Abstract
Thermal homeostasis of mammals is constrained by body-size scaling.
Consequently, small mammals require considerable energy to maintain a
high mass-specific metabolic rate (MSMR) and sustain target body
temperature. In association with gut microbiota, mammalian hosts
acquire absorbable molecules and fulfill their metabolic requirements.
Our objective was to characterize gut microbes in wild mammals and
relate those findings to host body-size scaling. Two large (Petaurista
philippensis grandis and P. alborufus lena), one medium (Trogopterus
xanthipes) and one small (Pteromys volans orii) species of flying
squirrels (FS) were studied. Using 16S rRNA genes, 1,104 OTUs were
detected from four FS, with 1.99% of OTUs shared among all FS. Although
all FS gut microbiota were dominated by Firmicutes, they were
constituted by different bacterial families. Moreover, Bacteroidetes
accounted for up to 19% of gut microbiota in small FS, but was absent
in large FS. Finally, based on metagenome predictions, carbohydrate and
amino acid metabolism genes were enriched in small body-size FS. In
conclusion, gut microbiota compositions and predictive metabolic
functions were characteristic of body-size in FS, consistent with their
adaptations to folivorous dietary niches.
Subject terms: Microbiome, Symbiosis, Animal physiology
Introduction
Body size is a major factor in endothermic animals’ metabolism to
promote survival. Mammals metabolize foods to generate enough heat to
balance surface heat loss^[36]1. However, heat losses depend on the
body surface area to volume ratio; this constraint is called body-size
(allometric) scaling and considered “structural and functional
consequences of changes in size or scale among otherwise similar
organisms”^[37]2. Basal metabolic rate (BMR) is the exponent of 3/4 or
2/3 of body mass (Kleiber’s law)^[38]3,[39]4, whereas mass-specific
metabolic rate (MSMR, in liters O[2]/kg/h) exponentially decreases as
body mass increases (Fig. [40]S1)^[41]2,[42]4,[43]5. Consequently, body
size and energy metabolism fundamentally constrain the interaction
between animals and their environment and determine their niche.
It remains unclear how small arboreal folivorous mammals maintain a
high MSMR as they have small to moderate body sizes (~250 to 8200 g),
due to both physical (habitat structures) and energetic (poor diet)
limitations of living in treetops^[44]6–[45]8. With a plant-based diet
comprised of 50 to 85% fiber and no endogenous enzymes to digest plant
biomass, small arboreal folivorous mammals must address their MSMR
through adapted digestive strategies^[46]9. There are two apparent
solutions: 1) increased retention time of digesta in enlarged digestive
chambers (e.g. cecum); and 2) assisted digestion from symbiotic gut
microbes^[47]10. Mammals have suites of gut microbes to improve energy
uptake^[48]11–[49]13, enabling hosts to acquire absorbable molecules.
For example, short-chain fatty acids (SCFAs) produced by gut microbes
provide their hosts (e.g. sheep and cattle) with up to 70% of their
caloric requirements^[50]14.
Variation of gut microbial composition is associated with hosts’
physiological circumstances, especially diet^[51]15,[52]16. Diverse
gut-microbial profiles converge according to dietary types of mammalian
hosts^[53]13,[54]17,[55]18. Hosts acquire fitness within specific
dietary niches that are reflected in variation in several dominant
microbial taxa, e.g. phyla Firmicutes, Bacteroidetes, Actinobacteria
and Proteobacteria^[56]13,[57]17–[58]19. With these diversified
symbionts, microbes colonizing mammalian gastrointestinal tracts may
have adapted distinct functions. For example, gut microbiota of
carnivores contain more genes for protein degradation, whereas those of
herbivores have numerous genes for protein biosynthesis and plant fiber
degradation^[59]18. Through cooperation with gut microbiota,
herbivorous hosts acquire absorbable nutrient molecules. Microbes not
only release extracellular enzymes to break down polysaccharides and
proteins, but also ferment SCFAs to provide energy for hosts^[60]20.
Although cellulolytic gut microbes have been characterized
(particularly from domesticated mammals), little is known about gut
microbial diversity regarding wild mammals’ body-size scaling.
Leaf-eating flying squirrels are among the smallest arboreal mammals
(range, 24 to 1500 g) and sustain a high metabolic rate (estimated as
0.41 to 1.82 liters O[2]/kg/h) despite a low-quality diet (i.e. tree
leaves)^[61]21–[62]23. Occupying specialized dietary niches in
treetops, folivorous flying squirrels rely on symbiotic microbes in an
enlarged cecum to degrade celluloses to meet energy demands^[63]20. To
address scaling issues, we studied four species (from three genera) of
folivorous flying squirrels, with body mass representing three size
classes: 1) large (two species), Petaurista philippensis grandis (PPG;
~1300 g) and P. alborufus lena (PAL; ~1500 g); 2) medium. Trogopterus
xanthipes (TX; ~450 g); and 3) small, Pteromys volans orii (PVO;
~130 g)^[64]24–[65]27. Mass-specific metabolic rates (MSMR) of mammals
(measured by oxygen consumption) are well studied, i.e., the so-called
mouse-elephant curve (Fig. [66]S1). The curve is useful because we
conducted field studies, making direct measurements for MSMR not
feasible. For these species, MSMR were estimated as 0.42 (PPG), 0.40
(PAL), 0.59 (TX), and 0.90 (PVO) liters O[2]/kg/h, respectively
(Fig. [67]S1). The MSMR of small flying squirrels is more than double
that of large species. Therefore, it was anticipated that small
folivorous flying squirrels had very effective digestion-absorption
strategies. Lignocellulose, which constitutes the majority of plant
biomass, is the major dietary component of these folivorous flying
squirrels. Large flying squirrels (PPG and PAL) consume primarily
leaves of broadleaf trees, accounting for up to 74.0% of their annual
diet^[68]28,[69]29. Captive TX in this study were fed natural diets,
including leaves of Chinese arborvitae (Platycladus orientalis), pine
nuts, and acorns^[70]30,[71]31, whereas wild PVO consumed young leaves,
buds, flowers, and seeds of Salix spp. and Picea spp.^[72]25.
Our objectives were to investigate differences in gut microbiota
composition among folivorous flying squirrels of various sizes. We
hypothesized that gut microbiota and microbial energy metabolism are
constrained by body-size scaling and that gut microbial composition and
functions reflect dietary niches of each flying squirrel species.
Consequently, we elucidated gut microbial composition of the four
folivorous flying squirrels in reference to their body mass by
sequencing bacterial 16 S rRNA gene libraries from fecal samples.
Thereafter, we predicted the metagenome and pathways of energy
metabolism contributed by gut microbiota.
Materials and Methods
Animals and fecal sample collection
Analysis of gut microbiota was done for four species of flying
squirrels: Siberian Flying Squirrel (Pteromys volans orii, PVO;
n = 19), Complex Toothed Flying Squirrel (Trogopterus xanthipes, TX;
n = 4) and two species of Giant Flying Squirrels (Petaurista
philippensis grandis, PPG; n = 3 and P. alborufus lena, PAL; n = 3).
Fecal samples from PVO flying squirrels were collected during a
mark-recapture study conducted from May to August in 2013 and 2014 at
The University of Tokyo Hokkaido Forest in Furano City (Hokkaido,
Japan). Permits for live trapping were approved by Hokkaido Government
Kamikawa General Subprefecture Bureau (No. 45 in 2013 and No. 10 in
2014) and by The University of Tokyo Hokkaido Forest (No. A13-12 in
2013 and No. A14-07 in 2014). These samples were sequenced in our
previous study^[73]32; sequence reads were retrieved from NCBI Sequence
Read Archive database (accession numbers: SRX3793743- SRX3793761).
Fecal samples of both PAL and PPG were collected from wild individuals
in March 2013 and in March-April 2014 at Wulai District, New Taipei
City, Taiwan (collection permissions No. 1023228856 and No. 1022101678
were granted by the Government of New Taipei City and Forestry Bureau,
Council of Agriculture, Executive Yuan in 2013 and 2014), in accordance
with the Wildlife Conservation Act^[74]33. The TX squirrels were farmed
animals (Supplementary Text) and fed Chinese arborvitae (Platycladus
orientalis) leaves, pine nuts, acorns, etc. (Shangluo, Shaanxi
Province, China)^[75]30,[76]31 For these animals, fecal samples were
collected from their cages in May, 2012 and immediately preserved in
RNAlater® (Thermo Fisher Scientific, Carlsbad, CA, USA) for subsequent
bacterial genomic DNA extraction. We did not retain live animals for
any sampling. Feces of TX squirrels are used in folk medicine (see
Supplementary Text).
DNA extraction, PCR, library preparation and sequencing
Bacterial genomic DNA was extracted from fecal samples (~200 mg) using
QIAamp Fast DNA Stool Mini kit (QIAGEN, Valencia, CA, USA), following
pathogen detection protocols. The V3-V4 region of the bacterial 16 S
rRNA gene was PCR-amplified using barcoded forward primers
(XXXXXXCCTACGGGNGGCWGCAG) and reverse primers
(XXXXXXGACTACHVGGGTATCTAATCC); 6-bp barcodes were indicated by XXXXXX.
The PCR was performed under the following conditions: 94 °C for 4 min,
followed by 25 cycles of 94 °C for 30 s, 57 °C for 30 s and 72 °C for
30 s, with a final elongation step at 72 °C for 8 min. Amplified
products of expected size (464 bp) were purified with QIAquick Gel
Extraction Kit (QIAGEN) and DNA concentrations determined using Qubit^®
3.0 Fluorometer (Invitrogen, San Diego, CA, USA). Three amplicon
libraries were constructed by pooling 20 samples with equal amounts of
DNA for each library. The 16 S rRNA gene amplicons were pair-ended
sequenced (2 ×300 bp) with 1 Gb qualified outputs per library using an
Illumina MiSeq platform at Yourgene Bioscience Company (Taiwan).
Sequence analysis
Raw data acquired from the three libraries were processed according to
the Amplicon SOP v2 of the Microbiome Helper workflow
([77]https://github.com/mlangill/microbiome_helper)^[78]34. Paired-end
reads were trimmed of barcodes with Cutadapt 1.8.1^[79]35 (-g XXXXXX -G
XXXXXX–discard-untrimmed; XXXXXX indicates 6-bp barcodes). Trimmed
sequence data were processed with QIIME 2 v. 2019.4^[80]36. The ‘DADA2’
plugin embedded in QIIME2 were used to identify amplicon sequence
variants (ASVs) from de-multiplexed sequence files (with
parameters:–p-trunc-len-f 270–p-trunc-len-r 210–p-max-ee 3)^[81]37.
Then, taxonomy was assigned by the ‘classify-sklearn’ function of
‘feature-classifier’ plugin with a Naïve Bayes Classifier trained on
SILVA 132, using 99% OTUs full-length sequences of 16 S rRNA
genes^[82]38. In total, there were 277,256 qualified sequences
representing 3,455 taxonomic features across the 29 samples (range, 959
to 33,265 reads). Due to over-classification of ASVs and unclear
taxonomy assignment for wild animals’ microbiota, we re-clustered
representative ASV features in OTUs with 97% similarity using uclust
v1.2.22q^[83]39 and re-assigned taxonomy with SILVA 123, using 97% OTUs
full-length sequences of 16 S rRNA genes using blastn 2.6.0 + ^[84]40
with e-value 1e-5 and extracted best hit according to bitscore (using
SILVA 123 database in order to further predict metagenome by Tax4Fun;
see below). Finally, 1,104 OTUs were redefined by the alternative
method. After fine-tuning for the ASV table, the 97% representative
sequences were aligned with MAFFT^[85]41 using the ‘alignment’ plugin
and variable positions were masked with ‘mask’ function. A phylogenetic
tree was built with the ‘Fasttree’ function^[86]42 in the ‘phylogeny’
plugin and then rooted with the ‘midpoint-root’ function.
Biodiversity and statistical analyses
Microbial community analyses were conducted with R package
vegan^[87]43. A Kruskal-Wallis test in R software^[88]44, with α=0.05,
was used for all statistical analyses and Dunn’s test for post-hoc
comparisons. To normalize sequencing output among samples, we rarefied
the ASV/OTU table to 959 reads per sample. Alpha diversity indices,
Shannon index (H’) was calculated by ‘diversity’ function, species
richness (S) was counted by ‘specnumber’ function and species evenness
(J) was calculated by following formula
[MATH:
J=H′l
nS :MATH]
, Faith’s phylogenetic diversity then was calculated by ‘pd’ function
of picante package. For beta diversity, dissimilarities among microbial
communities were measured by Bray-Curtis distance and conducted with
principal coordinates analysis (PCoA). PERMANOVA (permutational
multivariate analysis of variance) with pairwise comparisons, ANOSIM
(analysis of similarity)^[89]45 and ADONIS (permutational multivariate
analysis of variance using distance matrices)^[90]46 were used to test
heterogeneity of microbial communities among host species.
Network analysis for identifying co-occurrence microbial community members
Network analysis of co-occurrence microbial community members was done
with the R package igraph^[91]47. A co-occurrence matrix was
constructed from the OTU table, according to SparCC correlation
coefficients^[92]48 (≥0.3) between OTUs (calculated by ‘sparcc’
function of SpiecEasi R package^[93]49); these coefficients were also
used for assessing length of edges on the network. The latter was
conducted with the fast greedy modularity optimization algorithm^[94]50
to identify clusters in the network. Nodes with <5 connection degrees
were removed from the network and hub nodes from each cluster were
extracted for further community structure analyses.
Metagenome and functional prediction
Tax4Fun^[95]51 was used to predict the metagenome, which was based on
SILVA 123 16S database to evaluate potential functions of flying
squirrels’ gut microbiota. An OTU table was used for predicting Kyoto
Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) relative
abundances and being categorized by KEGG pathways. The FTU (fraction
taxonomic units unexplained) scores were evaluated for reliability of
metagenome prediction (0.21 ± 0.09; Fig. [96]S2). Metabolic pathway
enrichment analysis was conducted with the R package gage^[97]52
(testing by ‘gage’ function to test metabolic pathway enrichment by
comparing equal gene abundance distribution for within FS categories
and by comparing mean gene abundance among FS categories; significant
pathways were identified from one-tailed tests for up-regulation). The
P values from multiple-testing were adjusted with false discovery rate
(FDR), with a p-value adjusting function embedded in the gage package;
significances of enrichment analyses were defined by FDR q-value <0.05.
Enrichment scores were calculated according to the gene-set enrichment
analysis (GSEA) algorithm of DAVID bioinformatics
resources^[98]53,[99]54.
Results
Characterization of folivorous flying squirrel gut microbiota
The Illumina MiSeq platform generated a total of 3,665,256 high quality
paired-end sequences, with an expected sequence length of 464 bp and an
average of 9,561 non-chimeric reads/sample, ranging from 959 to 33,265
reads/sample. The SILVA NR123 SSU rRNA database was applied to identify
a total of 1,104 OTUs by re-clustering ASVs to 97%-identity OTUs. On
average (mean ± SD), PVO had the most bacterial OTUs (111.47 ± 106.07),
followed by TX (108.50 ± 56.01), PPG (107.00 ± 33.87), and PAL
(76.67 ± 30.89). Overall, 1.99% of OTUs were shared by all four
species, whereas 2.72-55.1% of unique OTUs were present in each of the
four species (Fig. [100]S3A). Since the two species of Petaurista (PAL
and PPG) shared a substantial proportion of OTUs (41.2%; 103 OTUs;
Fig. [101]S3B), a comparison was also done by combining the two
Petaurista, resulting in 4.08% of OTUs shared in three genera and
12.3-55.1% unique OTUs present in each genus (Fig. [102]S3C). The two
species of large flying squirrels (Petaurista) were much more similar
in gut microbiota than species of other genera, whereas PVO harbored
more than four-fold unique OTUs compared to either of the other two
genera.
Regarding relative abundance of all flying squirrel fecal microbiota
(Fig. [103]1A), the most abundant microbial phylum identified was
Firmicutes (average 62.36-100%), accounting for ~95% of gut microbiota
in the two host species of Petaurista (Fig. [104]2, Fig. [105]S1). The
next dominant phyla were Bacteroidetes (0-7.2% on average),
Actinobacteria (2.16-4.46% on average) and Cyanobacteria (0.63-3.42% on
average; Fig. [106]1B,C and Fig. [107]S4). Interestingly, Bacteroidetes
was absent in the two species of Petaurista, but accounted for 0-19.29%
in PVO and 0-0.52% in TX. It was noteworthy that from 1.21 (PVO) to
1.90% (TX) of OTUs were low confidently identified (<90% identity) in
database and were suspected to be unknown prokaryotic species.
Figure 1.
[108]Figure 1
[109]Open in a new tab
Relative abundance profiles of four folivorous flying squirrel species.
(A) Phylum level relative abundance (%) barplots of gut microbiota from
29 FS individuals (19 PVO, 4 TX, 3 PPG, 3 PAL). Relative abundance of
(B) Firmiutes and (C) Bacteroidetes grouped by four flying squirrel
species. (D) Bacteroidetes to Firmicutes ratio of flying squirrel gut
microbiota. PVO, Pteromys volans orii; TX, Trogopterus xanthipes; PPG,
Petaurista philippensis grandis; PAL, Petaurista alborufus lena.
Figure 2.
[110]Figure 2
[111]Open in a new tab
Alpha diversity indices of gut microbiota of four species of flying
squirrels. (A) Margalef’s species richness index, (B) Shannon’s
diversity index, (C) Pielou’s evenness index, and (D) Faith’s
phylogenetic diversity. None of the indices differed among the four
taxa, based on a Kruskal-Wallis test (p=0.69 for Margalef’s species
richness index, p=0.76 for Shannon’s diversity index, p=0.95 for
Peilou’s evenness index, and p=0.97 for Faith’s phylogenetic
diversity). PVO, Pteromys volans orii; TX, Trogopterus xanthipes; PPG,
Petaurista philippensis grandis; PAL, Petaurista alborufus lena.
Additionally, a lower Bacteroidetes to Firmicutes ratio (B/F ratio) was
regarded as obesity-related microbial biomarkers in laboratory models
and human studies; in other words, the B/F ratio may reflect ability of
energy extraction via gut microbiota^[112]55,[113]56. The B/F ratios of
four FS were measured (Fig. [114]1D). All FS had extreme low B/F ratio
(less than 1 and close to 0), although PVO had a slightly higher B/F
ratio (maximum 0.29). The FS B/F ratios are compared to other
folivorous mammals (with higher B/F ratios) in the Discussion.
Although gut microbiota of PVO was more diverse in phylum composition,
there were no differences (species level) among all host species in
Shannon’s diversity index (p=0.76, Kruskal-Wallis test), Margalef’s
species richness index (p=0.69), Pielou’s evenness index (p=0.95), or
Faith’s phylogenetic diversity (p=0.97; Fig. [115]2). Further, we
discussed host-specific order and lower levels of microbial composition
by using co-occurrence network analysis (see below). A principal
coordinates analysis (PCoA) (Fig. [116]3) presented gut microbial beta
diversity among host species and had three clusters corresponding to
three FS genera (PERMANOVA, pseudo-F=25.07, p=0.001).
Figure 3.
[117]Figure 3
[118]Open in a new tab
Principle coordinates analysis (PCoA) by Bray-Curtis distance metric of
gut microbiota from four species of flying squirrels. Gut microbiota of
flying squirrels are indicated by colors, Pteromys volans orii (PVO;
purple), Trogopterus xanthipes (TX; blue), Petaurista philippensis
grandis (PPG; red) and Petaurista alborufus lena (PAL; orange). Three
distinct groups were distributed along the first two PCs. Shaded
ellipses represented the 95% confidence intervals of FS groups.
PERMANOVA, ANOSIM and ADONIS tests for the four host species differed
(p=0.001, p=0.001, and p=0.004, respectively).
Network analysis identifies distinction among microbiota at lower taxonomic
level
Co-occurrence network analysis was done in addition to PCoA, because it
not only identified clusters but also enabled visualization of taxa
underlying distinctions among clusters (Fig. [119]4A). Clusters
identified corresponded to the three genera in PCoA graph. Moreover,
the network also identified centers (hubs) of core microbes at a finer
taxonomic level. Taxonomy of the three clusters were profiled
(Fig. [120]4B); all were dominated by families Lachnospiraceae and
Ruminococcaceae which belong to order Clostridiales (>80%). Both PVO
and TX clusters contained Clostridiales vadinBB60 group in their core
microbiota (0.74 and 10.74%, respectively). However, only PVO cluster
included additional featured families Christensenellaceae (1.43%),
Clostridiales Family XIII (0.64%), Elusimicrobiaceae (Elusimicrobia,
0.42%), and two families under Bacteroidales, Muribaculaceae (was named
as [121]S24-7, 8.29%) and Prevotellaceae (0.78%). Family
Eggerthellaceae, which belongs to (Actinobacteria, Coriobacteriales),
was common in all FS and accounted for 3.68, 5.6 and 1.7% in small
(PVO), medium (TX), and large (PPG and PAL) FS, respectively. Family
Coriobacteriales Incertae Sedis which also belongs to phylum
Actinobacteria and uncultured Rhodospirillales (Proteobacteria)
bacteria were detected in both large (Coriobacteriales: 0.75%,
Rhodospirillales: 0.46%) and small (Coriobacteriales: 2.34%,
Rhodospirillales: 0.39%) FS. Conversely, Akkermansiaceae
(Verrucomicrobia) was present in large (1.18%) and medium (3.08%) FS.
Gastranaerophilales, which was named YS2, a non-photosynthetic
Cyanobacteria, was present in small (1.39%) and medium (2.45%) FS.
Figure 4.
[122]Figure 4
[123]Open in a new tab
Core gut microbiota composition of three genera of flying squirrels.
(A) Correlation network of OTUs in four species of flying squirrels.
SparCC correlation (≥0.3) identified sub-communities based on a fast
greedy modularity optimization algorithm. Nodes on the network were
clustered into three groups delimited by host genus and body sizes;
purple nodes: PVO/small FS featured OTUs; blue nodes: TX/medium FS
featured OTUs; red node: PPG and PAL/large FS featured OTUs. Nodes with
labeling species, genus, and/or family taxonomy names were hub/central
OTUs within clusters which were identified by connection degree> the
third quantile within a cluster. (B) Gut microbiota phyla/order/family
stacked relative abundance barplots of three clusters (corresponding to
PVO, TX and PPG & PAL) based on the correlation network. The phyla and
orders are shown on the y-axis and bars filled with various colors
represent families belonging to corresponding phylum and order.
We further identified hub OTUs of each cluster on the network according
to node connectivity (third quantile or greater connection degrees
within cluster; Fig. [124]4A). There were 33, 8, and 11 hub OTUs
detected from PVO, TX, and PPG-PAL clusters, respectively. Although
three clusters shared a large proportion of Lachnospiraceae and
Ruminococcaceae at the family level, distinct genera and species served
as key OTUs for discriminating microbial compositions of FS hosts with
varying body sizes. For example, genera Roseburia and Shuttleworthia
were two core microbes of the PPG-PAL cluster, as was Ruminiclostridium
of the TX cluster, the genera Roseburia, Acetitomaculum, Oscillibacter,
Enterorhabdus, Moryella, Butyricicoccus, Coprococcus, Blautia,
Acetitomaculum, Eubacterium nodatum related genera, and Lactonifactor
longoviformis of the PVO cluster.
Folivorous flying squirrels’ gut microbiota harbored high energy producing
potential and small flying squirrels’ microbiota enriched more diverse
pathways
Using Tax4Fun as a metagenome predictive exploratory tool, genes were
categorized into KEGG Orthology metabolic pathways. All predicted KEGG
Orthology (KOs) were mapped to 43 (“within” FS comparisons) and 94
(“between” FS comparisons) pathways among the 154 KEGG metabolic
pathways. Each pathway was tested with gene-set enrichment by
comparison to expected gene abundance within each FS category (“within”
comparisons; Fig. [125]5A, Fig. [126]S5, and Table [127]S1) and mean
gene abundance among FS categories (“between” comparisons;
Fig. [128]5B–D and Table [129]S1). There were 43 microbial metabolic
pathways enriched in any or all of the three FS categories through the
“within” comparisons). The top 10 enriched pathways were mainly related
to carbohydrate and amino acid metabolism (Fig. [130]5A), followed by
nucleotide, glycan, cofactor-vitamin and lipid metabolism pathways, and
phytochemical or xenobiotic degradation (Fig. [131]S5). For the
“between” FS comparisons, various carbohydrate metabolic pathways in
the small-FS gut microbiota were more enriched than in medium or large
FS, such as fructose and mannose metabolism (ko00051), pentose
phosphate pathway (ko00030), starch and sucrose metabolism (ko00500),
glycolysis / gluconeogenesis (ko00010), and amino sugar and nucleotide
sugar metabolism (ko00520); in addition, methane metabolism (ko00680)
was also enriched in the small-FS gut microbiota (Fig. [132]5B). A few
carbohydrate and amino acid metabolic pathways were more significantly
enriched in medium- versus large-FS gut microbiota, e.g. pyruvate
metabolism (ko00620), carbon metabolism (ko01200), TCA cycle (ko00020),
butanoate metabolism (ko00650), prokaryotic carbon fixation (ko00720),
and glycine, serine and threonine metabolism (ko00260) (Fig. [133]5C).
However, only porphyrin and chlorophyll metabolism (ko00860) – a
cofactor /vitamin metabolic pathway– was significantly enriched in the
large-FS gut microbiota compared to both smaller flying squirrels’; and
starch and sucrose metabolism was the only significant pathway of the
large-FS gut microbiota that was enriched compared to medium FS.
Figure 5.
[134]Figure 5
[135]Open in a new tab
Enrichment analysis for predictive KEGG metabolic pathways of “within”
and “between” flying squirrels’ gut microbiota. (A) Top 10 of “within”
FS comparisons of KEGG metabolic pathways. All 43 significantly
enriched pathways are shown in Fig. [136]S5. (B–D) The “between” FS
comparisons of KEGG metabolic pathways; (B) The enriched metabolic
pathways of small FS by comparisons to medium and large FS,
respectively; (C) medium FS by comparison to small and large FS; (D)
large FS by comparison to small and medium FS. The dash lines indicate
that the FDR q-value = 0.05. PVO, Pteromys volans orii; TX, Trogopterus
xanthipes; PAL, Petaurista alborufus lena; PPG, Petaurista philippensis
grandis.
Discussion
Strictly folivorous mammals may rely on gut microbiota to maintain a
mass-specific metabolic rate
Folivorous mammals consume leaves with low nutrient content and have
specific ecological niches and physiological
adaptations^[137]21,[138]22. Symbiotic gut microbiota likely have
important roles to support ecological and evolutionary
adaptations^[139]16. As small endothermic mammals have larger body
surface area (BSA) to mass ratio, they consume energy to compensate
heat loss at a faster rate than larger endothermic mammals in a resting
condition. Furthermore, strictly folivorous arboreal mammals have a
lower limit of body mass of ~1 kg^[140]57. Therefore, to maintain high
MSMR, small leaf-eating mammals must rely either on a diet of
high-nutrient content, or on a high digestion-absorption rate assisted
by gut microbiota^[141]4,[142]57. Our study contributed to
understanding distinct gut microbiomes associated with folivorous FS of
various body sizes, i.e., two large (PAL and PPG), one medium (TX), and
one small FS (PVO), which reflect host’s diet niche and metabolic
efficiency. Although distinction of FS gut microbial compositions may
have been confounded by factors inherent to the hosts (e.g. taxonomy,
physiology) or by environmental factors (e.g. diet, geographical
distribution), this study was valid and valuable in terms of all
sub-tribe Pteromyina flying squirrels (Sciuridae, Brandt, 1855) based
on Kleiber’s law^[143]3 (i.e. body-size scaling).
Flying squirrels with distinct body sizes and dietary niches had distinct gut
microbiota
Nineteen (1.99%) gut microbial OTUs were shared among all four species
of FS, whereas 54-103 (7.9-41.2%) OTUs were shared between any two FS
species (Fig. [144]S3). We re-analyzed data of Muegge et al. (2008) of
six herbivorous hindgut fermenters housed in US zoos (two zebras,
African elephant, two black rhinos, African wild ass, orangutan, and
rabbit). There were 478 OTUs identified, but no OTUs shared among these
divergent species. In contrast, four FS in this study are phylogenetic
kin. Despite geographic disparity, they occupy similar ecological
niches – inhabiting treetops and consuming leaf-based diets. The two
large FS (PPG and PAL) with comparable body sizes harbored gut
microbiota with similar composition (42.2%), whereas the medium-small
FS in our study with discrete ranges of body sizes harbored distinct
gut microbiota (Fig. [145]3). In the two large FS, the majority (95%)
of gut microbiota were composed of the phylum Firmicutes with two
dominant families, namely Lachnospiraceae (52.52%) and Ruminococcaceae
(40.62%) (Fig. [146]4). Both of these families are common in mammalian
guts, especially highly abundant in herbivores, due to their ability to
degrade complex polysaccharides to SCFAs^[147]58–[148]60. In contrast,
in medium and small FS, Firmicutes (Lachnospiraceae, 46.27% and
Ruminococcaceae, 32.79%) comprised only ~70% of gut microbiota. Based
on a genomic comparison study, both of these two dominant bacterial
families are common in gut environments and have similar fibrolytic
functions^[149]61.
In addition, two minor Firmicutes (Christensenellaceae, 1.95% and
Clostridiales vadinBB60, 2.78%) were also enriched in medium and small
FS. It was reported that Christensenellaceae was significantly enriched
in humans with a lean body mass index (BMI; < 25)^[150]62; however,
Clostridiales vadinBB60 was enriched but Christensenellaceae was
decreased in mice on a high-fat diet^[151]63. Based on our findings and
previous studies, that Clostridiales vadinBB60 were enriched in
medium-small FS implied that TX’s and PVO’s lipid-rich diet (pine nuts
and seeds; see Introduction) in either captive or wild environments and
that Christensenellaceae were more abundant in lean animals was
consistent with greater heat losses in smaller body size FS.
There were up to 19.3% phylum Bacteroidetes in gut microbiomes of PVO.
The Bacteroidetes family Muribaculaceae (was known as S24-7),
accounting for ~8.3% of core microbiota in PVO, is a common bacterial
family in herbivore gastrointestinal tracts; it has high potential for
degradation of plant glycans and can be enriched by high-fat diets in
laboratory mice^[152]64–[153]69. Presumably, Muribaculaceae partially
replaced the function of Firmicutes for cellulose degradation and may
have promoted lipid absorption in gut microbiomes of TX and PVO that
ate seeds with high lipid content^[154]25,[155]29–[156]31,[157]70.
It is noteworthy that some bias may have occurred while assessing gut
microbial composition in this study (also see Limitations and
perspectives below). The four species of FS in this study are naturally
distributed in disparate geographical regions of Asia (China, Japan,
and Taiwan), and adapted to local environments, e.g. climate, phenology
and different plant-source diet, which may also affect their gut
microbial composition. Despite sampling bias and study design
limitations, we tried to explore body-size issue of host-microbe
interactions based on comparative physiology (Kleiber’s law). We
limited research targets to four FS that shared largely evolutionary
and ecological niches: shared common ancestors, adapted leaf-based
diets and inhabited treetops.
Bacteroidetes/Firmicutes ratio may be related to gut microbiota and host
metabolism relationships
Regarding the Bacteroidetes to Firmicutes ratio of gut microbiota,
increased Firmicutes was associated with obesity in a laboratory mouse
model and humans^[158]55,[159]56,[160]71; however, wild animals,
especially herbivorous mammals, harbor much more Firmicutes than
Bacteroidetes^[161]72–[162]74. For strictly leaf-based diet cases,
folivorous mammals can be either foregut- or hindgut-fermenters, with
distinct strategies to degrade a high-fiber diet^[163]9, thereby
contributing to unique gut microbial communities. In the present study,
as hindgut-fermenters, folivorous flying squirrels had a lower (0-0.29
in PVO; 0-19.3% of Bacteroidetes versus 62.4-100% of Firmicutes) or
even zero (absence of Bacteroidetes in all large FS) ratio of
Bacteroidetes/Firmicutes in their gut microbiota, in contrast to
non-ruminant foregut-fermentative folivores (e.g. 23.2-88.4% of
Bacteroidetes versus 10.2-51.79% of Firmicutes in Colobus monkey and
langur; re-analyzed data from Ley et al. 2008)^[164]13,[165]75,[166]76.
Other hindgut-fermentative folivores also had a low
Bacteroidetes/Firmicutes ratio in their gut microbiota, e.g., 13.3% of
Bacteroidetes versus 68.4% of Firmicutes in black Howler Monkey
(Alouatta pigra)^[167]77. Taken together, we inferred that folivorous
mammals independently acquired their own unique gut microbiota in
response to distinct digestive strategies, i.e., foregut- or
hindgut-fermentation^[168]13.
Gut microbiota drive biomass conversions of leaf-based diets
Mammals have a variety of diets that create taxonomic and functional
diversities of gut microbiota^[169]13,[170]18. Variations in gut
microbiota affect multiple aspects of host physiology^[171]14,
especially adaptation for extracting energy from various types of
feed^[172]16. Muegge et al. (2011) indicated that convergence of
mammalian gut microbiota is related to dietary type instead of host
phylogeny. However, there are few studies on variation in gut
microbiota in relation to host body size and metabolic rate. In this
study, we focused on gut microbiota of strictly folivorous, small
mammals for two reasons: 1) leaf-based food sources, with a high fiber
content, are expected to supply marginal nutrients; and 2) small
mammals usually need to maintain a higher MSMR. Thus, we were also
interested in gut-microbiota aided metabolic pathways of energy
extraction from low-quality diets.
In previous studies, gut microbiota of two folivorous mammals (Yunnan
Snub-Nosed Monkey Rhinopithecus bieti, foregut fermenter and Petaurista
alborufus lena, hindgut fermenter) were enriched with carbohydrate
metabolic pathway genes, the second most abundant orthologues after
those involved in protein/amino acid metabolism^[173]73,[174]78. With a
high diversity of glycoside hydrolases (GHs), folivores can degrade a
variety of lignocellulosic biomass from leaf-based diets. Valid
microbes harbored in Rhinopithecus bieti were phyla Bacteroidetes
(Bacteroides vulgates, and B. fragilis), Fibrobacteres (Fibrobacter
succinogenes), and Spirochaetes, whereas P. alborufus lena had
predominately Firmicutes^[175]73,[176]78.
For this study, we predicted gut metagenomes of four species of
folivorous flying squirrels. Irrespective of body size, carbohydrate
and amino acid metabolism were potentially enriched in metabolic
pathways. Gut microbiota harboring more energy-producing genes may have
been due to high energy demand of small body-size mammals (i.e. PVO).
The medium FS (TX), with three-fold higher estimated MSMR than large
FS, had energy-producing enriched in microbiota, although the
difference was not significant. Conversely, large FS microbiota were
composed of>90% of Firmicutes bacteria (mainly from families
Lachnospiraceae and Ruminococcaceae), which might harbor most
energy-producing genes of whole large FS microbiome. Despite decreasing
Firmicutes in medium and small FS microbiota, there were other
sub-dominant phyla such as Bacteroidetes (Families Bacteroidaceae,
Prevotellaceae, Muribaculaceae/S24-7), Cyanobacteria (Order
Gastranaerophilales/YS2), Proteobacteria, and Verrucomicrobia (Family
Akkermansiaceae) that may have complemented energy-producing functions
or replaced Firmitutes’ ecological roles in the gut.
In addition, as mentioned above, our metagenome prediction was
consistent with previous studies that amino acid biosynthesis genes are
the most abundant orthologues in herbivorous mammalian gut
microbiota^[177]18,[178]73,[179]78. However, herbivorous diets may
provide limited protein intakes and incomplete essential amino acid
composition. Like most small hindgut fermenters, a digestive strategy
perhaps used by folivorous flying squirrels is coprophagy (also known
as cecotrophy). Many small mammals (most are rodents and rabbits) with
plant-based, low-protein diets acquire nitrogen through coprophagy,
which provides energy and increases protein uptake^[180]9,[181]79.
Nitrogen sources are mostly converted to amino acids by cecal microbes
of small hindgut fermenters. Coprophagy by flying squirrels occurs in
the wild (personal, unpublished observations). Moreover, based on
metabolic function predictions, amino-acid-related genes were
relatively complete in the gut microbiota of flying squirrels
Therefore, we inferred that folivorous flying squirrels obtain
essential nutrients (products of microbial metabolism) through
coprophagy, similar to other small hindgut fermenters.
To cope with both poor diet and rapid heat-loses, arboreal folivorous
flying squirrels adapted by nurturing suitable microbes in their
enlarged ceca. Our findings provided insights regarding comparative
physiology of thermal regulation would supported by adaptations of gut
microbiota. We demonstrated that gut microbiota compositions were
closely linked to differences in body sizes/MSMR in folivorous flying
squirrels. In particular, microbial gene counts of metabolic pathways
were also associated with body-size scaling of flying squirrels.
Estimated high MSMR in small flying squirrels (PVO) would demand
greater potential to extract energy by “co-operation” with gut
microbiota. In addition, understanding adaptation of leaf-based dietary
niche of flying squirrels may elucidate how microbial assistance
enables these animals to function on low-quality diets. Large flying
squirrels mutualizing with>90% cellulolytic microbes (Firmicutes) was
consistent with their strictly leaf-based diet. In contrast, small
flying squirrels harbored additional versatile phyla capable of
cellulolytic activity as well as utilizing a high-lipid diet (e.g.
Eggerthellaceae^[182]80). These results were consistent with the
observation that a dietary adaptation of small flying squirrels is
supplementing their leaf consumption with increased seed
intake^[183]29. Further studies are required to characterize how
dietary variations affect composition and function of gut microbiota.
Limitations and perspectives
Although our study revealed interesting aspects of mammalian gut
microbial symbionts regarding their diversities and metabolic
potentials from the perspective of hosts’ body-weight scaling, results
were preliminary and must be interpreted with precaution because our
study remained largely exploratory in nature. First, more animals of
each species involved could have been included and replicate species
(at least two) of comparable sizes added, especially for medium and
small body size, preferably from one locality with more uniform diets,
either complete natural diet or one artificial diet. These measures
would have prevented the compounding effects caused by geographical and
diet heterogeneity which our current data cannot overcome. Second, some
experimental approach must be drawn into the future study design, such
as using species of small and large flying squirrels and applying some
control of animals’ genetic backgrounds by using animals of the same or
similar mitochondrial haplotypes (a commonly used genetic marker for
studying wild animals). Finally, the resolution power of the prediction
on metabolic pathway, although widely employed, is yet to be confirmed.
It would be desirable to examine expression levels of a suite of
enzymes that are relevant to energy production linked to gut microbial
contributions.
Supplementary information
[184]Supplementary information.^ (739.8KB, pdf)
Acknowledgements