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\begin{document}

\title{A gene catalogue of Rhinopithecus gut microbiome provides new insights
into dietary adaptation of foregut fermenting animals}



\author[1]{Xiaochen Wang}%
\author[2]{Jiali Zhang}%
\author[3]{Huijuan Pan}%
\author[2]{Yixin Chen}%
\author[2]{Dayong Li}%
\author[4]{Zuofu Xiang}%
\author[2]{Shuxin Mao}%
\author[1]{Ying Shen}%
\author[2]{Mingyi Zhang}%
\author[5]{Ming Li}%
\affil[1]{Institute of Zoology Chinese Academy of Sciences}%
\affil[2]{Affiliation not available}%
\affil[3]{Beijing Forestry University}%
\affil[4]{Central South University of Forestry \& Technology}%
\affil[5]{Institute of Zoology, CAS}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Gut microbiome gene catalogues have advanced the knowledge of
host-microbiome interactions in many mammals. Snub-nosed monkeys
(Rhinopithecus spp) are folivores with foregut fermentation which is
similar to ruminants, but their gut microbiota lacks a comprehensive
description. In this study, we constructed a comprehensive gene
catalogue by performing metagenomic analysis on 143 wild snub-nosed
monkeys and compare it to that of ruminants and monogastric animals. Our
results demonstrate the classification and functional characteristics of
the gut microbiome of snub-nosed monkeys and identified a set of core
genera in these mammals. Moreover, we found that the gut of snub-nosed
monkeys and other herbivores was enriched with more bacteria and enzymes
related to the degradation of structural carbohydrates, indicating the
importance of gut microbiota for dietary adaptation. Our study expands
resources for gut microbiome studies of nonhuman primates and provide
new insights into the evolutionary route of foregut fermenting animals
during dietary adaptation.%
\end{abstract}%



\sloppy


A gene catalogue of \emph{Rhinopithecus} gut microbiome provides new
insights into dietary adaptation of foregut fermenting animals

Xiaochen Wang1\#, Jiali Zhang1,2\#, Huijuan Pan3\#, Yixin Chen4, Dayong
Li5, Zuofu Xiang4, Shuxin Mao1,2, Ying Shen1, Mingyi Zhang3, Ming Li1,6*

\textsuperscript{1} CAS Key Laboratory of Animal Ecology and
Conservation Biology, Institute of Zoology, Chinese Academy of Sciences,
Beijing 100101, China\textsuperscript{2} College of Life Sciences,
University of Chinese Academy of Sciences, Beijing, 100049,
China\textsuperscript{3} School of Ecology and Nature Conservation,
Beijing Forestry University, Beijing, 100083, China\textsuperscript{4}
College of Life Sciences and Technology, Central South University of
Forestry and Technology, Changsha 410004, China\textsuperscript{5} Key
Laboratory of Southwest China Wildlife Resources Conservation
{[}Ministry of Education{]} China West Normal University, Nanchong
637009, China\textsuperscript{6} Center for Excellence in Animal
Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223,
China

\emph{\textbf{\#} Contributed equally to this work}

\emph{*Corresponding author} :

Dr. Ming Li, Key Laboratory of Animal Ecology and Conservation Biology,
Institute of Zoology, Chinese Academy of Sciences, 1--5 Beichenxi Road,
Chaoyang District, Beijing 100101, China. Email: lim@ioz.ac.cn; Tel:
86-10-64807067, Fax: 0086 10 64807099. ORCiD ID
(http://orcid.org/0000-0001-5689-6270)

\emph{Running Title}\textbf{:} Gene catalogue of snub-monkey gut
microbiome



\textbf{Abstract}

Gut microbiome gene catalogues have advanced the knowledge of
host-microbiome interactions in many mammals. Snub-nosed monkeys
(\emph{Rhinopithecus} spp) are folivores with foregut fermentation which
is similar to ruminants, but their gut microbiota lacks a comprehensive
description. In this study, we constructed a comprehensive gene
catalogue by performing metagenomic analysis on 143 wild snub-nosed
monkeys and compare it to that of ruminants and monogastric animals. Our
results demonstrate the classification and functional characteristics of
the gut microbiome of snub-nosed monkeys and identified a set of core
genera in these mammals. Moreover, we found that the gut of snub-nosed
monkeys and other herbivores was enriched with more bacteria and enzymes
related to the degradation of structural carbohydrates, indicating the
importance of gut microbiota for dietary adaptation. Our study expands
resources for gut microbiome studies of nonhuman primates and provide
new insights into the evolutionary route of foregut fermenting animals
during dietary adaptation.

\textbf{Keywords:} Snub-nosed monkeys, gut microbiome, gene catalogue of
gut microbiome, dietary adaptation, gastrointestinal tract



\textbf{1 \textbar{} INTRODUCTION}

Accumulating evidence indicates that the gut microbiome plays an
essential role in modulating the host's whole-body metabolism (Qin et
al., 2012; Turnbaugh et al., 2006), immune system (Sylwia Smolinska,
2016; X. Zhang, Chen, Zhao, \& Li, 2020) and adaptation to specific
diets and extreme environments (Baniel et al., 2021; H. Li et al., 2018;
X. Li et al., 2022; Z. Zhang et al., 2016). Therefore, a deeper
understanding of host-microbiome interactions is important for
understanding host adaptive evolution and health. In recent years, a
reference catalogue of gut microbiome genes has been constructed to
reduce data redundancy in large-scale gut metagenomic studies to improve
the estimation of the diversity and genetic potential of gut microbiome
and serve as a basis for metagenome-wide association studies (Xiao et
al., 2016; Xiao et al., 2015; Xie et al., 2021). The catalogue of
reference genes is a powerful tool to help us gain a comprehensive
understanding of the gut microbiome and is also essential for in-depth
functional metagenomic analyses such as species/gene profiling,
microbial biomarker discovery, and functional annotation (J. Li et al.,
2020; Thingholm et al., 2019). To date, many catalogues of reference
genes for the gut microbiome have been established in a variety of
mammals, including humans (J. Li et al., 2014; Qin et al., 2010),
rodents (Degnan et al., 2012) and ruminants (J. Li et al., 2020; Xie et
al., 2021). This has greatly promoted knowledge of the interactions
between host physiology and their gut microbiome, and is also of great
significance for understanding host dietary adaptation and health status
monitoring (R. E. Ley et al., 2008; Thingholm et al., 2019).

Nonhuman primates (NHPs) are our closest living relatives and among the
most widespread and successful mammals that underwent adaptive
radiation. Therefore, studies of the NHP gut microbiome are fundamental
in investigations of the ecological and evolutionary adaptation
mechanisms of NHPs and even mammals (Amato et al., 2016; Clayton et al.,
2018; Goldberg, 2008). Mimicking human dietary patterns in NHP animal
models has improved our understanding of the complex interplay between
diet, obesity, and the gut microbiome in humans (Newman et al., 2021).
Therefore, studies of the NHP gut microbiome may provide important clues
for understanding the characteristics of these bacterial communities in
the human gut and provide useful guidance and reference for studying the
interaction between humans and their gut microbiome (Jianan Sang 2022).
In addition, the impact of gut microbiome homeostasis on the
conservation of endangered NHPs is of particular concern (Basabose,
2002). Except phylogenetic relationships, the NHP gut microbiome is
influenced by multiple other factors, such as diet (Baniel et al.,
2021), physiology (Bennett et al., 2016), habitat quality (Barelli et
al., 2015), and social interactions (Bennett et al., 2016). For example,
the gut microbial structure of geladas (\emph{Theropithecus gelada} )
adapted to seasonal dietary changes through rapidly change (Baniel et
al., 2021). The richness and diversity of gut microbes in the Udzungwa
red colobus (\emph{Procolobus gordonorum} ) were reduced due to dietary
changes caused by human disturbance and habitat degradation (Barelli et
al., 2015). Therefore, it is very important to study the gut microbiome
of NHPs to understand the mechanisms of dietary adaptation and to
develop better conservation strategies for these endangered species.
However, considering that NHPs occupy a variety of habitats and niches,
most previous studies considered a single species or a small number of
captive individuals, which is far from being able to completely and
accurately represent the general characteristics of intestinal
microbiome in NHPs (Degnan et al., 2012; X. Li et al., 2018). The key to
detailed analysis of the gut microbiome in NHPs is the availability of
comprehensive gene catalogues of wild individuals (Zhu et al., 2020).
Therefore, the establishment of a comprehensive reference gene catalogue
of the gut microbiome in wild NHPs is of practical significance for
further developing the potential resource pool of the gut microbiome in
wild NHPs and exploring the relationships of the gut microbiome with
ecological adaptation and conservation in NHPs.

Among NHPs, snub-nosed monkeys (\emph{Rhinopithecus} spp) (SNMs) are a
group of endangered colobines including five living species of Sichuan
SNM (\emph{R. roxellana} ), Yunnan SNM (\emph{R. bieti} ), Guizhou SNM
(\emph{R. brelichi} ), Tonkin SNM (\emph{R. avunculus} ) and Myanmar SNM
(\emph{R. strykeri} ), of which Yunnan SNMs, endemic to the Tibetan
Plateau, are the NHPs with the highest altitudinal distribution (Quan
Gouqiang, 2002). Additionally, the living conditions of SNMs are more
severe than those of other primates, with features such as low
temperatures and limited oxygen (Li BG, 2002; Long YC, 1994), indicating
that SNMs should be suitable for studying the mechanisms adaptation to
extreme environments. SNMs have sacculated stomachs and are folivores
with foregut fermentation. In recent years, the genomic study of SNMs
revealed its genetic mechanism underlying dietary adaptations and found
evidence for functional evolution in the colobine RNASE1 gene that
enables digestion of the high concentrations of bacterial RNA derived
from symbiotic microflora (Zhou et al., 2014). Moreover, the digestion
of cellulose by the gut microbiome is also an important part of the
dietary adaptation of SNMs (Wang et al., 2021). Studies have shown that
the gut microbial structure of SNMs is similar to that of ruminants, but
the fermentation efficiency is lower than ruminants (D. J. Chivers,
Hladik, C.M, 1980). The sacculated stomachs of SNMs do not exhibit the
same strong functional division as the rumens of ruminants (D. Chivers,
1994). It is still not fully understood how the gut microbiome and
gastrointestinal tract (GIT) help SNMs extract sufficient energy and
nutrients from a high-fibre diet (Ganzhorn, 1992; Wang et al., 2021).
Previous studies of single SNM species have limited the description of
the overall genetic diversity of their gut microbiome (V. L. Hale et
al., 2018; V. L. Hale, Tan, C.L., Niu, K., Yang, Y., Zhang, Q., Knight,
R., 2019; Yao et al., 2021). Therefore, a comprehensive exploration of
the gut microbiome in a large number of wild SNMs is needed to further
elucidate their dietary adaptation mechanism, and the establishment of a
reference gene catalogue of the gut microbiome in wild SNMs is a
reasonable next step. Such a catalogue will provide an important
database and framework for analysing the relationships of the gut
microbiome with dietary adaptation and for making conservation plans. It
will also provide a data basis and reliable criteria for studies of the
gut microbiome as well as health, immunity, and adaptation to extreme
environments in SNMs.

Here, we constructed an integrated reference gene catalogue of
\emph{Rhinopithecus} gut microbiome (RGC) by processing 143 fecal
individual samples from wild populations of SNMs (Yunnan SNM, Sichuan
SNM, and Myanmar SNM). The aim of this study was to comprehensively
characterize the gut microbiome of SNMs and lay a solid foundation for
revealing the relationship between the gut microbiome and host
evolutionary adaptation and health. We envisage that the present gut
bacterial gene catalogue and functional characterization will serve as a
valuable reference and resource for studying the evolution of the gut
microbiome in foregut fermenters, also for the study of mammalian
evolution and functional adaptation models provides a wide range of
perspectives.

\textbf{2 \textbar{} METHODS}

\textbf{2.1 \textbar{} Sample collection and transportation}

A total of 143 fecal individual samples with different ages, sexes and
altitudes of distribution were collected from different wild populations
of Sichuan SNM (81 samples), Yunnan SNM (47 samples) and Myanmar SNM (15
samples) (Table S1). All fresh fecal matter were placed in 15ml of
RNAlater immediately after defecation and transported to Institute of
Zoology, Chinese Academy of Sciences (CAS) with dry ice, then stored at
-80 until DNA extraction.

\textbf{2.2 \textbar{} DNA extraction and sequencing}

Fecal DNA extraction was performed using the QIAamp DNA Stool Mini Kit
(Qiagen, Valencia, CA, USA) according to the standard protocol. The
quality and concentration of the DNA were determined using a Nanodrop
(ND-1000) spectrophotometer (Nanodrop Technologies, Wilmington, DE,
USA), and the integrity was confirmed by agarose gel electrophoresis.
DNA samples were stored at -20\selectlanguage{ngerman}°C until use. Shotgun sequencing was
performed using an Illumina NovaSeq 6000, with at least 10 Gb per
sample.

\textbf{2.3 \textbar{} Construction of the gene catalogue}

Shotgun sequencing reads for fecal samples from \emph{R.roxellana} ,
\emph{R. bieti} and \emph{R. strykeri} individuals were independently
processed. We filtered the raw data using Trimmomatic (v0.36) (Bolger,
Lohse, \& Usadel, 2014) to exclude adapter sequences and low-quality
reads. Then we used the genomes of \emph{R. strykeri}(assembly
ASM2376470v1), \emph{R. bieti} (assembly ASM169854v2),\emph{R.
roxellana} (assembly ASM756505v1) and human (assembly GRCh38.p13) to
remove contaminants and obtain clean data with Bowtie2 (v2.3.5)
(Langmead \& Salzberg, 2012). The remaining reads were considered
high-quality reads. In total, we obtained 1,448 GB of high-quality reads
with an average of 10.13 GB per sample. To construct a comprehensive
catalogue of reference genes in the SNM gut microbiome, we individually
assembled the high-quality reads from each sample into longer contigs
with MEGAHIT (v1.2.6) (Dinghua Li 2015). We obtained 62,306,924 contigs
longer than 300 bp. Next, MetaGeneMaker (Wenhan Zhu, 2010) was used to
predict open reading frames (ORFs), and we obtained 111,177,047 ORFs
from 143 samples with an average of 777,462 ORFs per sample, which were
longer than 102 bp, (Table S2). Three non-redundant gene-sets of the
\emph{R. roxellana} , \emph{R. bieti} , and \emph{R. strykeri} gut
microbiome were independently clustered using CD-HIT (Fu Limin,
2012)\emph{.} We further merged these three non-redundant gene-sets into
an integrated catalogue of reference genes in the SNMs by CD-HIT (Fu
Limin, 2012), referred to RGC (Figure 1).

\textbf{2.4 \textbar{} Taxonomic and functional annotation}

The genes of the RGC were translated into amino acid sequences using
NCBI Genetic Codes 11 (Kozak, 1983). Taxonomic annotation of amino acid
sequences was performed by Kaiju v1.8.0 (Menzel, Ng, \& Krogh, 2016) and
the NCBI NR database (released on Feb 24\textsuperscript{th}, 2021),
providing a detailed overview of the taxonomical composition of SNMs gut
microbiome with the parameters `-a greedy -e 5 -E 0.01 -v -z 5'.
Functional annotation was performed using the Kyoto Encyclopedia of
Genes and Genomes databases (KEGG, Release 100.0, genes from animals or
plants were excluded) and Evolutionary genealogy of genes:
Non-supervised Orthologous database (eggNOG, v5.0) on the basis of
Diamond (v0.9.24) with the parameters `--evalue 1e-5 -k 1' (Benjamin
Buchfink, 2015). KEGG and eggNOG annotations were performed using an
in-house pipeline, where each protein was assigned to a KEGG orthologous
group (KO) or eggNOG orthologous group (OG) when the highest-scoring
annotated hits contained at least one alignment with over 60 hits (Qin
et al., 2012). Carbohydrate enzyme annotation was carried out through
the Carbohydrate-Active enZYmes Database (CAZymes). We mapped protein
sequences to entries in the hidden Markov model (HMM) libraries of
CAZyme families downloaded from the CAZy database (CAZyDB.07312019) with
the hmmscan program in HMMER (3.1b2) (Pattabiraman \& Warnow, 2021).

\textbf{2.5 \textbar{} Estimation of the relative abundances of genes,
taxa and function terms}

To calculate the gene relative abundance, high-quality reads from each
sample were aligned against the RGC by Bowtie2 (v2.3.5) (Langmead \&
Salzberg, 2012). Sequenced-based abundance profiling was determined as
described in previous studies (Qin et al., 2012). The relative
abundances of taxa, KOs, OGs and CAZymes were calculated by summing the
abundance of the respective genes belonging to each category per sample,
based on the taxonomic assignments and KO, OG and CAZymes annotations,
respectively (J. Li et al., 2014).

Published gene relative abundance profiles and gene catalogues of 11
species were downloaded and analysed by the same taxonomic and
functional annotation pipeline in our study. These catalogues of 11
species included human (\emph{Homo sapiens} ) gut microbiome (ICG) (J.
Li et al., 2014), pig (\emph{Sus scrofa} ) gut microbiome (Xiao et al.,
2016), mouse (\emph{Mus musculus} ) gut microbiome (Xiao et al., 2015),
cynomolgus macaque (\emph{Macaca fascicularis} ) gut microbiome (X. Li
et al., 2018), and gut microbiome of various ruminants (\emph{Bos
taurus} ,\emph{B. grunniens} , \emph{Bubalus bubalis} , \emph{Capra
aegagrus} ,\emph{Ovis aries} , \emph{Capreolus pygargus} and
\emph{Hydropotes inermis} ) (Xie et al., 2021) (Table S4).

\textbf{2.6 \textbar{} Microbial composition analysis}

Alpha diversity of the gut microbiome was calculated using the Shannon
index based on the genus and KO profiles by ``vegan'' packsge in R
(Dixon, 2003). The overall differences in the bacterial community
structures were evaluated by principal coordinate analysis (PCoA) and
hierarchical tree analysis based on the Bray-Curtis dissimilarity
matrix, which were performed with ``vegan'' package in R (Dixon, 2003).
To show the special characteristics of the gut microbiome of SNM, the
taxonomic (phylum and genus) and function (COGs, KOs and CAZymes) terms
present in 90\% of individuals in each gene catalog were used for
comparison and visualization with UpSet Venn (Xie et al., 2021). Then,
we calculated the differences among 12 species by the Wilcoxon rank-sum
test or Kruskal--Wallis test, and the p values were adjusted using the
Benjamini--Hochberg false discovery rate (FDR) (Yoav Benjamini, 1995).

\textbf{3 \textbar{} RESULTS}

\textbf{3.1 \textbar{} Construction of a reference gene catalogue of
the} \textbf{SNM} s \textbf{gut microbiome}

We generated a gene catalogue of the gut microbiome in SNMs, comprising
18,169,322 non-redundant genes with an average length of 573 bp and
32.39\% of these genes were identified as complete (Figure 1; Table S3).
Of these non-redundant genes, only 9,829,597 (54.1\%) genes were
taxonomically classified. Of these, more than 99.35\% could be assigned
to bacteria, and the remaining 0.65\% being genes from viruses (0.24\%)
and archaea (0.41\%) (Table S5; Figure 2a). At the phylum level, most of
the annotated genes belonged to Firmicutes (41.99\%), followed by
Proteobacteria (3.43\%), Bacteroidetes (3.28\%) and Actinobacteria
(1.17\%). Moreover, 6,037,022 (33.2\%) and 6,809,541 (37.5\%) of the
bacterial genes in the RGC could be annotated to the genus and species
levels, respectively. At the genus level, most of the annotated genes
(5.25\%) belonged to \emph{Clostridium} , followed by
\emph{Ruminococcus}(2.11\%), \emph{Roseburia} (1.38\%),
\emph{Eubacterium} (1.17\%) and\emph{Oscillibacter} (1.05\%) (Figure S1;
Table S5).

Functional annotation of the gut bacterial genes contained in RGC
revealed a total of 8,061 KOs and 44,671 OGs, which represented 33.54\%
(6,049,083) and 56.30\% (10,221,423) of the RGC, respectively (Table
S6). These results illustrated that the gut microbiome of SNMs is a
highly complex taxonomic assemblage with many unknown genes. Pathways
associated with metabolism linked to the degradation of carbohydrate,
amino acid, energy, nucleotide, cofactors and vitamins were enriched
(Figure S2; Table S9). Further, genetic information processing pathways
(replication and repair, and translation), and environmental information
processing pathways (membrane transport and signal transduction) were
also enriched (Figure S2; Table S9). Additionally, we mapped the RGC to
the CAZy database. The results provided us with a complete picture of
the carbohydrate enzymes presented in SNMs gut microbiome. We obtained
371 enzyme families from 390,733 annotated genes. These genes accounted
for \textasciitilde{}2.15\% of the total bacterial genes in the RGC.
There is a wide diversity of glycoside hydrolases (GH) catalytic modules
in the RGC, including 140 GH families. This indicated that the SNMs have
a strong ability to digest structural carbohydrates, which is related to
their dietary adaptation. GHs are one of the major enzymes in the gut
microbiome and hydrolyse the glycosidic linkage of glycosides (Lee et
al., 2014). They play a crucial role in the digestion of complex
carbohydrates, such as starch and cellulose.

\textbf{3.2 \textbar{} The characteristics of SNMs gut microbes}

Based on the taxonomical profiles, Firmicutes and Bacteroidetes were the
two main phyla (Figure 2b), and \emph{Clostridium} and
\emph{Prevotella}were the dominant genera (Figure 2c) in the RGC.
Additionally, we identified a common set of 241 non-redundant genes,
4032 COGs, 624 genera, 45 phyla, 3018 KOs and 191 CAZy families shared
by all 143 individuals (Figure 2d; Table S7), suggesting the existence
of a core set of genes, microbial composition and functions in the gut
microbes of SNMs.

To assess the extent to which our gene catalogue represents the
diversity of the gut microbiome in SNMs, we mapped published data
generated from 10 fecal individual samples of wild Sichuan SNM to the
RGC. On average, 76.55\% of the reads mapped to our gene catalogue
(Table S8). The mapping rate was similar to that of SNMs in our study
(Table S2), indicating that the RGC should represent the characteristics
of the gut microbiome in SNMs. Furthermore, we also mapped 10 fecal
individual samples of captive Sichuan SNM to the RGC, and the average
mapping rate was 49.83\% (Table S 8), indicating that captive
environment has a significant effect on the structure of gut microbiome
in SNMs.

\textbf{3.3 \textbar{} Comparison of the RGC with the gene catalogues of
cynomolgus macaque, human, mouse, pig and ruminants}

The RGC was compared to the catalogues of monogastric animals (human
(IGC) (J. Li et al., 2014), cynomolgus macaque (X. Li et al., 2018),
mouse (Xiao et al., 2015) and pig (Xiao et al., 2016)) and ruminants
(dairy cattle, water buffalo, yak, goat, sheep, roe deer and water deer)
(Xie et al., 2021). Our results showed that the RGC comprised more genes
than the human catalogue (9.9 M, 1267 samples), cynomolgus macaque
catalogue (1.99 M, 20 samples), mouse catalogue (2.6 M, 184 samples),
pig catalogue (7.7 M, 287 samples), roe deer catalogue 13.7 M, 50
samples) and water deer catalogue (7.7 M, 50 samples), but fewer genes
than that of the yak (32.9 M, 50 samples), water buffalo (34.4 M, 50
samples), dairy cattle (35.5 M, 60 samples), goat (20.97 M, 60 samples)
and sheep catalogues (24.2 M, 50 samples) (Table S6; Figure 3a). Alpha
diversity analysis of the 12 species showed that the alpha diversity
(Shannon Index) of the RGC was not significantly different from that of
most ruminants (goat, sheep, yak and water buffalo) at the genus level
(Figure 3b). Since ruminants and SNMs have high levels of structural
carbohydrates in their diet, they need to ferment these substances (such
as cellulose) for nutrients. At the KO functional level, the alpha
diversity results showed that the RGC had no significant difference with
water deer (Figure 3c), and monogastric animals generally had higher
functional diversity than ruminants and ruminant-like animals (SNMs)
(Figure 3c). In conclusion, the gut microbes of SNMs and ruminants are
more similar.

We also constructed phylogenetic and hierarchical trees of the 12
species of hosts and their gut microbes at the genus level to further
characterize the relationship between SNMs and ruminant gut microbes.
The SNMs gut microbes clustered with ruminant gut microbes (Figure 3d).
PCoA showed a similar trend at both the functional and taxonomic levels,
showing that SNMs gut microbes was more closely related to ruminant gut
microbes than to monogastric animal gut microbes (Figure 3e and Figure
3f). SNMs and ruminants have similar dietary characteristics and GIT
structures adapted to foregut fermentation (Liu et al., 2022). As the
mammalian gut evolved morphologically toward herbivory adaptation, their
microbiome reached a compositional configuration similar to that in
unrelated hosts that have similar GIT structures and diets (R. E. Ley et
al., 2008; Liu et al., 2022).

\textbf{3.4 \textbar{} Core genera in mammals}

We selected the genera that were shared by all samples of each gene
catalogue among the 12 species and termed these core genera. We
identified 624 such core genera in the SNMs gut microbiome and 95 in the
human gut microbiome (Table S10). Comparing the core genera from these
species, we found 74 core genera shared among all 12 species, but we
also noted that these genera had different relative abundance between
each host (Figure S3). Among these 74 core genera, we further selected
the top 30 most abundant genera in each species and displayed them in
box plots (Figure S4). Then we found 9 genera shared in all 12 species,
including \emph{Clostridium} , \emph{Ruminococcus} , \emph{Prevotella}
,\emph{Bacteroides} , \emph{Eubacterium} , \emph{Parabacteroides}
,\emph{Roseburia} , \emph{Alistipes} , and \emph{Oscillibacter} , which
may constitute a core mammalian gut microbiome (Figure S4). These genera
belong to the Firmicutes (\emph{Clostridium} , \emph{Ruminococcus}
,\emph{Eubacterium} \emph{Roseburia} and \emph{Oscillibacter} ) and
Bacteroides (\emph{Prevotella} , \emph{Bacteroides}
,\emph{Parabacteroides} and \emph{Alistipes} ).

\textbf{3.5 \textbar{} Effects of physiological structure of the host
GIT on the gut microbiome}

To investigate the relationship between the physiological structure of
the host GIT and their gut microbiome in host dietary adaptation, we
divided the above 12 species into ruminant animals, SNMs (ruminant-like
animals) and monogastric animals according to their GIT physiological
structures. First, LEfSe test was used to detect the taxonomic and
functional (carbohydrate active enzyme families) differences among the
three groups. At the genus level, \emph{Ruminococcus} , \emph{Treponema}
and \emph{Clostridium} were significantly enriched in SNMs, and
\emph{Fibrobacter} ,\emph{Butyrivibrio} and \emph{Prevotella} were
significantly enriched in ruminant animals (LDA\textgreater{}4, \emph{p}
\textless{}0.05) (Figure 4a, Figure S5). The functions of these genera
are all related to the fermentation of complex carbohydrates such as
cellulose (Palevich et al., 2019; Ransom-Jones, Jones, McCarthy, \&
McDonald, 2012; Yang Gu \& Jiang, 2010). However, in the monogastric
animals, \emph{Bacteroides} ,\emph{Faecalibacterium} , \emph{Roseburia}
and \emph{Phocaeicola} were significantly enriched, which were
associated with the degradation of fat and protein (LDA\textgreater{}4,
\emph{p} \textless{}0.05) (Figure 4a; Figure S5). Carbohydrate active
enzyme analysis showed that GHs, which significantly enriched in SNMs
(GH78, GH13, GH109) and ruminant animals (GH25, GH5), were related to
the degradation of structural polysaccharides. However, GHs that were
significantly enriched in monogastric animals (GH43, GH2, GH92, GH97,
GH105, GH29, GH28, GH32, GH20) were related to the degradation of
oligosaccharides (LDA\textgreater{}3, \emph{p} \textless{}0.05) (Figure
4b; Table S13). These results suggested that the composition and
function of the gut microbiome are related to the host's dietary
adaptation, and the structure of gut microbes is more similar between
SNMs and ruminants.

In addition, we found that the relative abundance of more than 50\%
(1859) of genera in the SNMs gut microbiome were higher than that in
monogastric animals, but lower than that in ruminant animals (Table
S14). \emph{Bacillus} (Firmicutes, Bacilli),
\emph{Butyrivibrio}(Firmicutes, Clostridia) and \emph{Fibrobacter}
(Fibrobacteres, Fibrobacterales) had the highest relative abundance
among these genera in the SNMs gut microbiome. Most of these 1859 genera
belonged to Firmicutes (129 genera, average relative abundance in SNMs
gut microbes was 1.44\%), Bacteroidetes (245 genera, average relative
abundance in SNMs gut microbes was 0.62\%), Proteobacteria (866 genera,
average relative abundance in SNMs gut microbes was 0.41\%\%),
Fibrobacteres (3 genera, average relative abundance in SNMs gut microbes
was 0.24\%), Actinobacteria (264 genera, average relative abundance in
SNMs gut microbes was 0.26\%), and Tenericutes (4 genera, average
relative abundance in SNMs gut microbes was 0.10\%). Bacteria in these
phyla have the ability to degrade complex carbohydrates, especially in
Firmicutes (Yang Gu \& Jiang, 2010) and Fibrobacteres (Ransom-Jones et
al., 2012). We further found that the relative abundance of 37 GHs in
SNMs was higher than that in monogastric animals but lower than that in
ruminants. Most of these GHs are associated with the degradation of
structural carbohydrates such as cellulose and starch (Figure S6; Table
S13). Analysis of the correlations between these genera and these GHs
showed that most bacteria correlated positively with these GHs (Table
S15). We also found that with the adaptive evolution of the host GIT
structure to herbivory, the diversity and abundance of GHs related to
the fermentation of structural carbohydrates also increased, and the
relationship between these genera and GHs are more complex. (Table S15),
because the host needed a stronger fermentation capacity to obtain
nutrients.

In summary, the structure of the SNMs gut microbiome was more similar to
that of ruminants, both in taxonomy and function. Moreover, the relative
abundance of some bacteria and carbohydrate enzymes in SNMs was
intermediate to that those of ruminants and monogastric animals,
suggesting that the physiological structure of the host GIT plays a
regulatory role in the gut microbiome during the adaptation evolution of
diet.

\textbf{4 \textbar{} DISCUSSION}

In this study, we constructed a gene catalogue of the gut microbiome of
wild SNMs for the first time by processing 143 fecal individual samples,
which contained 18,169,322 non-redundant genes. The RGC also represents
the first gene-set of the gut microbiome from wild NHP populations and
provides a comprehensive resource for further investigations of the NHP
gut microbiome. Compared with previous studies of the SNMs gut
microbiome (V. L. Hale et al., 2018; Trevelline \& Moeller, 2022; Xu et
al., 2015), the RGC comprehensively characterizes the gut microbiome of
SNMs and probably contains most of the gut microbial genes prevalent in
wild SNM populations. Metagenomic data from 10 wild Sichuan SNMs
researched in a previous study showed a mapping rate of more than 75\%
in the RGC, but there was relatively poor representation in captive
SNMs, with an average mapping rate was 49.83\% (Table S8). Previous
studies have suggested that the captivity environment has a significant
effect on the gut microbiome of SNMs (Hui Zhu, 2018; Mingpu Qi, 2017).
Similar results have also been reported in other NHPs (Clayton et al.,
2016; Emmanouil Angelakis, 2016; V. L. Hale et al., 2018; Tayte P
Campbell, 2020). This proves that the RGC is reliable in describing the
gut microbiome of SNMs. Therefore, a comprehensive analysis of the gut
microbiome based on the RGC is of practical significance for studying
their dietary adaptation, which could further guide the development of
better conservation strategies for these endangered species.

We used this catalogue to study the characteristics of the SNMs gut
microbiome. In our study, both taxonomic and functional results
suggested that the gut microbiome of SNMs was related to structural
carbohydrate degradation. Notably, our results were identical to those
of previous studies at the phylum level, while the differences were
large at the genus level (Wang et al., 2021; Yao et al., 2021). At the
phylum level, Firmicutes and Bacteroides were the main groups in the RGC
(Figure 2b). This is consistent with the results of previous studies on
Sichuan SNMs (Wang et al., 2021; Yao et al., 2021), Yunnan SNMs (Xia et
al., 2022) and Guizhou SNMs (V. L. Hale, Tan, C.L., Niu, K., Yang, Y.,
Zhang, Q., Knight, R., 2019). At the genus level, \emph{Clostridium}
,\emph{Prevotella} , \emph{Ruminococcus} and \emph{Bacteroides} were the
groups with the highest relative abundance in the RGC (Figure 2c), which
was different from the results of previous single-species studies (V. L.
Hale, Tan, C.L., Niu, K., Yang, Y., Zhang, Q., Knight, R., 2019; Wang et
al., 2021). This was likely because previous studies on the gut
microbiome of SNMs were mainly based on 16S rRNA analysis and small
sample sizes (Xia et al., 2022; Yao et al., 2021), preventing the
results from describing the general characteristics of the SNMs gut
microbiome. The consistency of the main phyla reflected that the gut
microbiome of SNMs was related to their dietary adaptation. Many
Firmicutes bacteria, including \emph{Clostridium} , are able to utilize
xylose, xylan and xyloglucan, which are the major hemicellulose
components of plant cell walls (Canfora, Meex, Venema, \& Blaak, 2019).
These results once again verified the reliability and comprehensiveness
of the RGC and preliminarily demonstrated that the gut microbiome
composition of SNMs plays an important role in their dietary adaptation.

Alpha and beta diversity analyses also revealed the important role of
the gut microbiome in the dietary adaptation of SNMs (Figure 3b, 3c and
3d). The comparative analysis of 12 mammals including SNMs, ruminants
and monogastric animals, indicated that the gut microbiome of SNMs was
clustered more closely to that of ruminants (Figure 3d), which was
similar to previous findings (Zhou et al., 2014). As the morphology of
mammalian GIT underwent convergent evolution to adapt to herbivory,
their microbiome might have developed similar compositional
configurations in unrelated hosts with similar gut structures (R. E. Ley
et al., 2008). We further found evidence of coevolution between SNMs and
ruminants during dietary adaptation. In SNMs, \emph{Ruminococcus}
,\emph{Treponema} and \emph{Clostridium} were significantly enriched at
the genus level, and GH78, GH13 and GH109 were significantly enriched in
carbohydrate enzymes (GHs). The genera that were significantly enriched
in ruminants were \emph{Fibrobacter} , \emph{Butyrivibrio}
and\emph{Prevotella} , and the GHs that were significantly enriched were
GH25 and GH5 (Figure 4a and 4b; Figure S5). Although the genera and GHs
that were significantly enriched in SNMs and ruminants were different,
these genera and GHs were mainly associated with the digestion of
structural carbohydrates. For example, \emph{Ruminococcus}
,\emph{Clostridium} and \emph{Butyrivibrio} belong to Firmicutes and are
related to the degradation of cellulose (Canfora et al., 2019), and GH5
and GH78 both have cellulase activity (Christiane Liers, 2021). However,
the genera and GHs that were significantly enriched in monogastric
animals were related to the degradation of fat and protein and
oligosaccharides, such as \emph{Bacteroides} (Naofumi Yoshida, 2021)
(Figure 4a and 4b). This indicated that the gut microbes of SNMs and
ruminants evolved similar digestive strategies in the process of
adapting to the plant diet and further suggested that the gut microbiome
is an important part of host dietary adaptation.

SNMs and ruminants also display similar physiological adaptation
strategies to plant-based diets, and they are both foregut fermentative
animals (Liu et al., 2022). The rumen of ruminants and the enlarged
saccular stomach of SNMs provide a large space for the bacteria to
ferment cellulose and other substances (D., 1998; Karasov \& Douglas,
2013; P., 1988), but the fermentation efficiency of SNMs may be lower
than that of ruminants, because the sacculated stomach of SNMs does not
show as strong functional division as ruminants, and the differentiation
is intermediate between those of monogastric animals and ruminants (D.
J. Chivers, Hladik, C.M, 1980). Previous studies have reported that the
morphology of the GIT has an important impact on the composition and
function of bacterial communities (R. E. Ley et al., 2008). In our
study, we found that the relative abundance of 1859 genera (over 50\%)
and 37 GHs in SNMs was higher than that in monogastric animals but lower
than that in ruminants (Table S14). Most of these GHs were associated
with degradation of structural polysaccharides, such as GH10, GH12, GH9,
GH14 and so on (Table S15). We also found that as the host GIT structure
adapted to herbivory, the diversity and abundance of GHs related to the
fermentation of structural carbohydrates also increased (Table S14),
suggesting that the dual effects of GIT morphology and the gut
microbiome promoted the dietary adaptive evolution in foregut
fermentative animals, which provides a new perspective for exploring the
evolutionary path of the gut microbiome in these animals.

In addition, we defined a set of core gut bacterial genera,
including\emph{Clostridium} , \emph{Ruminococcus} , \emph{Prevotella}
,\emph{Bacteroides} , \emph{Eubacterium} , \emph{Parabacteroides}
,\emph{Roseburia} , \emph{Alistipes} and \emph{Oscillibacter} , based on
the gut microbiome generated by shotgun sequencing of 12 mammalian
species (Figure S4). However, the relative abundance of these genera
varied profoundly between each species (Figure S3), which might result
from the influence of genetic, dietary and physiological characteristics
of the host (R. E. Ley et al., 2008). Our core genera reflected, to some
extent, the basic composition of the mammalian gut microbiome. Previous
studies reported that most of these genera were important keystone
bacteria of the gut microbiome, which are closely related to host food
degradation, nutrient absorption, health, and intestinal homeostasis
(Ruth E Ley, 2016; Petia Kovatcheva-Datchary, 2015). Therefore, it is
highly necessary to focus on these core genera in future analyses of the
mammalian gut microbiome.

\textbf{5 \textbar{} CONCLUSION}

One of the most important features of this study was the large-scale
metagenomic analysis of 143 fecal individual samples from three species
of SNMs, which generated the first reference gene catalogue covering
most of the genes in the gut microbiome of SNMs, enabling us to fully
understand the detailed genetic diversity of the gut microbiome of SNMs.
The identification of bacteria and carbohydrate enzymes in the gut
microbiome of SNMs suggested their adaptation to a plant-based diet. We
compared metagenomic diversity and functional potential in SNMs with
those in ruminants and monogastric animals, and revealed that the
interaction between GIT morphology and the gut microbiome jointly
promotes dietary adaptation in foregut fermenting animals. We also
identified 9 highly abundant bacterial genera as core genera in these
mammals. In conclusion, this study provides a reference and data
framework to further reveal the evolutionary adaptation of SNMs and to
analyze the evolutionary route of the gut microbiome in animals with
foregut fermentation.



\textbf{Supplementary Material}

Supplementary information: figures S1-S6, tables S1--S15 and
description.

\textbf{Author Contributions}

M.L. and X.W. conceived this study and designed this project. X.W., J.Z.
and H.P. managed this project. Y.C., D.L., Z.X., S.M., M.Z. and Y.S.
collected samples. X.W. and J.Z. performed the experiments. X.W., J.Z.
and H.P. analyzed the data. J.Z., X.W., H.P. and M.L. wrote the
manuscript. D.L., Z.X., S.M., Y.S. and M.Z. provided help with the
analysis. All the authors critically reviewed and approved the
manuscript. M.L. supervised this study.

\textbf{Funding}

This study was supported by the Strategic Priority Research Program of
the Chinese Academy of Sciences (XDB31000000 and XDA19050000), the
National Natural Science Foundation of China (31821001 and 32070404) and
the State Forestry Administration of China.

\textbf{Acknowledgments}

The authors would like to thank Drs. Xiaoguang Qi and Hui Yao for their
help in sampling, and thank American Journal Experts (www.aje.com) for
the manuscript editing support.

\textbf{Competing Interests}

The authors declare no competing interests.

\textbf{Ethics Approval}

This study was approved by the Ethics Committee of the Institute of
Zoology, Chinese Academy of Sciences (CAS).

\textbf{Availability of data and materials}

Raw sequence reads generated from this study have been submitted to the
NCBI under BioProject XXXXX. All the gene catalogues, annotation
information and relative abundance profiles from this study are
available at XXXXXX. All data, code, and materials used in the analyses
are available in the main text or the supplementary materials.

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\textbf{Figure legends}

\textbf{Figure 1.} \textbf{Pipeline for the construction of the gene
catalogue of} \emph{Rhinopithecus} \textbf{gut microbiome (RGC).}Shotgun
sequencing reads for fecal samples from \emph{R. roxellana} ,\emph{R.
bieti} and \emph{R. strykeri} individuals were independently processed.
We generated three non-redundant gene-sets and merged them into an
integrated reference gene catalogue of the SNMs gut microbiome (RGC).



\textbf{Figure 2. Annotation of the RGC} . \textbf{A} . Breakdown of
taxonomic annotations for the RGC. \textbf{B} . The top 11 phyla in the
RGC. Firmicutes and Bacteroidetes are the main two phyla in the
RGC.\textbf{C} . The top 20 genera in the RGC. \emph{Clostridium} is the
main genus in the RGC. \textbf{D.} Number of shared microbiome features
among SNMs at different frequency thresholds for the number of genes
(black), COGs (pink), genera (orange), phyla (blue), KOs (purple) and
CAZymes (yellow). The percentages of shared items and animals are
represented on the y and x axes, respectively. The absolute numbers for
each item are indicated at the intercept between the percentages of
items and animals at the thresholds of 60\% and 100\%. Fewer than 2\% of
genes (207,635 genes) were shared by 60\% of individuals, while
approximately 36.5\% of the KO functions (2,943) were shared by 60\% of
individuals, suggesting redundancy of genes for similar functions.

\textbf{Figure 3. Comparisons of the RGC with the catalogues of
ruminants and monogastric animal.} \textbf{A.} The number of
non-redundant genes in 12 mammalian species. \textbf{B and C} . Alpha
diversity (Shannon index) at the genus, and KO function levels. Data are
shown as box plots. The horizontal lines indicate the medians, and the
whiskers indicate the lowest and highest points within 1.5× the
interquartile ranges into the lower and upper quartiles, respectively.
Coloured circles at the bottom indicate significance based on the
relative index of each cohort according to the Wilcoxon rank-sum test.
*\emph{P} \textless{} 0.05, **\emph{P} \textless{} 0.01,
***\emph{P}\textless{} 0.001. D. Comparison of the host phylogeny tree
(right panel; assembled using http://timetree.org/) and their gut
microbiome hierarchical tree (upper left panel). \textbf{E.} PCoA plot
of the gut microbiome composition in the 12 species at the genus level.
\textbf{F.}PCoA plot of the gut microbiome composition in the 12 species
at the KO function level.

\textbf{Figure 4.} \textbf{Genera and CAZy terms differentially
represented among ruminant animals, SNMs (ruminant-like animals) and
monogastric animals (LEfSe).} \textbf{A.} LEfSe test at genus level
(LDA\textgreater{}0.4, P\textless{}0.05). Red circle: enriched in SNMs,
green circle: enriched in ruminant animals, yellow circle: enriched in
monogastric animals. B. LEfSe test at the CAZy level. Red box: enriched
in SNMs, green box: enriched in ruminant animals, yellow box: enriched
in monogastric animals. Text in bold: enriched in GH families.\selectlanguage{english}
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