Shade et al. Apple flower microbiome
Supplemental Text for
Unexpected diversity during community succession in the apple
flower microbiome
Ashley Shade, Patricia S McManus, and Jo Handelsman
Supplemental Methods
Nucleic acid extraction
Prior to nucleic acid extraction, flowers were stored frozen at -80 C. Samples of flowers
were removed from the storage freezer, flash frozen in liquid nitrogen, and massed.
Microbial cells were separated from flower material before nucleic acid extraction.
Flowers were washed in 35-mL cold 1x PBS-0.15% Tween solution with five to seven 3mm glass beads as a mild abrasive. Tubes were secured horizontally in a polystyrene
foam container filled with ice, shaken at maximum speed on a rotator at 4 °C for 20 min,
and sonicated for 5 min in a water bath (model 2210, Branson Ultrasonics Corporation,
Danbury, CT). Large flower debris was removed by filtering the liquid through sterile
cheesecloth. The remaining flower debris was pelleted by centrifugation at 1500 rpm
(272 x g) and 4 °C for 5 min (model Avanti J-E, Beckman-Coulter, Brea, CA).
Supernatant was transferred to a clean tube, and centrifuged for 30 min at 6500 rpm
(5111 x g), 4 °C to pellet the microbial cells. The pellet was re-suspended with buffer
CLS-TC from the FastDNA spin kit (MP Biomedicals, Solon, OH). DNA was extracted
following manufacturer’s instructions with minor modifications. Samples were
homogenized for 1 min using a vortex adapter. We performed the optional 5-min
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Shade et al. Apple flower microbiome
incubation at 55 °C to maximize DNA recovery prior to the final spin. DNA was
quantified using a Nanodrop spectrofluorometer (ThermoScientific NanoDrop Products,
Wilmington, DE). Samples were stored at -20 °C until amplification. Extracted DNA
concentration was between 19 and 70 ng/µL.
PCR was performed according to Research and Testing Laboratories standard
protocols, and included a 30 cycle PCR with a mixture of HotStart and HotStar high
fidelity Taq polymerases. Tag-encoded FLX amplicon pyrosequencing utilized Roche
454 FLX instrument with Titanium reagents and procedures. The tag-sequences used
and sample descriptions ("mapping file") are given in Table S1A. The average product
length was 335 nucleotides (See Supplemental Methods).
Sequence analyses
Research and Testing Laboratories performed sequence denoising and chimerachecking, as per their standard analysis protocols (www.researchandtesting.com).
Additional quality control and processing of sequences was performed using the default
QIIME 1.3 workflows. There were 225,243 raw input sequences. Sequences with
ambiguous bases, a mean quality score below 25 within a 50-bp window, more than six
homopolymers, any primer mismatches, missing quality scores, or not between 200 and
1000 bp in length were removed. From these quality control steps, 171,996 sequences
passed, resulting in a minimum 1838, maximum 10776, and mean 5733 sequences per
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Shade et al. Apple flower microbiome
sample. Rarefaction to 1838 sequences was conducted for all samples prior to
community analyses.
It is common to have a wide range of amplicons per sample in 454 sequencing because
of sample to sample variability (e.g., as in (1)), which is why datasets are often rarefied
to an equal number of sequences per sample prior to analysis (2). For this dataset, we
noticed an interesting trend of an increased number of amplicons recovered and the
age of the blossom, and subsequently showed a significant, direct correlation between
these (see Main Text, and Supplemental Results below). Thus, we suggested that the
gradual increase in the number of amplicons is the result of increase microbial load on
each flower through time, as studies of Erwinia amylovora infection have shown that
bacterial growth rate increases with apple flower age (3, 4). Note that the dataset was
subsampled (rarefied) to an equal number of sequences per sample prior to any
community analyses.
Operational taxonomic units (OTUs) were set at 97% sequence identity with uclust (5)
and taxonomy was assigned using Ribosomal Database Project (RDP, (6)) with a
minimum confidence of 0.8. To be conservative with diversity estimates, singleton OTUs
(i.e., those detected only once over the entire dataset) were removed from the rarefied
dataset. Sequences were aligned with PyNAST using the green genes template (7), and
a phylogenetic tree was built with FastTree (8) to calculate UniFrac distances between
samples (singletons omitted, rarefied to 1838 sequences), a measure of beta-diversity
(9-11). Weighted UniFrac distances ultimately were chosen over unweighted to prevent
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Shade et al. Apple flower microbiome
over-emphasis of rare taxa in an uneven microbial dataset, and because of its greater
explanatory value in describing beta-diversity (principal components analysis of
weighted UniFrac axis 1 = 32.9% of variation explained, unweighted = 12.2%). Using
QIIME, rarefied richness (no. OTUs) and rarefied Faith’s phylogenetic diversity metric
(12) were calculated as measures of alpha (within-sample) diversity.
Among 225,243 raw sequences, 171,996 met our quality control criteria and were
retained for analysis. The average sequence length was 335 nucleotides. After
operational taxonomic unit (OTU) picking and clustering at 97% sequence identity,
4,531 OTUs were identified. Greater than half of these OTUs occurred only once
throughout the dataset (singletons). To estimate diversity conservatively, singletons
were removed after re-sampling (rarefaction) to 1,838 sequences per sample, the
minimum number of sequences observed within a sample. Omitting singletons did not
affect beta diversity (community differences across samples), as assessed with Mantel
test (Pearson’s R between full dataset and dataset omitting singletons = 0.997, p <
0.001), and Procrustes rotation (p = 0.001). After singleton removal, 1,677 OTUs from
50,865 tag-sequences remained in the analysis.
Evaluating differences in microbial community structure
There are two multivariate ways that distinguish whether communities have different
structures, by their centroid (mean) or by their spread (variability (13-15)). Different
membership is visualized by different centroids in an ordination, representing different
mean composition across groups (Figure S1Aa and S1Ab). Communities may also
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distinct because of differences in the variability, or spread (Figure S1Ab and S1Ac).
Difference in variability is visualized by dispersion from a centroid. Thus, we asked
whether there were differences in either centroid or spread across trees, as assessed
by weighted UniFrac distance. Weighted UniFrac distance accounts for differences due
to the number, relative abundance, composition, and phylogenetic representation of
OTUs (16).
Hierarchical clustering
Clustering is an alternative to ordination for uncovering patterns across species or
samples. All 1,677 OTUs from the 30 samples were included in the analysis. To cluster
the OTUs by similar patterns and not by similar abundances, each OTU was relativized
to its total number of occurrences (Figure S3A). Then, pair-wise Bray-Curtis similarities
were calculated between all OTUs. The similarities were used to begin the hierarchical
clustering based on the complete linkage algorithm of the hclust function in R. The
algorithm first combined the most similar OTUs to form clusters, and then combined the
most similar clusters iteratively until one large cluster was achieved. We explored
different levels of clusters for underlying temporal patterns. The first level below the
largest cluster contained six clusters (Figure 4a), and we found that these six clusters
were comprised of groups of temporally cohesive OTUs. Finally, before creating bar
charts, the dataset was relativized by the total number of sequences observed on each
day.
MultiCoLA
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Shade et al. Apple flower microbiome
To understand how omitting rare taxa from the dataset affected beta diversity, we
performed a multivariate cutoff level analysis (MultiCoLA; (17)) using two standard
metrics of community similarity, Bray-Curtis and Sørenson, as well as weighted UniFrac
distance (Table S3). In MultiCoLA, the least abundant taxa are removed from the
dataset in increasing proportions, and then each reduced dataset is correlated to the full
dataset to determine how the omission of rare taxa influences beta diversity.
Supplemental Results
Temporal analysis of apple flower communities
Because time was a significant descriptor of microbial phylogenetic diversity and
variability (and because we did not detect differences across trees), we aggregated the
six trees each day to explore further temporal patterns, and found that sequencing and
sampling efforts were sufficient but not exhaustive for all days (Figure S4A), likely
because of the high frequency of rare OTUs. Temporal patterns were not apparent at
the phylum level (Figure S1). For example, no time points were distinguished by the
presence or absence of particular phyla. Proteobacteria decreased in relative
abundance when the flowers opened, while Archaea and Chloroflexi increased and
maintained the same proportion until the final collection. TM7 also increased in relative
abundance at flower open. Verrucomicrobia peaked on 01 May, early flowering,
although it was relatively abundant at all time points.
There was no difference in Pielou’s evenness (equitability of taxon representation)
through time, except between 01 May and 29 April, and 01 May and 02 May (analysis of
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Shade et al. Apple flower microbiome
variance between days, F = 3.09 on 4 d.f., p = 0.03; Tukey’s HSD p = 0.04 for both
above comparisons, p > 0.05 for all others). In both cases, the 01 May community had
lower evenness and higher variability. A similar pattern was observed for richness
(analysis of variance F = 4.97 on 4 d.f., p < 0.05). There were no differences in the
number of OTUs across days, except for 01 May, which was significantly different from
both 29 April and 02 May (Tukey HSD p < 0.5 for both). However, there was no general
trend in evenness or richness through time.
It is expected that the number of sequences will vary across samples due to differences
in sequencing reactions, which is the rational for rarefying datasets to an equal
sequencing depth (2). However, this variability should not be systematic (e.g., through
time, as in Figure S2). We found that the number of sequences recovered, potentially
serving as a very rough approximation for the microbial load on flowers (18), peaked in
the middle of the time series, correlating with flower biomass (Pearson’s r = 0.42, p =
0.02, Figure S2). We speculate that the increase in the number of amplicons is
because of increased microbial load on each flower through time, as studies of Erwinia
amylovora infection have shown that bacterial growth rate increases with apple flower
age (3, 4). The correlation with flower biomass may be due to increased flower niche
spaces available for microbial colonization, such as on expanding petals before peak
flowering. However, because of known technical biases in DNA extraction, PCR
efficiency, primer specificity, and 454 noise, this result should be interpreted with
caution.
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Shade et al. Apple flower microbiome
Diversity and representation of OTUs across phyla and successional groups
To visualize the occurrences of OTUs across phyla, a tree was built omitting
unidentified Bacteria (Figure 6, main text). From OTUs identified to the phylum level,
there were 24 that had relative abundances between 0.006 and 0.10; 18 of these were
Deinococcus-Thermus or TM7. The other OTUs were evenly represented, and had
relative abundances less than 0.006. Though proportionally less than DeinococcusThermus and TM7, Proteobacteria, Bacteroidetes, and Actinobacteria were prevalent in
apple flower communities.
We also tested for differences in evenness and richness across successional groups.
There was evidence for modest differences in richness (analysis of variance p = 0.08)
and clear differences in evenness (Table S5). Specifically, the Mid successional group
had lower evenness than the other groups (all p < 0.001). Indeed, the dominant OTU in
the dataset, affiliated with Deinococcus-Thermus, was a member of the Mid
successional group (main text Figure 6 pink bars). Additionally, the Climax group had
significantly higher evenness than the others (all p < 0.05), except the Early-Succession
group (p = 0.20, main text Figure 6 dark green bars). All other successional groups did
not differ in evenness. These results suggest that, with a few exceptions, successional
groups contained similar and diverse representation of phylogenetic lineages, and that
the dynamics of any one phylum, order, or family did not characterize overall
successional patterns.
Normalizing the dataset to flower biomass
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Shade et al. Apple flower microbiome
We asked whether normalizing the community dataset to flower biomass would
influence our interpretation of community patterns, and found that the biomassnormalized dataset exhibited the same community patterns as the original dataset
(Protest R = 0.998, p = 0.001). Therefore, we continued analysis using the dataset that
was not normalized to flower biomass.
More and interesting network analysis results
For all significant associations, positive (collinear or direct) associations were
distinguished from negative (inverse or indirect) correlations. Positive associations are
representative, for instance, of mutualisms or shared resources, while negative
associations are representative, for instance, of antagonism. However, the biological
processes underpinning the direction of these associations cannot be informed by the
LSA. There was a similar number of positive and negative associations (772 positive
associations, 817 negative), but the majority of associations were time-delayed (1233
delayed associations, 322 unilateral).
Associations additionally could be described by whether they were unilateral (occurring
on the same day) or delayed in time (lagged). For example, a time-delayed association
may occur if the growth of one taxon alters environmental conditions, and, after time,
the altered environment impacts the growth of a neighboring taxon. The proportion of
delayed and unilateral associations was consistent across successional groups (Figure
S5A). Generalist taxa had a greater proportion of negative associations than others
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Shade et al. Apple flower microbiome
(Figure S5A), supporting the possibility that many Generalist OTUs, though persistent,
perhaps were not as competitive as the members of the other successional groups.
Among the 20% most prevalent OTUs for each tree, 72-84% of OTUs had at least one
significant association with another OTU (Table 3). However, among the 20% most
prevalent OTUs for the full network, 52% had associations. In general, the networks of
individual trees had comparable diameters, geodesic distance (a metric of the "small
world effect"- the average distance from any one OTU to any other), clustering
coefficients (a metric of the number of OTUs that are tightly associated together in small
cliques). An exception was Tree 4, which had a smaller diameter than the others.
Furthermore the average number of associations (mean degree) varied from 15.20 to
28.2 among trees. One interesting emergent property of the full network, though, that
was not manifested at the individual tree level, was the clustering coefficient, C, which
was much higher than those observed in individual trees. This suggests that the full
network is not simply the sum or average of the individual tree networks, but that using
the trees as biological replicates informs identification of clusters of associated OTUs
that otherwise may not have be apparent. These basic network properties suggest that,
like many other networks (e.g., (19)), the apple flower microbiome has highly clustered
OTUs with a clustering coefficient of 0.32, but also maintains the "small world" property
of minimal linkages between disparate nodes (here, represented as OTUs). Notably,
the random network generated with 175 nodes and 1539 edges (to be comparable to
the full network), had comparable geodesic distance and mean degree, but a higher
diameter and much lower clustering coefficient than the full network.
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Shade et al. Apple flower microbiome
Detection of Erwinia affiliated taxa
One taxon out of the full dataset (singletons included), OTU 1138, was affiliated with the
Erwinia genus at 97% sequence identity, but we were unable to determine whether
OTU 1138 represented Erwinia amylovora (the causal agent of fire blight). OTU 1138
was rare in the dataset and occurred 81 times over the time series, and peaked in
abundance before flowers opened and before streptomycin was sprayed. However,
there were no differences in OTU 1138 occurrence between trees sprayed with
streptomycin and trees that were not sprayed (p = 0.71). Notably, close relatives of E.
amylovora represent both plant pathogens and commensals. Some of these close
relatives may be protective against E. amylovora (e.g., Pantoea vagans C9-1). We
cannot distinguish these closely related, non-pathogenic species from OTU 1138.
Supplemental Discussion
Ecology of successional groups on apple flowers
The ecology of successional groups can be informed by the timing of each group’s peak
occurrence, as well as by the identity of prevalent members. For example, Late
successional members peaked in abundance just prior to petal fall. This group included
a high abundance of Lactobacillus and Acetobacter taxa, whose occurrences align with
conditions of flower decomposition by yeast (20-22). Additionally, the Climax group had
highest evenness and phylogenetic diversity compared to the other groups, suggesting
that resources were abundant and competition was relatively low at petal fall.
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Shade et al. Apple flower microbiome
The Generalist taxa persisted on the flowers, but because they never were the most
abundant, these taxa likely were not as competitive or as fast-growing as members of
the other successional groups. However, these relatively stable, persistently detected
Generalist members may inform as to the core microbiome of apple flowers at its most
conservative definition (23). These Generalists may be targeted for follow-up studies, as
their stability and relative abundance indicates that they may maintain intimate
relationships with the host, with potential utility for crop management.
Caveats: Why didn't we observe an effect of streptomycin?
There could have been an effect of streptomycin that was not apparent due to
methodological caveats. We suggest that the study duration was appropriate to the life
of the flowers and to the generation times of flower microorganisms, and thus exclude
the possibility that the study was too short to observe an impact of streptomycin.
However, a more likely caveat is that our methods cannot distinguish between the 16S
rRNA of dead and living cells, and so it is possible that we observed dead members of
the community. There are two clues that help to determine if a taxon were killed by the
streptomycin but its DNA remained. First, the taxon should have been detected in the
second time point, which was collected before streptomycin spraying. Second, the DNA
of the taxon whose members are dying should not increase over time, but instead
remain constant or decrease (due to degradation or scavenging from live organisms).
Instead, we see dynamic fluctuations among the majority of the observed taxa, including
daily peaks in abundances with each successional group. Among the minority of taxa
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Shade et al. Apple flower microbiome
that decreased in abundance after streptomycin was sprayed, we cannot determine
whether individuals affiliated with these taxa were alive. It is also possible that, despite
the short temporal window, streptomycin-sensitive taxa re-colonized the flower surfaces
after the antibiotic was degraded. Therefore, though it is possible that signal from dead
cells is present in the community, we suggest that this signal is likely small relative to
the signal of the successional dynamics, which are likely driven by viable and growing
microorganisms.
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Additional supplemental items (Table S1A, Figure S1A, Figure S2A, Figure S3A, Figure
S4A, and Figure S5A) are available at on the Handelsman lab resources page:
http://www.yale.edu/handelsmanlab/resources/index.html
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