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Molecular Ecology. 2022;31:4146–4161.wileyonlinelibrary.com/journal/mec
Received: 17 August 2021
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Revised: 29 April 2022
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Accepted: 20 May 2022
DOI: 10.1111/mec.16559
ORIGINAL ARTICLE
The faecal metabolome of black howler monkeys (Alouatta
pigra) varies in response to seasonal dietary changes
Elizabeth K. Mallott1,2,3 | Lotte H. Skovmand4 | Paul A. Garber5,6,7 |
Katherine R. Amato1
1Department of Anthropology,
Northwestern University, Evans ton,
Illinois, USA
2Department of Biological Sciences,
Vanderbilt University, Nashville,
Tennessee, USA
3Vanderbilt Microbiome Innovation
Center, Vanderbilt University, Nashville,
Tennessee, USA
4Department of Biolog y, McGill
University, Montreal, Quebec, Canada
5Department of Anthropolog y, University
of Illinois at Urbana- Champaign, Urbana,
Illinois, USA
6Program in Ecolog y, Evolution, and
Conser vation Biology, University of Illinois
at Urbana- Champaign, Urbana, Illinois,
USA
7International Centre of Biodiver sity and
Primate Conser vation, Dali University,
Dali, China
Correspondence
Katherine R. Amato, Department of
Anthropology, Northwestern University,
Evanston, IL, USA.
Email: katherine.amato@northwestern.
edu
Funding information
Canadian Institute for Advanced Research;
National Geographic Society, Grant/
Award Number: Wait t grant (W139- 10);
National Science Foundation, Grant/
Award Number: Graduate Research
Fellowship Program; University of Illinois
Graduate College, Grant/Award Number:
Disser tation Travel Grant; University
of Illinois Research Board; Vanderbilt
Microbiome Innovation Center
Handling Editor: Loren Rieseberg
Abstract
Mammals rely on the metabolic functions of their gut microbiota to meet their en-
ergetic needs and digest potentially toxic components in their diet. The gut micro-
biome plastically responds to shifts in host diet and may buffer variation in energy
and nutrient availability. However, it is unclear how seasonal differences in the gut
microbiome influence microbial metabolism and nutrients available to hosts. In this
study, we examine seasonal variation in the gut metabolome of black howler mon-
keys (Alouatta pigra) to determine whether those variations are associated with dif-
ferences in gut microbiome composition and nutrient intake, and if plasticity in the
gut microbiome buffers shortfalls in energy or nutrient intake. We integrated data on
the metabolome of 81 faecal samples from 16 individuals collected across three dis-
tinct seasons with gut microbiome, nutrient intake and plant metabolite consumption
data from the same period. Faecal metabolite profiles differed significantly between
seasons and were strongly associated with changes in plant metabolite consumption.
However, microbial community composition and faecal metabolite composition were
not strongly associated. Additionally, the connectivity and stability of faecal metabo-
lome networks varied seasonally, with network connectivity being highest during the
dry, fruit- dominated season when black howler monkey diets were calorically and nu-
tritionally constrained. Network stability was highest during the dry, leaf- dominated
season when most nutrients were being consumed at intermediate rates. Our results
suggest that the gut microbiome buffers seasonal variation in dietary intake, and that
the buffering effect is most limited when host diet becomes calorically or nutritionally
restricted.
KEYWORDS
black howler monkey, diet– microbiome interactions, faecal metabolites, plant metabolites
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in
any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.
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1 | INTRODUC TION
Mammals that consume diets rich in cellulose and other plant fibres
depend on gut microbes to extract short chain fatty acids (SCFAs)
and additional energy sources from food items (Flint et al., 2008;
Flint & Bayer, 2008; Lambert, 1998; Mackie, 20 02). In mammals re-
lying heavily on plant foods, the gut microbiome plays an essential
role in breaking down phenolics and other plant toxins (Dearing &
Kohl, 2017; Greene et al., 2020; Kohl et al., 2014, 2016). Additionally,
dietary shifts, environmental changes in food availability, rainfall and
temperature, and habitat differences, such as forest structure and
anthropogenic changes, appear to more strongly influence the gut
microbiome composition of animals that ingest a high proportion
of indigestible plant fibre than animals that ingest a diet composed
principally of fruit, flowers and/or invertebrates (Frankel et al., 2019;
Greene et al., 2019). Moreover, there is evidence of functional and
compositional convergence in the gut microbiome of animals with
high- fibre diets, both within and across taxonomic groups (Amato
et al., 2019; Hale et al., 2018; Ley et al., 2008; Muegge et al., 2011).
Gut microbial composition can rapidly change in response to
dietary variation (e.g., high fat to low fat consumption) (David
et al., 2014; Turnbaugh et al., 2009). Several studies have identi-
fied seasonal shifts in gut microbial composition, including in wild
mice (Apodemus sylvaticus) (Maurice et al., 2015), wild Bale mon-
keys (Chlorocebus djamdjamensis) (Trosvik, Rueness, et al., 2018),
captive and wild giant pandas (Ailuropoda melanoleuca) (Wu
et al., 2017; Xue et al., 2015), wild flying squirrels (Pteromys vo-
lans orii) (Liu et al., 2019), wild sage grouse (Centrocercus uropha-
sianus) (Drovetski et al., 2019), wild Verreaux's sifakas (Propithecus
verreauxi) (Springer et al., 2017), wild tench (Tinca tinca) (Dulski
et al., 2020) and wild geladas (Theropithecus gelada) (Baniel
et al., 2021; Trosvik, Muinck, et al., 2018). Seasonal shifts in the
gut microbiome of these species are linked to changes in the spe-
cific foods consumed, food availability or macronutrient compo-
sition of the diet (Kartzinel et al., 2019; Orkin et al., 2019; Re n
et al., 2017). For example, in African great apes (Gorilla gorilla gorilla
and Pan troglodytes troglodytes), the gut microbiome contains more
fibre- degrading taxa and higher cellulose degradation functional
potential when individuals are consuming a leaf- heavy diet (Hicks
et al., 2018). In Chinese alligators (Alligator sinensis) and hibernat-
ing ground squirrels (Ictidomys tridecemlineatus), mucin- degrading
gut microbial taxa increase in abundance during periods of fast-
ing, when less energy is available from dietary sources (Carey
et al., 2012; Tang et al., 2019). Gut microbial community stabil-
ity and resilience improve host health by reducing long periods of
gut microbial dysbiosis (Allaway et al., 2020; Sommer et al., 2 017).
However, the ability of the microbiome to plastically respond to
short- and long- term changes in environmental conditions could
also be important for host health and fitness. This plastic response
in gut microbiome composition and function influences host phe-
notype, resulting in variable host physiology and behaviour in
different environmental circumstances (Davidson et al., 2018;
Moeller & Sanders, 2020; Stearns, 1989; West- Eberhard, 2003).
Evidence is beginning to emerge that diet- related gut micro-
bial changes may buffer energy and nutrient availability. In hu-
mans, we see marked, rapid shifts in gut microbiome composition
and SCFA production immediately after an ultramarathon (Grosicki
et al., 2019) or in response to increases of other forms of exercise
(Estaki et al., 2016; Keohane et al., 2019), that are consistent with
an increase in gut microbial efficiency beneficial for host health. Gut
microbial community composition shifts also have been observed in
pregnant and lactating monkeys, resulting in a more metabolically ef-
ficient gut microbiome (Mallott et al., 2020; Mallott & Amato, 2018).
Similarly, seasonal shifts in gut microbiome composition and SCFA
production in black howler monkeys (Alouatta pigra) appear to com-
pensate for decreases in energy intake, buffering against nutrient
and energy shortfalls (Amato et al., 2015).
The extent to which changes in microbiome composition result
in shifts in potential host- relevant functions and microbial metab-
olism (beyond changes in SCFA production) remains unclear. The
microbial functions actively being expressed vary more between
individuals than either gene family or metabolic pathway presence
in the microbiome, and variability in microbial gene expression
appears to play a larger role in influencing host phenotypic plas-
ticity than do changes in the taxonomic composition or functional
potential of the gut microbiome (Barroso- Batista et al., 2020;
Heintz- Buschart & Wilmes, 2018; Tanca et al., 2017 ). Thus, studies
of gut microbiome composition and function using marker gene
or metagenomic sequencing may miss important variation in ac-
tively expressed microbial functions. Metabolomics, identifying
the small molecules produced during metabolism, allows us to ex-
amine how microbial metabolism responds to changes in nutrient
and energy intake in the host (Bäckhed & Crawford, 2010; Ursell
et al., 2014). Several studies have shown that microbially associ-
ated metabolites are strongly linked to host hea lth and met abolism
and respond to dietary change in much the same way as microbi-
ome composition (Filippis et al., 2016; Maier et al., 20 17; Mchardy
et al., 2013; Sharon et al., 2014). Metabolomics offers greater
insight into the fine- scale plasticity in microbial metabolism that
acts as a buffering agent against nutrient and energy shortfalls.
Investigating how the metabolome responds to changing diet and/
or energetic needs in wild systems helps us understand the role of
the gut microbiome in buffering energetic or nutrient shortfalls in
animals; however, few studies have used metabolomics to examine
gut microbial functional shifts in wild animals (Garber et al., 2019;
Gomez et al., 2015, 2016).
To determine how host– microbe cometabolic processes dynam-
ically respond to seasonal changes in diet and nutrient intake, we
examined the metabolome of a black howler monkey population ex-
periencing seasonal changes in feeding behaviour, nutrient intake,
gut microbiome composition and SCFA production, as reported in
our previous research (Amato et al., 2014, 2015, 2 017). This pop-
ulation experiences three distinct seasons— Wet, Fruit- Dominated
(WFD), Dry, Leaf- Dominated (DLD), and Dry, Fruit- Dominated
(DFD)— each characterized by distinct energy and nutrient intake
profiles (Table 1). The intake of protein, energy, lipids, neutral
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detergent fibre and nonstructural carbohydrates was highest in the
WFD season and lower in the DLD and DFD seasons. Therefore, we
found this population optimal for addressing three research ques-
tions: (i) Does the metabolome of black howler monkeys var y sea-
sonally? (ii) If so, are seasonal changes in the metabolome associated
with changes in gut microbial community composition and linked to
changes in nutrient and energy intake? (iii) Do seasonal changes in
the metabolome suggest that the gut microbiome is buffering short-
falls in energy or nutrient intake? If buffering is occurring, then when
the consumption of a specific macronutrient declines, we expect to
see an increase in metabolite concentrations related to those spe-
cific macronutrient metabolic pathways.
2 | MATERIALS AND METHODS
2.1 | Field data collection
Behavioural data and faecal samples for microbiome and metabo-
lome analysis were collected from adult and juvenile black howler
monkeys (N = 16 individuals) in Palenque National Park, Mexico.
All juveniles included in this data set were over 1 year of age and
foraging independently (Amato et al., 2014). Data collection oc-
curred during three distinct 10- week sampling blocks: September–
November 2010 (WFD), January– March 2011 (DLD) and April– June
2011 (DFD). Activity and dietary data were collected during 20- min
instantaneous focal individual samples recorded 5 days per week.
Nutrient consumption was estimated from observational feeding
data and published plant nutritional content values. Plant material
for metabolite analysis was sampled from the 10 most consumed
food items in each season and preserved in 70% methanol. Faecal
samples were collected every 2 weeks from all 16 individuals and
stored in 96% ethanol. Once per month, an aliquot of one faecal
sample from each individual was stored in 80% methanol for fae-
cal metabolite analysis. In five instances, two samples for metabolite
analysis were collected for an individual during a single month. At
least one gut microbiome and gut metabolome sample was available
for each season for all individuals included in the analysis. For more
detailed data collection methods, see related publications (Amato
et al., 2014, 2015, 2017; Amato & Garber, 2014).
2.2 | 16S rRNA gene sequencing and analysis
DNA was extracted from 115 biweekly faecal samples from eight of
the 16 individuals (Mallott & Amato, 2021a), and the V1– V3 region of
the 16S rRNA gene was amplified and sequenced as described pre-
viously (Amato et al., 2014, 2015). Sequences were re- analysed in
qiime2 (2019.4) using the dada2 algorithm, adjusted for use with 454
sequencing data, and taxonomy was assigned using a Naïve Bayesian
classifier and the Greengenes 13.8 database.
2.3 | Metabolite extraction and analysis
Polar and nonpolar metabolites were extracted separately from
81 monthly faecal samples from 16 adult and juvenile individuals
(Mallott & Amato, 2021c) and 30 plant samples (Mallott & Amato,
2021b) following previously published protocols (Amato et al., 2017;
Garber et al., 2019; Gomez et al., 2015). Briefly, metabolites were
extracted in 1 ml of 70% methanol and sonicated. The resulting
lysed cell pellets were fractionated with 5 ml 70% methanol and
chloroform, centrifuged, and extracts of both fractions were evapo-
rated under vacuum at −60°C. Untargeted metabolomics were per-
formed on a GC/MS system. Spectra were compared to electron
impact mass spectrum libraries NIST08 (NIST) and W8N08 (Palisade
Corporation), and a library of 520 unique metabolites custom- built
by the University of Illinois Urbana- Champaign Metabolomics
Center. Data were normalized to an internal standard and sample
dry weight, and all metabolite concentrations were reported rela-
tive to hentriacontanoic acid per gram dry weight. While the me-
tabolomic methods used here may be biased against detecting lipids,
we expect that bias to be consistent across the sampling period and
therefore not affect seasonal comparisons in metabolite abundance.
Metabolite extraction, metabolomics and chromatogram process-
ing were carried out at the University of Illinois Urbana- Champaign
Metabolomics Center in the Roy J. Carver Biotechnology Center.
We examined differences in the metabolome across seasons using
permutational multivariate analysis of variance (PERMANOVA) of a
Bray– Curtis distance matrix of the entire set of metabolites detected
in the vegan and pairwiseAdonis packages (Martinez Arbizu, 2017;
Oksanen et al., 2019) in r (r - p r o j e c t . o r g ). Additionally, metaboanalyst
TAB LE 1 Energy and nutrient intake profiles for each of the three seasons experienced by the study population of black howler monkeys.
Energy consumption is expressed as energy per metabolic body weight (MBW) and nutrient intake values are expressed as grams per
metabolic body weight
Season
Energy (kcal/
MBW) Protein (g/MBW ) Lipid (g/MBW)
Nonstructural
carbohydrate (g/MBW)
Neutral detergent
fibre (g/MBW)
Wet, Fruit- Dominated
(WFD)
High
(177.4– 182.9)
Intermediate to high
( 9 . 4 – 1 1 . 9 )
High (3.2– 4.2) Intermediate to high
(2 9.0– 2 9.7)
High (42.3– 50.3)
Dry, Leaf- Dominated
(DLD)
Intermediate
(105.5– 172.4)
Intermediate to high
(7.7– 10.0)
Low to intermediate
(1.2– 3.2)
Low (12.4– 16.2) Intermediate
(27.2– 39.9)
Dry, Fruit- Dominated
(DFD)
Low
( 1 0 6 . 6 – 1 1 4 . 5 )
Low (4.7– 5.3) Low to intermediate
( 1 . 8 – 2 . 1 )
Intermediate to high
(16.3– 17.0)
Low (21.9– 24.7)
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4.0 (www.metab oanal yst.ca) was used to examine variation in the
metabolome across seasons. Only metabolites that were found in
>50% of samples were analysed. After removing samples with >90%
missing values, we calculated descriptive multivariate statistics and
conducted a pathway enrichment analysis. For the pathway enrich-
ment analysis, samples were normalized by sum and data mean-
centred and divided by the standard deviation of each variable. The
SMPDB (https://smpdb.ca/) metabolite library was used to group
metabolites into pathways. Pathways were considered to be more
abundant in one season compared with another if two or more me-
tabolites were detected in the pathway and all detected metabolites
were more abundant. The p- values were Holm- adjusted to account
for multiple comparisons for the pathway enrichment analysis.
2.4 | Microbiome and metabolite interactions
Monthly averages for each individual were calculated for microbiome
composition and matched with the monthly faecal metabolome data
(eight individuals, 44 matched data points; for individuals with two
faecal metabolome samples in a given month, an average value was
calculated). Seasonal averages for each individual were calculated for
comparisons between plant metabolome and faecal metabolome data
(13 individuals, 39 matched data points). As seasonal averages were
used for the comparison of plant and faecal metabolomes, this analysis
was performed across the entire study period, not for each season. A
Ma nte l test us ing a Sp earman co r relat ion me t h od was u s ed to co m p are
mean monthly individual microbial community composition at the ASV
(Amplicon Sequence Variant), genus, family and phylum levels with
metabolite composition in the vegan package (Oksanen et al., 2019)
in r. We used the ccrepe package (Bielski & Weingart, 2021) in r to
construct co- occurrence networks both within and between the mi-
crob iome an d fae cal meta bol ome, as wel l as between the plan t met ab-
olome and faecal metabolome. Compositionally corrected Spearman
correlation matrices were constructed for microbial ASVs, genera,
families and phyla present in >25% of samples and metabolites pre-
sent in >50% of samples. Networks were graphed from significant
positive correlations (q < 0.05 and rho >0.5) and network attributes
were calculated in cytoscape 3.8.0 (cytos cape.org). Within cystoscape,
the clus terMaker2 app was used to identify highly connected clusters.
Linear models were used to examine if edge betweenness differed
seasonally in r. Seasonal differences in edge betweenness for specific
microbe– metabolite interactions were examined using false discovery
rate (FDR)- corrected Kruskal– Wallis tests in r.
3 | RESULTS
3.1 | Metabolome composition
We positively identified 282 compounds in the metabolome of
black howler monkeys (Mallott & Amato, 2021d). Of the compounds
found in >50% of samples, 38.9% were lipids, 14.6% were amino
acids, 13.2% were carbohydrates, 11.1% were organic acids, 9.7%
were benzenoids, 4.2% were phenylpropanoids and 8.3% were other
classes of compoun ds. Palmiti c acid was the most abunda nt metabo -
lite (14.9 ± 9.5%), followed by stearic acid (14.4 ± 6.0%), beta- amyrin
(9. 3 ± 7.2%), vaccenic acid (6.3 ± 3.4%) and glycerol (4.5 ± 2.5%).
3.2 | Seasonal differences in metabolites
Faecal metabolite profiles, including rarely detected metabolites,
differed significantly between seasons (PERMANOVA, F = 14.329,
R2 = .269, p < .001). Pairwise comparisons showed that each season
was distinct (all padj < .001). Partial least squares discriminant analy-
sis (PLS- DA) of a subset of frequently occurring metabolites (>50%
of samples) did not find a significant separation between seasons
(p = .106) (Figure 1).
Pathway enrichment analysis found 48 pathways that were
differentially enriched between the WFD and DLD seasons (39
with two or more metabolites detected in the pathway), 12 dif-
ferentially enriched pathways between the DLD and DFD seasons
(nine with two or more metabolites detected in the pathway), and
24 pathways that were differentially enriched between the WFD
and DFD seasons (19 with two or more metabolites detected in
the pathway) (Table 2). Histidine metabolism (both catabolism and
anabolism), the malate– aspartate shuttle, nicotinate and nicotin-
amide metabolism and the degradation of several essential amino
acids (lysine, valine, leucine, and isoleucine) were enriched during
the DLD season compared with both the WFD and DFD seasons.
While several pathways— aspartate metabolism, bile acid biosyn-
thesis, citric acid cycle, mitochondrial electron transport chain,
phospholipid biosynthesis, and the metabolism of essential and
nonessential amino acids (arginine, proline, citrulline, cysteine, glu-
tamate, phenylalanine, tyrosine, valine, leucine and isoleucine)—
were enriched in the DFD season compa red wit h the WFD se ason,
no pathways were uniquely enriched in the DFD season compared
to both the WFD or DLD seasons. Similarly, no pathways were
uniquely enriched in the WFD season, though fructose and man-
nose degradation were enriched in the WFD season compared
with the DLD season.
3.3 | Metabolite and microbiome co-
occurrence networks
Few microbial genera were found to co- occur. A small cluster of
genera were significantly positively correlated when examining
com bi ned data from all three seasons— Desulfovibrio, Moglibacterium,
Sutterella, the p.75a5 genus within Erysipelotrichaceae, an unclas-
sified genus within Moglibacteriaceae, an unclassified genus within
Synergistaceae and an unclassified genus within Veillonellaceae
(Figure 2). Microbial association networks constructed using sam-
ples from each season yielded three (WFD), two (DLD) or no (DFD)
significantly positively correlated taxa.
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Faecal metabolite networks varied seasonally in both their con-
nectivity and stability. The metabolite network during the WFD
season had the lowest network connectivity— low mean shortest
path length, edge betweenness, betweenness centrality and net-
work density— which suggests that there are few interconnected
metabolic processes during this season (Table 3). The WFD season
metabolite network had a low mean clustering coefficient and a
low network density, indicating intermediate network stability. The
DLD season had intermediate to high network connectivity and
high network stability, indicating that metabolite networks during
this season might be robust to perturbations despite being some-
what interconnected. The DFD season metabolite networks had
the highest network conn ec tivity and lowest network st ability, sug-
gesting that many metabolic processes are intertwined during this
season but that the metabolic processes present are ephemeral or
easily perturbed.
The metabolites that were most important in the network also
varied seasonally. During the WFD season, metabolites related to
ketone body metabolism and lipid metabolism had the highest be-
tweenness centrality, suggesting that these metabolites are import-
ant to network functioning and communication (Figure 3). High edge
betweenness values (important connections with high rates of in-
formation transfer) were found between several essential and non-
essential amino acids, as well as between nucleic acids during the
WFD season. During the DLD season, while some metabolites with
high betweenness centrality were related to important metabolic
pathways, such as propanoate metabolism and lipid metabolism,
several highly connected metabolites with both high betweenness
centrality and edge betweenness were tannins and other antifeed-
ant compounds potentially harmful to the howlers (e.g., epicatechin
and protocatechuic acid). Metabolites that played a central role in
the DFD season were typically involved in lipid metabolism and the
metabolism of sugars. High edge betweenness values were found
between metabolites involved in glycolysis, the citric acid cycle and
lipid metabolism.
3.4 | Associations between metabolites and
microbial genera
Microbial community composition and faecal metabolite composi-
tion were not significantly associated (Mantel test; ASV: r = .082,
p = .154; genus: r = .072, p = .196; family: r = .050, p = .265; phyla:
r = −.0 45, p = .702). We did not find any significant correlations
between individual microbial ASVs and metabolites, microbial
genera and metabolites, microbial families and metabolites, or mi-
crobial phyla and metabolites after adjusting p- values for multiple
comparisons (all q > .05) (Tables S1– S4). Without FDR correction,
we found one highly connected cluster containing Streptococcus,
Desulfovibrio, an unclassified genus within Synergistaceae,
FIGURE 1 Plot of the first and second
components from the PLS- DA analysis.
Red triangles denote data points from the
DFD season, green pluses denote data
points from the DLD season and blue
crosses denote data points from the WFD
season. Ellipses encircle 95% confidence
intervals
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MALLOTT et al.
TAB LE 2 Pathways that were differentially enriched between each pair of seasons and the associated holm- corrected p- value, and the
ratio of detected metabolites in the pathway to total pathway metabolites
Metabolic pathway WFD vs. DLD DLD vs. DFD DFD vs. WFD
No. of metabolites
detected
Alanine metabolism .005 .195 1.000 5/17
Alpha linolenic acid and linoleic acid
metabolism
1.000 < .001 .187 1/19
Amino sugar metabolism .002 .024 1.000 4/33
Ammonia recycling .005 .123 1.000 6/32
Arachidonic acid metabolism < .001 .005 1.000 1/69
Arginine and proline metabolism < .001 .038 .004 8/53
Aspartate metabolism < .001 .1 51 .002 4/35
Beta oxidation of very long chain fatty acids 1.000 .480 .933 5/17
Beta- alanine metabolism .005 .273 1.000 5/34
Betaine metabolism .059 1.000 .216 1/21
Bile acid biosynthesis .005 1.000 .001 4/65
Biotin metabolism .039 1.000 1.000 1/8
Butyrate metabolism 1.000 .412 1.000 1/19
Cardiolipin biosynthesis .003 1.000 .007 1/11
Carnitine synthesis .134 .893 1.000 3/22
Catecholamine biosynthesis 1.000 1.000 1.000 1/20
Citric acid cycle < .001 1.000 < .001 4/32
Cysteine metabolism < .001 .139 1.000 3/26
d- Arginine and d- ornithine metabolism 1.000 1.000 .814 1/11
De novo triacylglycerol biosynthesis .003 1.000 .007 1/9
Fatty acid biosynthesis .057 1.000 < .001 6/35
Fatty acid elongation in mitochondria .006 1.000 < .001 1/35
Fatty acid metabolism .006 1.000 < .001 1/4 3
Folate metabolism < .001 .005 1.000 1/29
Fructose and mannose degradation .013 .363 1.000 3/32
Galactose metabolism 1.000 .005 .005 8/38
Gluconeogenesis .003 1.000 1.000 5/35
Glucose– alanine cycle .002 .146 1.000 4/13
Glutamate metabolism .002 .129 1.000 8/49
Glutathione metabolism .002 .141 1.000 5/21
Glycerol phosphate shuttle .001 1.000 .008 3/11
Glycerolipid metabolism < .001 .058 < .001 5/25
Glycine and serine metabolism .020 .146 .995 10/59
Glycolysis .006 1.000 1.000 3/25
Histidine metabolism .002 .023 1.000 2/4 3
Homocysteine degradation 1.000 .224 1.000 2/9
Inositol metabolism .005 1.000 < .001 3/33
Inositol phosphate metabolism .005 1.000 .001 2/26
Ketone body metabolism 1.000 .412 1.000 1/13
Lactose degradation 1.000 1.000 1.000 2/9
Lactose synthesis .027 1.000 1.000 2/20
Lysine degradation .001 .027 1.000 3/30
Malate– aspartate shuttle .001 .027 1.000 3/10
Methionine metabolism .1 27 .3 47 .821 6/43
(Continues)
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Metabolic pathway WFD vs. DLD DLD vs. DFD DFD vs. WFD
No. of metabolites
detected
Mitochondrial beta- oxidation of long chain
saturated fatty acids
1.000 1.000 1.000 1/28
Mitochondrial beta- oxidation of medium chain
saturated fatty acids
1.000 1.000 1.000 1/27
Mitochondrial beta- oxidation of short chain
saturated fatty acids
1.000 1.000 1.000 1/27
Mitochondrial electron transport chain < .001 1.000 < .001 3/19
Nicotinate and nicotinamide metabolism .001 .011 1.000 4/37
Nucleotide sugar metabolism 1.000 1.000 1.000 1/20
Oxidation of branched chain fatty acids 1.000 .412 1.000 1/26
Pantothenate and CoA biosynthesis .267 1.000 1.000 1/21
Pentose phosphate pathway < .001 1.000 .624 1/29
Phenylacetate metabolism .752 1.000 1.000 1/9
Phenylalanine and tyrosine metabolism < .001 .180 .018 4/28
Phosphatidylcholine biosynthesis 1.000 .115 1.000 1/14
Phosphatidylethanolamine biosynthesis 1.000 .008 1.000 2/12
Phosphatidylinositol phosphate metabolism .664 1.000 .002 1/17
Phospholipid biosynthesis .026 .444 .019 2/29
Phytanic acid peroxisomal oxidation 1.000 .412 1.000 1/26
Plasmalogen synthesis < .001 1.000 .001 3/26
Porphyrin metabolism 1.000 1.000 1.000 1/40
Propanoate metabolism .005 .123 1.000 7/42
Purine metabolism < .001 .128 .034 10/ 74
Pyrimidine metabolism .059 1.000 .355 5/59
Pyruvaldehyde degradation .063 1.000 1.000 1/10
Pyruvate metabolism .006 1.000 1.000 3/48
Riboflavin metabolism 1.000 1.000 .784 1/20
Selenoamino acid metabolism .010 .153 .207 5/28
Spermidine and spermine biosynthesis 1.000 1.000 1.000 1/18
Sphingolipid metabolism 1.000 .151 1.000 3/40
Starch and sucrose metabolism 1.000 .123 .187 4/31
Steroid biosynthesis < .001 1.000 < .001 4/4 8
Steroidogenesis 1.000 1.000 1.000 1/43
Threonine and 2- oxobutanoate degradation 1.000 1.000 1.000 2/20
Thyroid hormone synthesis 1.000 1.000 1.000 1/13
Transfer of acetyl groups into mitochondria .315 1.000 1.000 3/22
Trehalose degradation 1.000 1.000 1.000 1/11
Tryptophan metabolism .043 .141 1.000 3/60
Tyrosine metabolism < .0 01 .0 61 .003 6/72
Ubiquinone biosynthesis 1.000 1.000 1.000 1/20
Urea cycle < .001 .163 < .001 7/29
Valine, leucine and isoleucine degradation .026 .021 1.000 5/60
Vitamin B6 metabolism .007 1.000 .784 1/20
Warburg ef fect < .001 .227 < .001 8/58
Notes: Cells highlighted in green are pathways with metabolites that are consistently higher in abundance in the first season listed, red cells are
pathways with metabolites that are consistently lower in abundance in the first season listed and cells highlighted in yellow are pathways with no
consistent direction of effect.
TAB LE 2 (Continued)
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MALLOTT et al.
an unclassified genus within Burkholderiales, an unclassified
genus within Erysipelotrichaceae, an unclassified genus within
Alphaproteobacteria, and several metabolites involved in lipid me-
tabolism, fatty acid metabolism and nonessential amino acid bio-
synthesis (Figure 4).
The strength of associations between microbial genera and
metabolites varied seasonally. Edge betweenness differed sig-
nificantly between seasons (Linear model; F = 1147. 2 , p < .001).
The DLD season had significantly higher edge betweenness than
either the DFD season (Tukey; t = 33.0 3, p < .001) or the WFD sea-
son (Tukey; t = 4 6.19, p < .001). The DFD season had significantly
higher edge betweenness than the WFD season (Tukey; t = 17. 35,
p < .001).
3.5 | Interactions between ingested plant
metabolites and faecal metabolites
When examining associations between average seasonal con-
sumption of plant metabolites and seasonal averages of faecal
metabolites for each individual, we found significant positive as-
sociations (all q < .05 and rho > .5) between 64 pairs of ingested
plant metabolites and faecal metabolites. Network analyses indi-
cate that there were two highly connected clusters of plant and
faec al me tabol ites, and several sma ller cl uster s (Figure 5). The two
highly connected clusters primarily contained sugars and fatty
acids from both plant sources and faecal targets. This is probably
linked to fatty acid biosynthesis and related metabolic functions.
Here, we found plant secondary metabolites as faecal targets, in-
cluding a plant diterpenoid (dehydroabietic acid) that significantly
correlated with several sugars and fatty acids, and an organosul-
phur intermediate that also connected closely with sugars on the
lower cluster. We also found 1,2,3- trihydroxybutylbenzene as a
faecal target linked to the plant source 1,3- di- tert- butylbenzene,
also known as pyrogallol. The other highly connected cluster con-
tained plant sugars, long- chain fatty acids, and faecal metabolites
related to the metabolism of essential nutrients. Here, we found
epicatechin as faecal targets, closely connected with fatty acids
and a glycoside as plant sources.
4 | DISCUSSION
In this study, we examined how seasonal variation in macronutri-
ent intake influences the gut metabolome in wild black howler mon-
keys. We found that the gut metabolome varied seasonally in our
study population, similar to previous studies (Amato et al., 2015).
We found partial support for the hypothesis that changes in the gut
microbiome and corresponding changes in the metabolome buffer
seasonal energy and/or nutrient shortfalls. Additionally, we found
strong associations between faecal metabolites and ingested plant
metabolites. However, we did not find significant associations be-
tween the metabolome and microbiome composition.
The faecal metabolome of black howler monkeys is dominated
by lipids, amino acids, carbohydrates and organic acids, similar to
other species of nonhuman primates (Garber et al., 2019; Gomez
et al., 2015, 2016; Ni et al., 2021). Some of the long- chain fatty acids
prominent in the black howler monkey metabolome— palmitic acid,
stearic acid and vaccenic acid— are associated with high- fat diets in
mice (Daniel et al., 2014) and fruit- dominated diets in lowland gorillas
FIGURE 2 Faecal microbial interaction network calculated
from all data. Nodes that were significantly positively correlated
after FDR correction (q < .05 and rho > .5) are shown. Node
colour denotes mean shortest path length (lowest = yellow,
highest = purple) and node border width increases with higher
values of betweenness centrality. Edge colour indicates edge
betweenness values (lowest = purple, highest = blue) and edge
width increases with higher values of rho
unclassified Synergistaceae
unclassified Veillonellaceae
p.75.a5 (Erysipelotrichaceae)
Desulfovibrio
Sutterella
Mogibacterium
unclassified
Mogibacteriaceae
TAB LE 3 Network attributes for metabolites present in >50% of samples within each season
Season
Mean shortest
path length
Mean edge
betweenness
Mean betweenness
centrality
Mean clustering
coefficient
Network
density
Number of
clusters
Mean cluster
size
WFD 1.382 ± 0.493 9.103 ± 7. 7 31 0.077 ± 0.235 0.168 ± 0.290 0.058 46.25 ± 7. 361
DLD 1.950 ± 0.789 18.826 ± 19.185 0.116 ± 0.223 0.167 ± 0.260 0.118 64.667 ± 5.086
DFD 2.839 ± 1.145 62.015 ± 65.232 0.064 ± 0.128 0.284 ± 0.352 0.059 88.375 ± 15.702
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MALLOTT et al .
(Gomez et al., 2016). Palmitic acid and stearic acid are the most
common saturated fatty acids in nature while vaccenic acid is one
of the most common unsaturated fatty acids (Sommerfeld, 1983).
For primates that principally consume plant foods, such as black
howler monkeys, the majority of lipid intake comes from fruits and
their seeds (Norconk et al., 2009). Given that this population of black
howlers has not been observed to consume large amounts of seeds
and commonly voids undigested seeds in their faeces, the fatty
acid profiles of their faecal metabolomes are likely to be strongly
influenced by a yearly diet composed of 57.3% fruits (per cent dry
weight) (Amato & Garber, 2014).
We found significant seasonal differences in faecal metabolome
profiles, both in composition and in pathway enrichment. Several
metabolic pathways were differentially enriched between sea-
sons, with lower concentrations of metabolites present during the
WFD season when compared to both the DLD and DFD seasons.
Add i t io n all y, me t ab o lit e ne tw o rk st ru c tu r e var i ed ac r os s se as o n s. Th e
metabolite network was highly connected but less stable in the DFD
season, when black howler monkey diets are calorically and nutri-
tionally more constrained. In contrast, during the WFD season when
black howler monkeys are consuming more energy- and nutrient- rich
diets, the metabolite network was diffuse. The most stable metab-
olite network occurred during the DLD, when most nutrients were
being consumed at intermediate rates. In addition, edge between-
ness in the microbial– metabolite interaction networks was highest
in the DLD season, indicating that consistent, strong connections
FIGURE 3 Faecal metabolite interaction networks calculated from all data (a), samples from the WFD season (b), DLD season (c) and
DFD season (d). Nodes that were significantly positively correlated after FDR correction (q < .05 and rho > .5) are shown. Node colour
denotes mean shortest path length (lowest = yellow, highest = purple) and node border width increases with higher values of betweenness
centrality. Edge colour indicates edge betweenness values (lowest = red, highest = blue) and edge width increases with higher values of rho
|
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MALLOTT et al.
between individual bacteria and metabolic products may be contrib-
uting to metabolite network stability in this season. These patterns
suggest that metabolic cross- feeding may be more necessary for
the gut microbial community when nutrients from the host diet are
less readily available in the gut. Because cross- feeding can increase
the metabolic efficiency and/or ecological stability of the micro-
bial community (Coyte et al., 2015; Coyte & Rakoff- Nahoum, 2019;
D'Souza et al., 2018; Evans et al., 2020; Goldford et al., 2018; Gudelj
et al., 2016; Liu & Sumpter, 2017; Smith et al., 2019), our data provide
preliminary evidence of improved nutritional buffering by the gut
microbiome during the DLD. During the DFD season, when metab-
olite network connectivity was high but stability was low, metabolic
cross- feeding may be taking place, but the cross- feeding relation-
ships are not as consistant or stable over time.
Microbial responses to variation in the intake of specific nutri-
ents over time also provide evidence of nutritional buffering by the
gut microbiome. For example, given that leaves tend to be high in
fibre and low in host- metabolizable energy (Norconk et al., 2009),
we expected to see enrichment of pathways related to structural
carbohydrate and lipid metabolism during the DLD, as microbes
degrade fibre to produce SCFAs. While the pathway enrichment
analysis did not show an increase in carbohydrate and lipid metabo-
lism, we found that metabolites related to lipid metabolism and SCFA
metabolism became more important in metabolite networks during
the DLD season. In addition, the metabolism of essential vitamins
(B3) and amino acids were enriched in the DLD season, potentially
compensating for a diet poor in specific nutrients and aiding in the
digestion of protein- rich leaves. Similarly, although fruits have more
host- metabolizable energy than leaves, they are lower in protein
compared to leaves, and overall caloric intake during the DFD sea-
son was reduced by 37.4%– 40.0% compared with the WFD season
and 0%– 33.6% compared with the DFD season (Table 1). Therefore,
we expected microbial buffering to result in an enrichment of amino
acid synthesis pathways and lipid metabolism pathways. We did see
an enrichment of amino acids pathways during the DFD and lipid
metabolites having a more central role in the metabolite network
(Figure 3). In contrast, we observed a de- enrichment of many path-
ways during the WFD season when the black howler monkey diet
was least nutritionally constrained. These results confirm earlier
work in this population (Amato et al., 2 014, 2015), as well as other
FIGURE 4 Faecal metabolite– microbe
interaction network calculated from all
data points. Nodes that were significantly
positively correlated (p < .05 and rho > .5)
are shown. Node colour denotes mean
shortest path length (lowest = yellow,
highest = purple) and node border
width increases with higher values of
betweenness centrality. Edge colour
indicates edge betweenness values
(lowest = red, highest = blue) and edge
width increases with higher values of rho
Arachidyl alcohol
L-Phenylalanine
1-Tritriacontanol
unclassified
Ruminococcaceae
toluquinol
Sapiol
unclassified
Erysipelotrichaceae
unclassified
Coriobacteriaceae
Benzoic acid
L-Valine
11-Eicosenoic acid
C18-2-1
unclassified Bacteria
unclassified
Alphaproteobacteria
RFN20 (Erysipelotrichaceae)
unclassified Clostridiales
Cellobiose
Glyoxylic acid
Streptococcus
9,19-Cyclolanostan-3-ol-24-methylene-(acetate-)-3b
1,3-Di-tert-butylbenzene
N-Carboxyglycine
L-Alanine
Inositol phosphate
unclassified
Coriobacteriaceae
unclassified
Desulfovibrionaceae
unclassified Burkholderiales
unclassified Lachnospiraceae
Clostridium
2-Keto-L-gluconate
2,4,6-Tri-tert-butylbenzenethiolunclassified
Mogibacteriaceae
Pyruvic acid
Prevotella
Desulfovibrio
Docosanol
Blautia
unclassified
Dethiosulfovibrionaceae
L-Aspartic acid
1-Octacosanol
Mogibacterium
Prevotella
myo-Inositol
L-lactic acid
Sucrose
Palmitoleic acid
beta-Alanine
unclassified Synergistaceae
Capric acid
Glycerol-3 phosphate
2-Hydroxyphenylpropionic
acid
Lachnospira
L-lysine
Dodecanoic acid
Oscillospira
Dehydroabietic acid
Gluconic acid
Coprobacillus
Cyclohexanecarboxylic acid
gamma-Aminobutyric acid
unclassified Prevotellaceae
Melissic acid A
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MALLOTT et al .
studies in mammals (Gomez et al., 2015; Koren et al., 2012; Mallott &
Amato, 2018; Springer et al., 2017; Sun et al., 2016; Wu et al., 2017),
that indicate the gut microbiome acts as a potential buffer limiting
energy and nutrient shortfalls due to seasonal changes in diet or
changes in host nutrient requirements.
The gut microbiome also provides nutritional benefits to hosts
by processing plant secondary metabolites that otherwise act as tox-
ins or digestive inhibitors. This relationship has been documented in
desert woodrats (Dearing & Kohl, 2017; Kohl et al., 2014 , 2016) and
may be important to highly folivorous primates such as black howler
monkeys, whose yearly diet contains 33.1% young and mature leaves
(% dry weight) (Amato & Garber, 2014). Interestingly, our plant- faecal
metabolite interaction network from all seasons combined showed
faecal target metabolites associated with essential nutrients, fatty
acid metabolism, and the metabolism of chemical defensive com-
pounds clustered with two groups of plant source metabolites: sim-
ple carbohydrates and fatty acids. Specifically, the plant secondary
metabolites included dehydroabietic acid in the upper largest clus-
ter, a diterpenoid for chemical defence commonly found in tree resin
(Helfenstein et al., 2017), and epicatechin, a flavan- 3- ol and major
component of condensed tannin (Ferreira et al., 1999; Khanbabaee
& Ree, 2001), in the right smaller cluster. This suggests that indi-
viduals ingested toxic plant secondary metabolites while consuming
sugar and fatty acid metabolites used for fatty acid/glucose- related
biosyntheses. This relationship could indicate a potential trade- off in
that foods they consume with the highest protein and caloric value
also contain high amounts of tannins and other difficult to digest
plant secondary metabolites. While some mammals might avoid food
items high in particular plant secondary metabolites, evidence sug-
gests that other mammals readily consume foods high in secondary
metabolites if those foods are also high in energy, protein or water
(Felton et al., 2009; Lambert & Rothman, 2015; Remis et al., 2001;
Simpson & Raubenheimer, 2001; Villalba & Provenza, 2005). The
presence of tannin- or toxin- degrading bacteria in the gut microbi-
ome could facilitate this behaviour by allowing animals to tolerate
higher concentrations of plant secondary metabolites in their diet.
We also identified an aromatic thiol (organosulphur compound), po-
sitioned in the larger cluster of sugar/fatty acid metabolites along
with dehydroabietic acid, that is either derived from plant leaves
(Gonulalan et al., 2019), from soil (Shen et al., 2020), or is an inter-
mediate byproduct of another aromatic hydrocarbon (such as butyl-
benzene) that was degraded by sulphur- reducing gut bacteria (such
as Desulfovibrio, which degrades hydrocarbons with a sulphate redox
reaction) (Lyles et al., 2014; Widdel et al., 2006, 2010) after uptake in
the diet. Either way, the presence of this aromatic thiol might be an-
other indicator of how the black howler monkey gut microbiome fa-
cilitates a higher tolerance for increased amounts of plant secondary
metabolites during dietary shifts. Mechanistic experiments testing
the capacity of the howler monkey gut microbiome to degrade these
potential plant toxins will be necessary to verify this relationship.
Although our data suggest that the gut microbiome buffers wild
howler monkey hosts against nutritional challenges, they also indi-
cate that these microbial services are likely to have limits. Our me-
tabolite networks indicated increased cross- feeding and community
stability in response to nutritional constraints during the DLD com-
pared to the WFD. However, during the DFD season, as black howler
monkey diets became even more calorically and nutritionally con-
strained, the stability of the metabolite networks began to decline.
This trend suggests there may be a threshold past which specific
dietary changes alter the underlying structure of the microbiome in a
way that compromises the nutritional services the microbiome pro-
vides to the host. For example, reductions in the intake of specific
FIGURE 5 Plant metabolite- faecal
metabolite interaction network calculated
from all data points across all seasons.
Nodes that were significantly positively
correlated (p < .05 and rho > .5) are
shown. Node colour denotes mean
shortest path length (lowest = yellow,
highest = purple) and node border
width increases with higher values of
betweenness centrality. Edge colour
indicates edge betweenness values
(lowest = red, highest = blue) and edge
width increases with higher values of rho.
Direction of arrow is from source (plant
metabolite) to target (faecal metabolite)
Threonic acid
GalactosideMyristic acid
1,6-Anhydroglucose
Inositol phosphate
Lactic acid
L-tyrosine-1
Nicotinic acid
1,2,3-trihydroxybenzene
Squalene
9,19-Cyclolanostan-3-ol-24-methylene-(acetate-)-3b
4-Hydroxy-3-methoxyphenethyleneglycol
Sorbose
2,4,6-Tri-tert-butylbenzenethiol
2-Keto-gluconic acid
3,4,5-Trihydroxypentanoic
acid
2-O-Glycerol-beta-D-galactopyranoside
4-Methoxycinnamic acid
Gluconic acid
1,3-Di-tert-butylbenzene
Ursolic acid
Trehalose
Galactofuranoside
Arabinofuranoside
3-Deoxy-arabino-hexaric
acid
L-Arabinose
Hentriacontanol
2-Methyl-benzoic acid
1,2,3-Trihydroxybutane
Glucuronic acid
D-Glutamic acid
Butylamine
Glycerol
Threitol
Dehydroabietic acid
Pyrophosphate
Gluconic acid-1,5-lactone
Palmitoleic acid
Glycerol-3-phosphate
Epicatechin
Glucaric acid
Malonic acid
Palmitic acid
Hexacosanoic acid
Mannitol
Xylonic acid-1,5-lactone
5-Hydroxypipecolic acid
C16-1
|
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MALLOTT et al.
macronutrients consumed in large amounts such as fibre, or reduc-
tions that persist for extended time periods could lead to the loss of
key microbial taxa (Sonnenburg et al., 2016). Studies of black howler
monkeys in anthropogenically altered environments report reduced
microbial diversity as well as reduced relative abundances of SCFA-
producing microbial taxa (Amato et al., 2013). These losses in micro-
bial taxa are correlated with losses of plant species, and presumably
particular macronutrients, from the howler diet, and are likely to in-
hibit nutritional buffering by the microbiome. Identifying the dietary
thresholds past which the gut microbiome loses its ability to buffer
hosts from nutritional shortfalls in black howler monkeys as well as
other wild animals will provide important insight into host ecology
and conservation as well as microbial community dynamics.
Data describing howler monkey physiology are critical for veri-
fying the magnitude of impact of potentially beneficial microbiome
functions on hosts in variable environments. While some changes
in microbial metabolism could benefit the host by increasing the
availability and subsequent absorption of nutrients or key vitamins
lacking in the diet, they might also be harmful to the host if scarce
nutrients are diverted into networks of gut microbial metabolism.
Alternatively, the impacts of changes in microbial metabolism on
the host might be negligible. These potential outcomes probably
occur along a gradient and are context- dependent. Evaluating host
nutrient and energy balances using noninvasive markers as well as
performing controlled laboratory experiments to assess microbial
metabolism and interactions with host dietar y substrates in real time
will provide crucial insight.
Regardless of the extent to which the obser ved shifts in mi-
crobial metabolism buffer host nutrition, our data suggest they
are strongly driven by host diet. Although previous research has
shown that nutrient limitations in the large intestine resulting from
dietary changes influence microbial community composition (Reese
et al., 2018), wild animals systems such as this one make it difficult
to test the extent to which host- driven changes in the intestinal
environment result in preferential recruitment of specific microbial
taxa or genes. However, it is widely accepted that changes in host
diet alter the nutritional environment in the gut such that microbes
will differentially regulate their metabolic pathways (Fontaine &
Kohl, 2020). This alters both competitive and mutualistic interac-
tions between microbes, as suggested by the shifting networks of
microbial taxa and metabolites that we observed across seasons.
Furthermore, one of the strongest relationships we detected was
between the faecal metabolome and the metabolite content of the
plants consumed by black howler monkeys. For example, during the
DLD season, we saw an increase in the centrality and importance
of tannins and other plant secondary metabolites in the faecal me-
tabolite networks that is probably related to the higher concentra-
tions of these compounds in leaves compared with fruit. During the
DLD, the consumption of mature leaves increased to 64% (per cent
dry weight) compared with 37% during the WFD season and 43%
during the DFD season (Amato et al., 2015; Amato & Garber, 2014).
While mature leaves probably contribute more tannins to howler
monkey diets compared with young leaves, rates of mature leaf
consumption were found to be relatively low and constant in this
population (1%– 8% dry weight) (Amato & Garber, 2014). We also
fo und a high n umb er of sig nif icant dire ct associa tion s between plant
and faecal metabolites. Previous studies demonstrated similarly
strong relationships between primate dietary intake and gut mi-
crobiome composition and function using DNA- based approaches
(Amato et al., 2015; Mallott et al., 2018; Orkin et al., 2019). Fewer
have identified these relationships with faecal metabolites (Garber
et al., 2019; Gomez et al., 2015, 2016).
In conclusion, we found strong relationships both between sea-
sonal changes in diet and gut microbiome function and between the
consumption of specific plant metabolites and faecal metabolite
profiles. These patterns suggest that the gut microbiome might be
buffering howler monkeys against seasonal variations in nutrient
intake. However, we also identified evidence of a potential thresh-
old in dietary intake past which the ability of the gut microbiome
to buffer howler monkeys could be diminished. Moving forward,
combining detailed studies of nutrient consumption and data on gut
microbial community composition and function with biomarkers of
host energy status and physiology will help to clarify the magnitude
of these potential benefits and limitations and more precisely iden-
tify relevant mechanisms of interaction between both hosts and mi-
crobes as well as among different microbes.
AUTHOR CONTRIBUTIONS
E.K.M. analysed data and wrote the paper. L.J.S. analysed data and
edited the paper. P.A.G. provided funding for metabolomics analysis
and edited the paper. K.R.A. designed the study, provided funding
for field data collection, performed data collection and edited the
paper.
ACKNOWLEDGEMENTS
This study was funded by a National Geographic Waitt grant (W139-
10) and a University of Illinois Dissertation Travel Grant to K.R.A.,
and a University of Illinois Research Board Grant to P.A.G. K.R.A.
was supported by a National Science Foundation Graduate Research
Fellowship and is currently a CIFAR fellow. E.K.M. is currently sup-
ported by the Vanderbilt Microbiome Innovation Center. Thanks to
Brianna Wilkinson, Sarie Van Belle and Alejandro Estrada for field
support. We also thank CONANP, SEMARNAT and SAGARPA in
Mexico and the CDC in the USA for permits and logistical support.
We would like to thank Alex Ulanov for this help with metabolite
analyses through the Metabolomics Center at the Roy J. Carver
Biotechnology Center, University of Illinois at Urbana- Champaign.
P.A.G. wishes to thank Chrissie, Sara, Jenni and Dax for their support
and encouragement. This research was supported in part through
the computational resources and staff contributions provided by the
Genomics Compute Cluster, part of the Quest high- performance
computing facility at Northwestern University, which is jointly sup-
ported by the Feinberg School of Medicine, the Center for Genetic
Medicine, Feinberg's Department of Biochemistry and Molecular
Genetics, the Office of the Provost, the Office for Research and
Northwestern Information Technology.
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MALLOTT et al .
CONFLICT OF INTEREST
The authors have not conflict of interest to declare.
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly
available the digitally-shareable data necessary to reproduce the
reported results. The data is available at https://doi.org/10.6084/
m9.figsh are.14995 407.v1
DATA AVAIL AB ILI T Y STAT EME N T
Sequencing data can be found in the Sequence Read Archive (ncbi.
nlm.nih.gov/sra) under BioProject PRJNA745184. Fecal metabolite
data, plant metabolite data, and metadata associated with metabo-
lite and 16S rRN A gene se que ncing dataset s can be found in Figsh are
(https://figsh are.com/proje cts/Black_howler_monkey_metab olite
s/118236, DOIs listed below). Code for all analyses can be found
on GitHub (https://github.com/Krama to- lab/howler_metab olites).
Mallott, Elizabeth; Amato, Katherine (2021): Metadata for 16S rRNA
gene sequences. figshare. Dataset. https://doi.org/10.6084/m9.figsh
are.14995 449.v1 Mallott, Elizabeth; Amato, Katherine (2021): Plant
metabolites consumed by season. figshare. Dataset. ht t p s : //do i .
org/10.6084/m9.figsh are.14995 440.v1 Mallott, Elizabeth; Amato,
Katherine (2021): Metadata for fecal metabolite samples. figshare.
Dataset. https://doi.org/10.6084/m9.figsh are.14995 437.v1 Mallott,
Elizabeth; Amato, Katherine (2021): raw_fecal_metabolite_data.xlsx.
figshare. Dataset. https://doi.org/10.6084/m9.figsh are.14995 407.v1
ORCID
Elizabeth K. Mallott https://orcid.org/0000-0001-5446-8563
Katherine R . Amato https://orcid.org/0000-0003-2722-9414
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SUPPORTING INFORMATION
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Supporting Information section at the end of this article.
How to cite this article: Mallott, E. K., Skovmand, L. H., Garber,
P. A., & Amato, K. R. (2022). The faecal metabolome of black
howler monkeys (Alouatta pigra) varies in response to seasonal
dietary changes. Molecular Ecology, 31, 4146–4161. ht tps://d o i .
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