1
2
3
4
5
Supplementary Materials
6
location), following the manufacturer’s Buccal Swab Extraction Protocol. DNA purity was
7
quantified via spectrophotometry (Nanodrop, Thermo Fisher Scientific, Waltham, MA) and
8
samples were stored at -20̊C until sequenced.
9
Bacterial DNA extraction and sequencing
Bacterial DNA extraction from swabs was performed using the QIAamp DNA Mini kit (Qiagen,
Twenty (20) μL of the extracted DNA solution was sent to Metagenome Bio Inc.
10
(Toronto, Canada) for sequencing of a fragment of the 16S rRNA gene using the 341F [1] and
11
806R [2] universal primers, using an Illumina MiSeq sequencing system. The primers contained
12
Illumina adaptor sequences and priming sites for sequencing [3]. Indexing sequences (6bp) were
13
also incorporated into both primers for pooling multiple samples in one run. The 25 L PCR
14
reaction contained 5 L of standard OneTaq buffer (5x), 0.25 L of 25mM dNTP, 0.5 L of
15
forwarded and reverse primers (10 M each), 1 L BSA (12 mg/ml), 0.125 L of OneTaq DNA
16
polymerase (NEB), 1-10 ng DNA, and water up to 25 L. PCR was run under the following
17
conditions: 94oC for 5 min, 30 cycles at 94oC for 30 sec, 45oC for 45 sec, 68oC for 1 min, and
18
finally at 68oC for 10 min. Triplicate PCR reactions were performed for each DNA sample in
19
order to minimize bias. Each PCR product were checked on 2% agarose gels. Following pooling
20
three PCR products of each sample, the amount amplified 16S rRNA was quantified on 2%
21
agarose gels. All PCR products were combined in equal amount of the 16S rDNA, loaded on an
22
agarose gel with SYBR Green (Invitrogen). The DNA band was excised with a Qiagen MinElute
23
gel extraction kit. The purified library DNA was quantified using Qubit dsDNA HS assay kit
24
(Life Technologies). Library pool (8 pM) was spiked with 5% phiX control (V3, Illumina) to
25
improve bae imbalance. Paired-end sequencing with read lengths of 251 bp was performed using
26
MiSeq Regent Kit V2 (2 x 250 cycles) on Illumina MiSeq sequencing system.
27
Sequences were processed using PANDAseq [4] and Quantitative Insights into
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Microbial Ecology (QIIME; version 1.8.0;[5]). Sequences were filtered to a length of 390-590
29
bp. Additional filtering thresholds included an alignment penalty of 0.01, default parameters of 2
30
k-mers and a matching primer threshold of 0.6. Sequences were dereplicated, sorted by
31
abundance, and singletons were removed. Operational Taxonomic Units (OTUs) were clustered
32
using UPARSE [6] and chimeras were filtered out using USearch’s ‘Gold’ database. Using a
33
minimum of 97% confidence, the Ribosomal Database Project Classified assigned taxonomic
34
classifications to each OTU using the GreenGenes database (version 13.5). Sequences were
35
aligned using the GreenGenes reference sequences and filtered. A midpoint rooted tree was
36
produced using FastTree in QIIME. After quality filtering, 1834 OTUs and 6425795 sequences
37
were detected in the dataset (average: 133870.7 ± 8351.966). The first capture included 1149
38
OTUs and 3108790 sequences (average: 129532.9 ± 7813) and the second included 1622 OTUs
39
and 3317005 sequences (average: 138208.5 ± 14913.34). Rarefaction curves for both capture
40
period one and two are shown in Figure 2.
41
42
Calculation of Shannon Diversity
43
Calculations of Shannon diversity were made using the “estimate_richness” function in the R
44
‘phyloseq’ package [7]. This package calculates Shannon diversity (H’) using the formulation:
45
𝐻 ′ = − ∑ 𝑝𝑖 ln(𝑝𝑖 )
46
where pi is the proportion of reads in the ith OTU. In this way Shannon Diversity Index is a
47
measure of taxa richness that integrates relative abundance within a system.
48
Identification of Top Bacterial Families
49
The families identified are those that were found to persist at the highest average relative
50
abundance (>0.43%) across the first and the second sampling periods. Measures of relative
51
abundance were calculated as the summation of sequence reads that could be assigned to the
52
family level, which were then standardized by total number of sequence reads within each
53
sample. A family level of analysis was chosen as it was the highest level of taxonomic resolution
54
that could be reliably assigned to almost all of the most abundant OTUs. In this way, pooled
55
OTUs of the families Pasteurellaceae, Neisseriaceae, Streptococcaceae, Oxalobacteraceae,
56
Lachnospiraceae, Ruminoncoccaceae and Prevotellaceae, consistently had the highest relative
57
abundance across the first and second capture period.
58
Like Pasteurellaceae, Streptococcaceae tended to increase in relative abundance with
59
increases in FGM, (R2 = 0.14, p = 0.07). Conversely the less prevalent families in the
60
microbiome tended to decrease in relative abundance with increases in FGM; Neisseriaceae (R2
61
= 0.14, p =0.07), Oxalobacteraceae (R2 = 0.002, p = 0.8)., Lachnospiraceae (R2 = 0.25, p =
62
0.01), Ruminoncoccaceae (R2 = 0.06, p =0.2) and Prevotellaceae (R2 = 0.03, p = 0.4).
63
Antibiotic Experiment
64
In a concurrent experiment, we attempted to disrupt the squirrel bacterial microbiome
65
community assemblage. Individuals were randomized into either control (n = 12) or treatment (n
66
= 12) groups upon first capture. After body measurements and oral swabs and fecal samples were
67
collected, squirrels in the treatment group were subcutaneously injected with 0.3ml/kg of
68
cefovecin (Convenia, Zoetis, Florham Park, NJ), whereas the control group was likewise injected
69
with the equivalent volume of sterile saline solution. The second period of capture occurred
70
when the antibiotic was expected to be at its highest circulating level within individuals based
71
upon the manufacturer’s recommendations. Nonetheless, in samples collected during the 2nd
72
capture period, we found no evidence for an effect of antibiotic treatment thus we pooled
73
“control” and “treated” animals for the 2nd capture period. Antibiotic treatment as a fixed effect
74
was not significant for explaining bacterial diversity at the phylum level (p = 0.4). Likewise, no
75
effect of antibiotic was observed in change in Pasteurellaceae relative abundance between
76
capture periods (p = 0.46). Additionally, even with the inclusion of treatment as a fixed effect in
77
the current GLMs, the observed relationships between bacterial communities, fecal
78
glucocorticoid metabolite concentration and phylum diversity remained significant at p < 0.05.
79
Thus, given these results, we conclude that the antibiotic was ineffective in this application.
80
Given the failure of the antibiotic to elicit a response, for the purposes of this manuscript we
81
pooled samples from the control and treatment group in subsequent analysis to increase
82
statistical power
83
Supplementary Tables
84
Supplementary Table 1. Correlation coefficients and p – values of fecal glucocorticoid
85
metabolite (natural-log transformed) versus Shannon’s diversity of the North American red
86
squirrel (Tamiasciurus hudsonicus) oral bacterial microbiome. In all instances only sequence
87
reads that could be reliably assigned to the taxonomic level of interest were considered.
88
Taxonomic
Unit
89
First Sample
r
p
Second Sample
r
p
Pooled Samples
r
p
Family
-0.61
0.0009
-0.49
0.006
-0.53
0.00004
Order
-0.62
0.0008
-0.42
0.02
-0.50
0.00009
Class
-0.60
0.001
-0.47
0.009
-0.51
0.00006
90
91
92
93
Supplementary Figures
94
95
Figure 1. Relative abundance of the most prevalent bacterial families pooled across both
96
captures. Median and interquartile range shown, whiskers denote minimum and maximum.
97
98
99
A)
B)
100
101
C)
D)
102
103
104
105
106
107
Supplementary Figure 2. Rarefaction curves of OTU Shannon diversity versus sampling depth
108
(A: capture one; B: capture two) and average observed OTU versus sampling depth (C: capture
109
one; D: capture two) for 24 red squirrel oral microbiome samples.
110
111
112
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