes. This self-contained website presents an interactive view of the
different eukaryotic taxa in the metagenomic samples in the form of hierarchical pie
charts. The size of the sections indicates the abundance (as number of reads) in the
datasets based on the classification of reads derived from PAUDA. The different
samples can be explored in the top-left list. This file (File_S3.Euk-OS.html) was
produced with Krona (Ondov et al., 2011).
Supplementary File S4: Comparisons of category abundances between groups of
datasets. Tabs are named for the comparison performed and the groups of datasets
compared. The categories correspond to GO terms from the Molecular Function
(MF), Biological Process (BP), and Cellular Component (CC) domains; and the
Subsystems categories (SS). The groups of datasets contrasted were, following table
15
1, Pre-oil from May 2010 (P), Oiled from July and October 2010 (O), Recovered from
July 2011 (R), Oiled from July 2010 (O1), and Oiled from October 2010 (O2).
Comparisons were performed using the DESeq2 package, and the columns indicate
the base mean abundance after normalization (baseMean), the log2 of the fold-change
between compared groups (log2FoldChange) and its estimated standard error (lfcSE),
the statistic for the negative binomial test (stat), the corresponding p-value (pvalue),
and its correction for multiple testing (padj). Only categories with significant
differences are reported (p-value adjusted ≤ 0.05), and the rows are colored by the
group of datasets in which the corresponding category is significantly more abundant.
This file (File_S4.Annotation.xlsx) is a spreadsheet with 16 tabs in Microsoft Excel
format.
16
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