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Divergence across diet, time, and populations rules out parallel evolution in the gut
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microbiomes of Trinidadian guppies
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Karen E. Sullam, Benjamin E. R. Rubin, Christopher M. Dalton, Susan S. Kilham,
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Alexander S. Flecker, Jacob A. Russell
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Supplementary Text and Figures
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Supplemental Methods:
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Additional information on sample processing and sequencing
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Whole guppies were preserved in 95% ethanol for the bacterial analysis and
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70% ethanol for the gut length analysis and were transported to Drexel University,
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where samples were frozen in -20 °C prior to dissections 4-14 months later. After
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dissections, whole guts were immediately added to Mo Bio PowerBead Tubes.
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Following dissection, a digital picture was taken of each gut for length measurement
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using ImageJ. The filters and sediment samples were collected in sterile Whirl-Pak®
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Sampling Bags. Following collection, samples were frozen at -20 °C for 3 months,
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followed by storage at -80 °C for 12 months prior to extraction.
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Eighty samples were included in three different sequencing runs at Research
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and Testing Labs. A subset of four samples from the Guanapo 2010 survey (two HP
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samples and two LP sample) were run independently and multiplexed with samples
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not included in the present study. Thirty-six of the remaining 2010 survey samples
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were run in the second run, while the final run included the 2011 survey samples, the
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samples from the dietary experiment, and one sample from the 2010 survey that had
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minimal coverage from the first sequencing run.
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Additional information on dietary manipulation study
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Males were included in the tanks because females decrease energy assimilation in the
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absence of male guppies (Reznick 1983), but only females were used for subsequent
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measurements. All tanks had the same male to female ratio (1:3) and all female
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guppies were reproductive over the course of the entire experiment.
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Additional information on classification
The BLAST algorithm, using the Greengenes database as a reference, was first
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used for taxonomic classification of representative sequences. Any reads that were
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either classified as chloroplasts or failed to classify to bacteria were excluded from
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further analysis. It became apparent that a number of such classifications at the
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phylum level deviated greatly from results involving BLAST against the NCBI
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database and from RDP classification. For this reason we utilized UCLUST (in Qiime
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v. 1.8) to classify all but 82 of our OTUs that went unassigned using this method.
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BLAST classification against the Greengenes database appeared to adequately
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classify these latter OTUs, and thus our overall classification consisted of a hybrid
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approach involving both methods.
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Additional information on OTU genotyping
We built a pipeline to find genotype variation within the 6 most dominant
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OTUs of the dataset, from which we focused on 2 OTUs that were composed of
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samples run in the same batch to eliminate possibly confounding batch effects (OTU
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4447 and 5760). First, to reduce the computational load of performing alignments on
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all sequences in each OTU, we removed non-unique sequences using the dereplicate
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function of USEARCH v. 6.0.307 (Edgar 2010). De-replicated sequences were
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aligned with MUSCLE v. 3.8.31 (Edgar 2004). We used the “–maxiters 2” option on
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those OTUs (2023 and 4447) with very large numbers of unique sequences (>17,000)
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as attempting full alignments caused MUSCLE to crash. Default parameters were
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used for all other OTUs. Alignments were then repopulated with the non-unique
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sequences removed by de-replication. To reduce problems introduced by sequencing
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errors, homopolymers and surrounding gaps were masked from alignments before
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further analysis. A commonly encountered suspicious alignment pattern with the
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following three characteristics was also masked: (1) adjacent sites had one identical
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non-gap allele, (2) one allele at one site was a gap, (3) the frequency of the gap was
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within 10% of the frequency of the identical allele in the site that did not include a
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gap. This pattern suggested that the two apparently variable adjacent sites were
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actually made up of two misaligned sites (e.g. site one: -/G, site two: G/A produced
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by MUSCLE is likely a misalignment of a correct alignment of site one: G/G, site two
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-/A). Although there is potentially useful information in these sites, they were
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excluded to maintain genotype quality. We extended the filter to include similarly
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suspicious situations following the same pattern except that both bases were shared in
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the two adjacent sites (e.g. site one: A/G, site two: G/A). This latter pattern less
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clearly represents misalignment, but when these sites were examined more closely,
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they were invariably surrounded by combinations of gaps and nucleotides that
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suggested alignment error. In addition, all alignments dropped precipitously in quality
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after several hundred bases, so each OTU alignment was examined and trimmed at the
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length where clear misalignments became common. While these exclusions
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potentially reduced the amount of true variation that we could identify, the indel
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errors inherent in 454 sequencing and the difficulty of accurately aligning such large
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numbers of sequences made this procedure necessary. We ran our pipeline separately
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on each OTU to identify the appropriate quality control parameters and poorly-
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aligned sites for exclusion. All parameters for each OTU are given in Supplementary
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Table 4.
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Many sequences could not be assigned an allele at every variable site, leading
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to incomplete genotypes. These arose due to the presence of low frequency bases that
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likely represented sequencing errors, alignment errors, or masked sequence data.
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Therefore, when these incomplete genotypes were missing data at just a single site but
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were otherwise identical to one and only one other complete genotype, they were
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assigned the corresponding complete genotype. Although we could potentially be
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collapsing unique genotypes in this way, most of these incomplete genotypes likely
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represented the genotype to which they were assigned. At the very least, they were at
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least more closely related to the assigned genotype than to any other. Additionally,
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sequences with genotypes including missing data that did not meet the quality
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requirements were discarded. Finally, genotypes present at a frequency of less than
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0.1% across the entire dataset were also excluded to further minimize the inclusion of
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sequence and alignment artifacts.
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For each OTU analyzed, samples with more than 25 reads per OTU were
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included in the genotyping analysis. For OTU 4447, 2 samples from 2011 were
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excluded in the analysis to focus on site variation within the 2010 samples. For OTU
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5760, 1 HP Aripo and 1 LP Marianne sample were excluded from analysis to focus in
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on populations with n > 1.
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Results:
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Enterotyping Analysis
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It was determined that all fish gut samples, including those from the wild and
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the dietary study, were optimally partitioned in to six enterotypes (Supplementary
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Table 5). These groupings appeared to correlate to the dominance of certain OTUs,
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where the wild fish either had one of the following OTUs as a dominant bacterium
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(OTU 2023, 4447, and 5998) or were in the sixth partition in which no bacterium had
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a strong dominance. The two partitions that encompassed lab-reared fish were
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dominated by two OTUs (OTU 1229 and 1106).
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The two OTUs that dominated the lab fish and seemingly shaped their
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enterotype grouping, were not abundant in wild fish, but were still found in the wild
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fish. For example, 13 out of 55 wild fish (8 LP fish and 5 HP fish) harbored the
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Spirochaeta-derived OTU 1106 that dominated guts of lab-reared LP fish. For the
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Entomoplasmatales-derived OTU 1229 that was common in lab-reared HP fish, 6 out
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of 55 wild fish (5 HP fish and 1 LP fish) were found to have this OTU.
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OTU member genotyping
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The genotypic, or strain, composition of two dominant OTUs showed
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variation across one or more scales. The Marianne LP and the Quare LP differed from
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the other sampling locations that had sufficient representation of OTU 4447, which
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included the Aripo HP, Aripo LP, and Guanapo LP (Supplementary Figure 3A). OTU
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5760 showed variation in rare strains between two ecotypes from separate streams
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(Supplementary Figure 3B).
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Reznick DN (1983). The structure of guppy life histories: the tradeoff between growth
and reproduction. Ecology 64: 862-873.
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Supplementary Figures:
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Supplementary Figure 1: Rarefaction curves of observed species number. Analyses
were performed using QIIME to characterize species richness of bacterial
communities from A) guppy guts colored by stream and separated by ecotype
background from the 2010 survey, and B) environmental samples from the Guanapo
River. The difference in y-axis of the two graphs shows that environmental samples,
particularly those from sediment, tend to harbor greater bacterial diversity than guppy
guts.
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Supplementary Figure 2: Principal Coordinates Analysis of guppy gut bacterial
communities from the 2010 field survey based on A) unweighted UniFrac distances
and B) Hellinger-transformed Bray-Curtis distances.
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Supplementary Figure 3: Strain analysis of dominant OTUs with their
corresponding phyla in parentheses. Analyses include A) OTU 4447, which shows
differences between certain sampling locations and ecotypes, and B) OTU 5760,
which differs among rare strains between the two streams and ecotypes. HP and LP
are used to distinguish ecotypes, collected from High (HP) vs. Low predation (LP)
habitats. Number of reads from each sequence library is listed on top of bars for each
OTU. Each vetted genotype is assigned a different color and listed in each panel by
different letters (See Supplementary Table 4 for information of which genotype
corresponds to which letters). Stacked bar graphs show the proportion of all reads
from the given OTU made up by each genotype.
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Supplementary Figure 4: Size standardized gut length comparison of guppies across
four streams. Size standardizations to visualize the results and account for allometry
(Torres & Vanni 2007) were made by calculating the size corrected gut characteristic
(i.e. length or weight) = gut characteristic/standard length of fish^(slope of all
individuals’ log gut characteristic/ log standard length). Color is associated with
stream of origin and asterisks indicate significant differences between HP and LP
ecotypes.
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Supplementary Figure 5: Network analysis of samples collected in the 2011
Guanapo field survey during which gut bacteria were compared to environmental
samples. N= 3 for gut samples from the three different environments and N = 2 for all
environmental samples, except for HP water sample, for which N = 1. The gut
samples clearly separate from the environmental samples.
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