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\begin{document}

\title{Seasonal Dietary Shifts Alter the Gut Microbiota of a Frugivorous Lizard
Teratoscincus roborowskii (Squamata, Geckonidae)}



\author[1]{Lei Shi}%
\author[1]{Weizhen Gao}%
\author[1]{Yang Yi}%
\affil[1]{Xinjiang Agricultural University}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Seasonal dietary shifts of animals are important ecological adaptation
strategies. An increasing number of studies have shown that seasonal
dietary shifts can influence or even determine the composition of gut
microbiota. The turpan wonder gecko Teratoscincus roborowskii lives in
extreme desert environments, which have flexible dietary shift to
fruit-eating in warm seasons. But the impact of such shifts on gut
microbiota is poorly understood. Here, 16SrRNA sequencing and LC-MS
metabolomics we used to examine the changes of gut microbiota
composition and metabolic pattern of T. roborowskii. The results
demonstrated that the gut microbes of T. roborowskii had significant
seasonal changes, the diversity and abundance of gut microbiota in
autumn were higher than those of in spring. Firmicutes, Bacteroidetes
and Proteobacteria were the core gut microbes of T. roborowskii.
Verrucomicrobia and Proteobacteria exhibit dynamic pattern of ebb and
flow between spring and autumn.The composition and structure of gut
microbes in different seasons perform specific metabolic functions, and
this change may be an important adaptation for T. roborowskii to cope
with dietary shifts and improve energy acquisition. Our study will
provide a theoretical basis for exploring the adaptive evolution to
special frugivorous behavior of the T. roborowskii, which is an
important supplement to the study of the gut microbiology of desert
lizards.%
\end{abstract}%



\sloppy


Seasonal Dietary Shifts Alter the Gut Microbiota of a Frugivorous
Lizard\emph{Teratoscincus roborowskii} (Squamata, Geckonidae)

Wei-Zhen Gao\textsuperscript{+}, Yi Yang\textsuperscript{+}11+These
authors have contributed equally to this work and share first
authorship, Lei Shi\textsuperscript{*}

College of Life Sciences, Xinjiang Agricultural University, Urumqi,
Xinjiang 830052, China

\textbf{* Correspondence:} Lei Shi, Ph.D., College of Life Sciences,
Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China.
E-mail: leis@xjau.edu.cn

Abstract

Seasonal dietary shifts of animals are important ecological adaptation
strategies. An increasing number of studies have shown that seasonal
dietary shifts can influence or even determine the composition of gut
microbiota. The turpan wonder gecko\emph{Teratoscincus roborowskii}
lives in extreme desert environments, which have flexible dietary shift
to fruit-eating in warm seasons. But the impact of such shifts on gut
microbiota is poorly understood. Here, 16SrRNA sequencing and LC-MS
metabolomics we used to examine the changes of gut microbiota
composition and metabolic pattern of \emph{T. roborowskii} . The results
demonstrated that the gut microbes of \emph{T. roborowskii} had
significant seasonal changes, the diversity and abundance of gut
microbiota in autumn were higher than those of in spring. Firmicutes,
Bacteroidetes and Proteobacteria were the core gut microbes of \emph{T.
roborowskii} . Verrucomicrobia and Proteobacteria exhibit dynamic
pattern of ebb and flow between spring and autumn.The composition and
structure of gut microbes in different seasons perform specific
metabolic functions, and this change may be an important adaptation for
\emph{T. roborowskii} to cope with dietary shifts and improve energy
acquisition. Our study will provide a theoretical basis for exploring
the adaptive evolution to special frugivorous behavior of the \emph{T.
roborowskii} , which is an important supplement to the study of the gut
microbes of desert lizards.

\textbf{Keywords: \emph{Teratoscincus roborowskii}, \emph{s}easonal
dietary changes, gut microbiota, metabolomics, ecological adaptation}

Introduction

The intestinal structure of animals satisfies the requirements of food
digestion and nutrient absorption. It also maintains homeostasis of gut
microbes (Sommer \& B\selectlanguage{ngerman}äckhed, 2013). Complex interactions of the gut
microbial ecosystem occur between the host and its gut microbes
(McFall-Ngai et al., 2013). The gut microbiota plays an important role
in the intestinal immune system, energy metabolism (Sommer \& Bäckhed,
2013), reproductive activity (MacLeod et al., 2022), and longevity (Kim
\& Benayoun, 2020)of the host.

The composition, structure, and function of gut microbiota are
influenced by multiple factors (Suzuki \& Worobey, 2014). Seasonal
environmental fluctuations can significantly alter the gut microbiota of
animals. For example, the gut microbiota of \emph{Pteromys
volansorii}varies seasonally on an ecological scale (Liu et al., 2019).
In addition, seasonal physiological changes associated with hibernation
also alter gut microbiota in greater horseshoe bats (\emph{Rhinolophus
ferrumequinum} ) and brown bears (\emph{Ursus arctos} ) (Sommer et al.,
2016; Xiao et al., 2019). Trophic niche expansion is an active
adjustment of wildlife to seasonal and environmental changes (Guisan et
al., 2014), and dietary shifts can affect or even determine the
composition of animal gut microbes that change dynamically with~the
season (Maurice et al., 2015). Studies on wood mice (\emph{Apodemus
sylvaticus} ), plateau pikas (\emph{Ochotona curzoniae} ), brown frogs
(\emph{Fejervarya limnocharis} ), black howler monkeys (\emph{Alouatta
pigra} ), and giant pandas (\emph{Ailuropoda melanoleuca} ) have shown
that gut microbes undergo dynamic seasonal changes (Maurice et al.,
2015; Fan et al., 2022; Huang et al., 2021; Amato et al., 2015; Wu et
al., 2017). These results indicate that seasonal dietary shifts can
significantly alter animal gut microbiota.

At present, fecal metabolomics is widely considered a key tool for
studying the relationship between hosts and their gut microbiota and has
attracted extensive attention (Zierer et al., 2018). Metabolomics
enables researchers to identify a large proportion of the metabolites
present in a sample. By analyzing these products of cellular metabolism,
metabolomics can reveal valuable information about an organism's
metabolic or physiological state at the time of sampling (Tang et al.,
2020). For example, in wildlife research, joint analysis of metabolomics
and gut microbiota has revealed differences in the abundance and
metabolic phenotypes of gut microbes of \emph{Panthera pardus
japonensis} in captive and wild environments (Hua et al., 2020). The
co-metabolic patterns of the gut and microbes in mountain gorillas
(\emph{Gorilla beringei beringei} ) and western lowland gorillas
(\emph{Gorilla gorilla} ) indicate that dietary restriction is a
potential factor driving specific changes in gut microbes (Gomez et al.,
2016). Combined analysis of gut microbes and metabolomics has also
become an effective approach for identifying relevant biomarkers to
reveal complex changes in metabolic and biochemical pathways (Tran et
al., 2020).

Significant differences in the composition, structure, and abundance of
gut microbiota in different animal groups, such as mammals (Zhao et al.,
2018), birds (Grond et al., 2018), and fishes (Egerton et al., 2018),
have been extensively studied. Reptiles, especially lizards, have a
special evolutionary history and environmental adaptation behaviors
(Pianka \& Vitt, 2003). however, relatively little research has been
conducted on the gut microbes of lizards (Colston \& Jackson, 2016).
Relevant studies have focused on changes in the patterns of gut microbes
of crocodile lizards (\emph{Shinisaurus crocodilurus} ), western fence
lizards (\emph{Sceloporus occidentalis} ), northern grass lizards
(\emph{Takydromus septentrionalis} ), and mesquite lizards
(\emph{Sceloporus grammicus} ) under the influence of human activities,
temperature, diet, altitude, and other factors (Tang et al., 2020;
Moeller et al., 2020; Zhou et al., 2020; Montoya-Ciriaco et al., 2020).

The Turpan wonder gecko (\emph{Teratoscincus roborowskii} ) is a walking
gecko species endemic to the Turpan Depression in the Xinjiang Uyghur
Autonomous Region of China (Shi et al., 2007; Shi et al., 2002). They
live in extreme desert environments (Song et al., 2009; Zhou et al.,
2019). Research on Turpan wonder geckos mainly focuses on foraging mode,
activity rhythms, and seed dispersal (Song et al., 2017; Werner et al.,
1997; Yang et al., 2021). Field observations and dietary analysis have
shown that \emph{T. roborowskii} feeds mainly on insects in spring,
whereas the proportion of caper fruits in the food can reach 85\% in
summer and autumn, showing a significant seasonal shift in dietary
habits (Liu et al., 2010; Yang et al., 2021). This seasonal dietary
shift may be an important factor in \emph{T. roborowskii} adaptation to
harsh and arid desert habitats.

The Turpan wonder gecko goes through a long hibernation process every
year (Li et al., 2010). Fruit-eating strategies are more likely to be
deployed with seasonal variations in food availability to maximize
pre-hibernation food intake and satisfy nutritional requirements (Naya
et al., 2010; Robbins et al., 2012). Related studies have shown that
switching from insects to plant fruits is a common mechanism through
which migrating birds accumulate fat for energy~(Marshall et al., 2016).
Gerbils (\emph{Psammomys obesus} ), which also live in a desert
environment, can convert a small amount of carbohydrates into endogenous
fructose due to the stimulation of a high-salt diet, thereby aggravating
the tendency to obesity and effectively adapting to the lack of food
(Kaiser et al., 2012). Turpan wonder geckos face abundant caper fruit
resources in summer and autumn. From this, we can speculate that fresh
caper fruit is not only an excellent source of water and nutrients but
also a special way for Turpan wonder geckos to acquire energy and store
fat during the pre-hibernation period. This dramatic shift in seasonal
dietary habits can lead to alterations in gut microbes to meet the
functional needs of dietary transition. Therefore, 16S rRNA sequencing
technology and LC-MS metabolomics were used in this study to explore
changes in the gut microbiota and metabolic patterns of Turpan wonder
geckos during spring and autumn.

\section*{Materials and methods}

{\label{materials-and-methods}}

\textbf{Animal and feces collection}

\emph{T. roborowskii} individuals were captured from the desert in the
suburb of Turpan in May 2021 and September 2021. The capture site is
located near the Turpan Eremophytes Botanic Garden. In consideration of
the search ability of the site, we use the coordinates of the Turpan
Eremophytes Botanic Garden to locate it. Which is located in the Turpan
Basin in the Xinjiang Uyghur Autonomous Region of China (40°51\selectlanguage{english}'N,
89deg11\selectlanguage{english}'E). The experiment was divided into two groups: (1) 11 lizards
were captured in spring (spring group; SG), and 23 complete fresh fecal
pellets were collected during the fasting period; (2) 15 lizards were
captured in autumn (autumn group; AG), and 21 complete fresh fecal
pellets were collected during the fasting period.

After capture, Turpan wonder geckoes were numbered, and their sex and
other basic information were recorded. Subsequently, each \emph{T.
roborowskii} was placed in a clean feeding box and fasted during this
period. A noninvasive fecal sampling method was used to check for
excretion every 2 h to ensure the timely collection of fresh fecal
samples. Complete fecal samples were collected into sterile cryovials
using fresh, sterilized tweezers. Samples were immediately marked and
snap-frozen in liquid nitrogen, and then transferred to a -80\selectlanguage{english} freezer
for subsequent experimental operations. Our previous study showed that
the transit time of food through the digestive tract of \emph{T.
roborowskii} is up to 15 days (Yang et al., 2021), therefore, fecal
collection was performed while no food was provided for 15 days. After
the experiment, all the animals were in good physiological condition and
were treated in strict accordance with the relevant regulations on
animal welfare. All of the experiments and handling of the animals were
conducted according to the research protocols approved by the Animal
Welfare and Ethics Committee of Xinjiang Agricultural University.

\textbf{DNA extraction and PCR amplification}

Fecal samples from different seasons were homogenized to obtain four
groups of mixed samples for spring and autumn (Zhou et al., 2020; Xiao
et al., 2019; Gong et al., 2021). Extraction of total microbial DNA from
fecal samples was performed. The V3-V4 hypervariable regions of the
microbial 16S rRNA gene were amplified by PCR using primers 338F
(5\selectlanguage{english}'-ACTCCTACGGGAGGCAGCA-3\selectlanguage{english}') and 806R (5\selectlanguage{english}'-GGACTACHVGGGTWTCTAAT-3\selectlanguage{english}'). The
PCR conditions were as follows: 98degC for2 min; 30 cycles at 98degC for
15s, 55degC for 30s, and 72degC for 30s; and extension at 72degC for 5 min
(Zhou et al., 2020). The products were purified, quantified, and
normalized to generate a sequencing library (SMRT Bell). The constructed
library was first subjected to library quality inspection, and the
qualified products were sequenced using PacBio Sequel at Biomarker
Technologies Co., Ltd. (Beijing, China).

Raw subreads were calibrated to get circular consensus sequencing (CCS)
(SMRT Link, version 8.0), then Lima v1.7.0 software was used to identify
CCS sequences of different samples through barcode sequences and remove
chimeras to obtain Effective-CCS sequences. Operational taxonomic units
(OTUs) were clustered with a 97\% similarity cutoff using UPARSE
(version 10.0) (Edgar, 2013).

Alpha diversity index analysis was performed using QIIME2 software, via
the Wilcoxon rank-sum test to compare the community diversity indices
(Ace richness estimator and Shannon-Wiener index) (Prehn-Kristensen et
al., 2018; Schloss et al., 2011).

Dimensionality reduction of data was based on the Bray-Curtis distance
matrix by principal coordinate analysis (PCoA), and nonmetric
multidimensional scaling (NMDS) was used to observe the differences in
gut microbial community structure (Oksanen et al., 2013). We modeled the
binary Jaccard distance dissimilarities based on an operational
taxonomic unit (OTU) level table via analysis of similarities (ANOSIM)
(Anderson, 2001).

Using SILVA as the reference database, the community species composition
analysis of the two groups of experiments was performed at various
levels: phylum, order, family, genus, and species. Linear discriminant
effect size (LEfSe) analysis was used to screen microorganisms with
large differences as potential markers (Segata et al., 2011), the
significance of different species was determined using Matestats
software, and differential strains were screened according to the
criteria of LDA\textgreater{}4, q\textless{}0.05. Picrust2 was used to
predict changes in KEGG-enriched functional pathways across the groups
(Kanehisa, 2019).

\textbf{LC-MS metabolomics detection}

Metabolites of fecal samples were detected in positive and negative ion
mode, and metabolites were separated using a Waters Acquity 1-Class PLUS
ultraperformance liquid chromatography system (Waters XevoG2-XS QTOF
high-resolution mass spectrometer). Primary and secondary mass
spectrometry data were collected in MSe mode under the control of
acquisition software (MassLynx V4.2, Waters). In each data acquisition
cycle, dual-channel data acquisition can be performed at both low and
high collision energies simultaneously. The low collision energy is 2 V,
the high collision energy range is 10\textasciitilde{}40 V, and the scI
ion source is as follows: capillary voltage, 2000 V (positive ion mode)
or -1500 V (negative ion mode); cone voltage, 30 V; ion source
temperature, 150 degC; desolvent gas temperature, 50 degC; backflush gas
flow rate, 50 L/h; desolventizing gas flow rate, 800 L/h (Wang et al.,
2020).

The raw data collected using MassLynx V4.2 was processed by Progenesis
QI software for peak extraction, peak alignment, and other data
processing operations, based on the Progenesis QI software online METLIN
database and Biomark's self-built library for identification; at the
same time, theoretical fragment identification and mass deviation were
within 100 ppm.

\textbf{Quality control and data analysis of fecal metabolite detection}

Principal component analysis (PCA) was performed on metabolite data, and
the OPLS-DA model was calculated using the R (3.3.2) package ``ropls''
to visualize the clustering of samples (Thevenot et al., 2015). The
differential metabolites were screened by combining the fold difference,
P-value of the \emph{t} -test, and VIP value of the OPLS-DA model. The
screening criteria were FC\textgreater{}1, P-value \textless{}0.05, and
VIP\textgreater{}1. The cluster profiler used the hypergeometric test
method to perform enrichment analysis on the annotation results of the
differential KEGG metabolites and screened some representative metabolic
pathways for impact value and significance as the enrichment criteria.

\textbf{Results}

\textbf{Composition of gut microbiota}

A total of 468 different OTUs were divided into the spring (SG) and
autumn (AG) groups (Figure 1A). Among these, there were 373 OTUs in the
spring group, 434 OTUs in the autumn group, and 339 OTUs in both groups
(Figure 1B).

\textbf{Analysis of alpha diversity of gut microbiota}

The results of alpha diversity analysis based on the phylum level using
the Wilcoxon rank sum test showed that the Ace richness estimator in the
autumn group (\emph{P} =0.023\textless{}0.05) was significantly higher
than that in the spring group (Figure 2A). However, the Shannon--Wiener
diversity index in the spring group (\emph{P} =0.029\textless{}0.05) was
significantly higher than in the autumn group (Figure 2B). At the family
level, the Ace (\emph{P} =0.029\textless{}0.05) and Shannon (\emph{P}
=0.029\textless{}0.05) indices in the autumn group were significantly
higher than those in the spring group (Figure 2C, D).

\textbf{Analysis of beta diversity of gut microbiota}

PCoA was conducted to observe the differences in the gut microbial
community structure. The results show that the contribution rates of the
first and second principal components are 50.28\% and 20.22\%,
respectively. The distance between the groups was relatively close, and
the distance between the groups was further; that is, the samples of the
two treatment groups were well-clustered, and the separation between the
different groups was clear. Compared with the spring group, the gut
microbial community structure among \emph{T. roborowskii} individuals in
the autumn group had higher similarity (Figure 3A).

The results of NMDS and similarity analysis indicated a significant
difference in the gut microbiota between the spring and autumn groups
(Stress=0.0008; Anosim: R=0.865, \emph{P} =0.026). The distribution
within the group was more discrete in the spring group and more uniform
in the autumn group, suggesting that food and environmental influences
after hibernation promoted the clustering and stability of the gut
microbes of the Turpan wonder gecko (Figure 3B). In conclusion, the gut
microbes of Turpan wonder geckos changed significantly between spring
and autumn, manifesting as increased microbial abundance and diversity.

\textbf{Analysis of gut microbiota species composition and differences}

The composition of the gut microbiota in the spring and autumn groups
of\emph{T. roborowskii} is shown in Figure 4. At the phylum level, the
main annotated groups were Firmicutes (32.79\%; 36.92\%), Bacteroidetes
(31.53\%; 34.45\%), Proteobacteria (7.28\%; 18.99\%), Verrucomicrobia
(19.58\% and 4.30\%), and Tenericutes (4.62\% and 1.93\%, respectively)
(Figure 4A).

At the family level, the main annotated groups were Bacteroidaceae
(15.15\%; 16.54\%), Lachnospiraceae (10.12\%; 20.54\%), Akkermansiaceae
(19.58\%; 4.30\%), Erysipelotrichaceae (11.90\%; 3.67\%), Tannerellaceae
(9.06\%; 4.99\%), Lactococcaceae (6.38\%; 7.31\%), Pseudomonas (0.00\%;
11.15\%), and others were highly abundant in fecal samples (Figure 4B).

\emph{Bacteroides} (15.15\%; 16.54\%), \emph{Akkermansia} (19.58\%;
4.30\%), \emph{Parabacteroides} (9.06\%; 4.99\%),
\emph{Roseburia}(0.06\%; 11.72\%), \emph{Pseudomonas} (0.00\%;
11.15\%),\emph{Odoribacter} (1.89\%; 6.83\%), and \emph{Alistipes}
(4.52\%; 2.68\%), among others, were the core bacterial genera of
\emph{T. roborowskii} (Figure 4C).

Based on the above analysis of community structure and species
composition, the gut microbes of the Turpan wonder geckos changed to a
certain extent in different seasons. We further used LEfSe analysis
based on the effect pattern of LDA (LDA\textgreater{}4,\emph{P}
\textless{}0.05) to screen the microorganisms with significant
differences in relative abundance between the two groups. At the phylum
level, the relative abundance of Tenericutes (LDA=4.08, \emph{P} =0.02)
in the spring group was significantly higher than in the autumn group;
however, the abundance of Proteobacteria was higher in the autumn group
(LDA=4.74, \emph{P} =0.02).

At the family level, Erysipelotrichacea (LDA=4.64, \emph{P} =0.021),
Tannerellaceae (LDA=4.31, \emph{P} =0.02), and uncultured bacterium
Mollicutes RF39 (LDA=4.06, \emph{P} =0.02) were present at significantly
higher levels in the spring group than in the autumn group. In the
autumn group, Enterobacteriaceae (LDA=4.22, \emph{P} =0.018),
Marinifilaceae (LDA=4.41, \emph{P} =0.02), Lachnospira
(LDA=4.67,\emph{P} =0.021), and Pseudomonas (LDA=4.68, \emph{P} =0.014)
were significantly more prevalent than in the spring group.

At the genus level, \emph{Erysipelatoclostridium} (LDA=4.64,\emph{P}
=0.02), \emph{Parabacteroides} (LDA=4.28, \emph{P}
=0.021),\emph{Breznakia} (LDA=4.10, \emph{P} =0.021),
and\emph{uncultured\_bacterium\_o\_Mollicutes\_RF39} (LDA=4.10,\emph{P}
=0.021) were significantly higher in the spring group,
while\emph{Odoribacter} (LDA=4.38, \emph{P} =0.021),
\emph{Rothella}(LDA=4.73, \emph{P} =0.014) and \emph{Pseudomonas}
(LDA=4.18,\emph{P} =0.014) were significantly higher in the autumn group
(LDA\textgreater{}4, \emph{P} \textless{}0.05; Figure 5).

\textbf{Predictive analysis of gut microbial function}

The functional pathway abundance of KEGG corresponding to the 16S rRNA
sequencing data was predicted using picrust2. The results show that the
KEGG metabolic pathways at the first level mainly consisted of the
following functional categories: metabolism (78.6\%), genetic
information processing (8.0\%), environmental information processing
(6.5\%), cellular processes (3.0\%), human diseases (2.5\%), and organic
systems (1.4\%).

In the differential analysis of KEGG metabolic pathways at the second
level, the carbohydrate metabolism, replication and repair, and drug
resistance pathways were significantly enhanced in the spring group
(\emph{P} \textless{}0.05). Amino acid metabolism, metabolism of
terpenoids and polyketides, xenobiotic biodegradation and metabolism,
cell motility, signal transduction, and lipid metabolism were
significantly enhanced in the autumn group (\emph{P} \textless{}0.05;
Figure 6A).

In the enrichment process of the third-level metabolic pathways,
glycolysis/gluconeogenesis, amino sugar and nucleotide sugar metabolism,
and metabolic pathways such as fructose and mannose metabolism,
galactose metabolism, and purine metabolism were significantly higher in
the spring group (\emph{P} \textless{}0.05). The two-component system
and glycine, serine, and threonine metabolism were significantly
enhanced in the autumn group (\emph{P} \textless{}0.05; Figure 6B).

\textbf{Fecal metabolic profiling of}T. roborowskii

The fecal metabolic profiles of \emph{T. roborowskii} were acquired by
LC-MS, and metabolism was detected with good intra-group
reproducibility. Fecal metabolites were significantly separated in the
first principal component (PC1: 82.9\%) (Figure 7A). In the OPLS-DA
model, the metabolic curves were clearly different between the two
sample groups (Q2=0.99\textgreater{}0.9 indicates an excellent model),
and the results show that there are obvious differences in fecal
metabolites between the spring and autumn groups (Figure 7B).

The absolute values of log2FC (Fold Change, FC) were sorted to get the
top 10 metabolites in each group. In the spring group, the levels of
hexaethylene glycol, leucyl-tyrosine, scillipheosidin
3-{[}glucosy-(1-\textgreater{}2)-rhamnoside{]},
1-O-caffeoyl-(b-D-glucose 6-O-sulfate), serinyl-gamma-glutamate,
bradykinin hydroxyproline, phaseolic acid, TR-saponin B, corepoxylone,
and ethylparaben were high. Methyl dihydrojasmonate, dodecanoic acid,
trans-2-hexyl-1-cyclopropaneacetic acid, cochliophilin A,
``PC(20:5(5Z,8Z,11Z,14Z,17Z)/24:0),'' uridine diphosphate glucose,
grevillol, benzyl hexanoate, trans-cinnamic acid, etc. were higher in
the autumn group (Figure 8).

The differential metabolites were classified as carboxylic acids and
derivatives, fatty acids, organooxygen compounds, steroids and steroid
derivatives, glycerol glycerophospholipids, prenol lipids, and
glycerolipids.

Differential metabolites were screened based on the criteria of\emph{P}
=0.05, VIP=1, and FC=1. Finally, 1264 differential metabolites were
detected in the two groups of samples, of which 668 were upregulated
(higher in the autumn group), and 596 were downregulated (higher in the
spring group). The relative contents of deoxycholic acid glycine
conjugate and 26-methyl nigranoate were relatively higher in the spring
group. Simulansine, 3-oxocholic acid, and asparaginyl-proline were the
three metabolites with the highest relative contents in the autumn group
(Figure 9).

KEGG functional classification of metabolites showed that metabolites
participate mainly in metabolism, organismal systems, human diseases,
environmental information processing, and other functional pathways.
Among these, metabolic pathways, biosynthesis of secondary metabolites,
and microbial metabolism in different environments are related to a
variety of metabolites.

The KEGG enrichment network map of differential metabolites showed that
lysine degradation, pantothenate and CoA biosynthesis, steroid hormone
biosynthesis, carbon fixation pathways in prokaryotes, and other
differential metabolites between different seasons had a close positive
or negative relationship (Figure 10).

\textbf{Fecal metabolic profiling and its correlation with the gut
microbiota}

Metabolite clusters were obtained by hierarchical cluster analysis
(HCA). Pearson correlation analysis of significantly different
metabolites and significantly different OTUs (microbes) was performed to
determine the relationships between metabolites and microbes (Figure
11). The results showed that Proteobacteria, Verrucomicrobia,
Lentisphaerae, Elusimicrobia, Tenericutes, and other phyla had the
closest relationship with the metabolites, and the dominant
microorganisms showed different positive or negative correlations with
different metabolite clusters. This confirms the results of the LEfSe
analysis; that is, bacterial phyla with significant seasonal differences
correspond to some differential metabolites, suggesting that they are
closely related and may be the key factors affecting gut microbes.

\textbf{Discussion}

The strict nocturnal activity rhythm of \emph{T. roborowskii} (Song et
al., 2009) and their special burrow construction not only avoid high
temperatures in the daytime but also create a relatively constant living
temperature. These behaviors are effective mechanisms for adapting to
harsh and hot desert environments (Song et al., 2017). It is clear that
seasonal dietary shifts are also a way to cope with the drought
environment and enrich the food composition of \emph{T. roborowskii} ,
which may be the main reason for the significantly higher gut microbial
abundance and diversity of\emph{T. roborowskii} in autumn relative to
the spring. Studies on insectivorous and carnivorous bats have shown
that diet variation significantly affects the gut microbial community
structure (Gong et al., 2021). Therefore, improvement of gut microbial
diversity and changes in community structure in \emph{T. roborowskii}
may be the main factors in coping with seasonal dietary shifts. Kohl et
al. (2016) fed \emph{Liolaemus ruibali} a diet ratio of 50\% insects +
50\% plants or 10\% insects + 90\% plants and found that lizards fed a
rich, plant-based diet showed a higher level of gut microbial diversity.
This is consistent with our results; that is, the intake of caper fruit
significantly increased the diversity of gut microbes, and this change
may be beneficial to the achievement of gut function and the
stabilization of microbiota in\emph{T. roborowskii} (Soriano et al.,
2018).

At the phylum level, the contents of Firmicutes, Bacteroidetes,
Proteobacteria, and Verrucobacterium in the gut microbes of\emph{T.
roborowskii} in spring and autumn accounted for more than 91\% of the
core flora. Studies have shown that Firmicutes, Bacteroidetes, and
Proteobacteria are important members of the gut microbiota in most
vertebrates (Zhao et al., 2018; Hale et al., 2019; Wang et al., 2018;
Kohl et al., 2013). As a nocturnal desert gecko, the leopard gecko
(\emph{Euplepharis macularus} ) possesses a gut microbial composition
that is highly similar to that of \emph{T. roborowskii} and remains
stable after 28 d of fasting, suggesting that animals living in extreme
desert environments have a high degree of control over gut microbes
(Kohl et al., 2014). Other lizard species, such as a toad-headed lizard
(\emph{Phrynocephalus vlangalii} ) (Zhang et al., 2018), \emph{S.
crocodilurus} (Jiang et al., 2017), \emph{T. septentrinalis} (Zhou et
al., 2020), and \emph{S. occidentalis} (Moeller et al., 2020), are also
dominated by Firmicutes and Bacteroidetes, which indicates that
different lizard taxa have similar gut microbial composition at higher
taxonomic levels.

However, the gut microbial composition of\emph{T. roborowskii} is unique
at both the family and genus levels. \emph{S. crocodilurus} lives in
warm, moist habitats and feeds mainly on earthworms and loaches. The
main bacterial families are Pasteurellaceae, Deinococcaceae, and
Comamonadaceae (Jiang et al., 2017). There were significant differences
compared with \emph{T. roborowskii} . A study of \emph{T.
septentrionali} s from eastern China showed that this insect-eating
lizard has a composition similar to that of gut microbes of \emph{T.
roborowskii} at the family level, but the abundance differs greatly
among families (Zhou et al., 2020). \emph{P. vlangalii} living at high
altitudes appears to have a more similar gut microbial composition to
that of \emph{T. roborowskii} (Zhang et al., 2018), which may be caused
by similar food sources and arid environments. It can be seen that some
factors such as diet (Campos et al., 2018) and environmental factors
(Kartzinel et al., 2019) can significantly affect the composition of
lizards' gut microbes.

Individuals with a high ratio of Firmicutes to Bacteroidetes have a
higher ability to obtain energy from food (Clarke et al., 2012). Our
results showed that \emph{T. roborowskii} has more than 65\% abundance
of Firmicutes and Bacteroidetes in different seasons. Firmicutes are
responsible for the metabolic transformation of carbohydrates and
proteins (Flint et al., 2015), and can produce large amounts of
energy-rich short-chain fatty acids that are associated with digestive
efficiency (Turnbaugh et al., 2009). Bacteroidetes have the capacity to
ferment amino acids and carbohydrates and are involved in bile acid,
polysaccharide, and steroid metabolism (Rios-Covian et al., 2017). They
work together to promote fat accumulation (Turnbaugh et al., 2006). The
relative stability of the core flora may be favorable for maintaining a
high level of nutrient absorption capacity during the active season. On
one hand, this meets the direct cost of summer breeding input (Speakman,
2008), and on the other hand, it ensures energy accumulation before
hibernation.

The special frugivory strategy chosen by \emph{T. roborowskii} in the
long-term evolutionary process promotes seasonal changes in its gut
microbes. For example, the abundance of Proteobacteria in the autumn
group was significantly higher than in the spring group (\emph{P}
\textless{}0.05), which seems to be an effective way to cope with this
shift. Relevant studies have shown that Proteobacteria are related to a
variety of metabolic pathways and can secrete a large number of
enzymatic substances related to polysaccharide and protein metabolism,
which can effectively decompose polysaccharide and vitamin production
(Abdul Rahman et al., 2016; Colston \& Jackson, 2016), thus satisfying
the combination of \emph{Capparis spinosa} fruit and insect food and
providing the possibility for the accumulation of energy materials
before hibernation. The relative abundances of Enterobacteriaceae and
Pseudomonas in the autumn group were significantly higher than those in
the spring group, which may also be the result of adaptation to fruit
food with higher sugar content. Related studies have shown that some
Enterobacteriaceae species possess sucrose-specific phosphotransferase
systems and sugar transporters with different functions and structures
(Le Bouguenec \& Schouler, 2011). Carbohydrate metabolism is also widely
recognized as a nutritional basis for \selectlanguage{greek}γ-\selectlanguage{english}proteobacteria to colonize the
gut and maintain strains (Chang et al., 2004).

Lachnospira and Ruminococcaceae were the most abundant Firmicutes
species in the \emph{T. roborowskii} gut. Most studies have confirmed
that both have abundant genes related to polysaccharide degradation and
that their ability to utilize dietary polysaccharides is effective
(Vacca et al., 2020). The high abundance of ABC transporters is the
basis for the utilization of complex plant materials and the transport
of various degradation products (Biddle et al., 2013), which can use
lactic acid and acetate to produce short-chain fatty acids such as
butyrate through the butyryl-CoA: acetate CoA-transferase pathway (Flint
et al., 2015). The significant increase in Lachnospiriaceae (Roseburia)
in autumn may be an adaptation to the abundant heteropolysaccharides in
caper fruits (Bai et al., 2007), and may improve the efficiency of
nutrient metabolism.

Akkermansiaceae are widely present in the gut of hibernating animals
(Tang et al., 2019), and are considered probiotics with functions such
as promoting intestinal mucosal barrier repair and regulating intestinal
flora metabolism (Belzer \& De Vos, 2012). The relative abundance
of\emph{Akkermansia muciniphila} was significantly higher in the spring
group than in the autumn group. We speculate that the period
when\emph{T. roborowskii} was collected was still in the transitional
period of enterotype transition; therefore, these individuals still had
a higher abundance of Verrucobacterium. Akkermansiaceae can produce
short-chain fatty acids such as acetate and propionate by degrading
intestinal mucin (Feng et al., 2018). The production of fatty acids can
maintain the immune state of the intestine and create an anaerobic
environment required for the growth of strictly anaerobic symbiotic
microorganisms, thus establishing a mutually beneficial relationship
with the host (Shealy et al., 2021). Most Enterobacteriaceae and
Pseudomonas are facultative anaerobes, and the destruction of the
anaerobic environment leads to their rapid proliferation (Rivera-Ch\selectlanguage{ngerman}ávez
et al., 2016). Therefore, the high abundance of Verrucomicrobia in
spring limited Proteobacteria; however, the abundance of Verrucomicrobia
decreased in autumn, and some aerobic Proteobacteria dominated.

Analysis based on PCA and PLS-DA showed significant differences in fecal
metabolites among different seasons, which may be caused by differences
in seasonal diets (Li et al., 2020). Plant-derived chemicals such as
lauric acid and cinnamic acid were significantly enriched in the autumn
group because \emph{T. roborowskii} had limited ability to digest caper
seeds (Yang et al., 2021), and some plant-derived components were
retained.

Deoxycholic acid glycine conjugate and 3-oxocholic acid are secondary
bile acids. Bile acids participate in cholesterol metabolism through the
enterohepatic circulation. This plays a crucial role in the digestion
and absorption of components, regulates carbohydrate metabolism, and
effectively regulates metabolic homeostasis (Bao et al., 2016).

From the differential metabolite KEGG enrichment network map, it can be
seen that the biosynthetic pathways of pantothenate and CoA are
significantly associated with a variety of metabolites. Pantothenate and
D-4'-phosphopantothenate are precursors of pantothenate and CoA.~CoA is
involved in various biochemical reactions, including the tricarboxylic
acid cycle, fatty acid synthesis and oxidation, and amino acid
metabolism (Choudhary et al., 2014; Ma et al., 2020). The high CoA
biosynthesis in autumn suggests that \emph{T. roborowskii} has a
vigorous metabolic level, which is consistent with our speculation.
L-piperic acid, 5-aminopentanoicacid, and D-1-piperideine-2-carboxylic
acid were higher in the spring group, demonstrating that lysine was
degraded through the pipecolic acid pathway. Related studies have shown
that \selectlanguage{english}L-lysine can improve the absorption and utilization of food
proteins (Hallen et al., 2013). This appears to explain the adjustment
of the Turpan wonder geckos in spring to cope with a protein-rich,
insect-based diet.

Correlation analysis between metabolite clusters and gut microbes
revealed an interesting phenomenon: Verrucomicrobia and Tenericutes were
more abundant in spring, whereas Proteobacteria and Elusimicrobia were
more abundant in autumn. These have the same correlation with certain
metabolite clusters, which means that the gut microbiota in different
seasons performs specific metabolic functions, so the gut microbiota
of\emph{T. roborowskii} shows obvious seasonal patterns with changes in
the seasonal diet.

\textbf{Conclusion}

In summary, there were seasonal changes in the gut microbes of \emph{T.
roborowskii} , with higher diversity and relative abundance in autumn
than in spring. Firmicutes and Bacteroidetes had a stable proportion,
Proteobacteria and the significant growth of Lachnospiriaceae in autumn
may be an adaptive factor to cope with seasonal dietary shifts.
Secondary bile acids, such as deoxycholate-glycine conjugates, showed
higher levels in different seasons, suggesting their important role in
metabolism. Picrust functional predictions showed that
metabolism-related pathways varied in different seasons, and the core
microbiota in different seasons appeared to perform functions associated
with specific metabolic pathways. In the future, captive breeding and
artificial manipulation of the food ratio will be an effective way to
further understand seasonal dietary shifts and the effects of frugivory
strategies on the gut microbiota and metabolic functions of lizards such
as \emph{T. roborowskii} .

\textbf{Authors' contributions}

ZG, YY, and LS conceived the ideas and designed methodology, W-ZG and LS
do field work. W-ZG and YY collected the data. W-ZG analyzed the data.
All authors contributed to the article and approved the submitted
version.\textbf{Acknowledgments}

We thank Zhiyuan Yin, Peng Ding, Xi Chen and Pengying Niao for their
fifieldwork help. We would like to thank Editage (www.editage.cn) for
English language editing.

\textbf{Ethics approval and consent to participate}

The authors declare that this article is reported in accordance with the
ARRIVE guidelines 2.0. All experimental procedures involving animals
were approved ( animal protocol number: 2021079) by the Animal Welfare
and Ethics Committee of Xinjiang Agricultural University, Urumqi,
Xinjiang, China.

\textbf{Conflict of interest}

The authors declare that they have no competing interests.

\textbf{Availability of data and materials}

The data that support the findings of this study are openly available in
National Center for Biotechnology Information (NCBI) Sequence Read
Archive (SRA) at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA887219,
reference number {[}PRJNA887219{]}.

\textbf{Funding}

We grateful acknowledge funding from the National Natural Science
Foundation of China (32260527, 32260118, 31260511), Innovative Team
Foundation of Biology of Xinjiang Agricultural University, China
(ITB202101).

\textbf{References}

Abdul Rahman, N., Parks, D. H., Vanwonterghem, I., Morrison, M., Tyson,
G. W., \& Hugenholtz, P. (2016). A phylogenomic analysis of the
bacterial phylum Fibrobacteres. Frontiers in microbiology, 6, 1469.

Amato, K. R., Leigh, S. R., Kent, A., Mackie, R. I., Yeoman, C. J.,
Stumpf, R. M., Wilson, B. A., Nelson, K. E., White, B. A., \& Garber, P.
A. (2015). The gut microbiota appears to compensate for seasonal diet
variation in the wild black howler monkey (\emph{Alouatta pigra} ).
Microbial ecology, 69(2), 434-443.

Anderson, M. J. (2001). A new method for non-parametric multivariate
analysis of variance. Austral ecology, 26(1), 32-46.

Bai, H., Zhao, X., Ma, L., \& Xi, L. (2007). Determination on the
content of nutritional ingredient in the fruit of \emph{Capparis
spinosa} . Acta Agriculturae Boreali-occidentalis Sinica, 16(5):
211-213, 226.

Bao, Q., Shen, X., Qian, L., Gong, C., Nie, M., \& Dong, Y. (2016).
Anti-diabetic activities of catalpol in db/db mice. The Korean Journal
of Physiology \& Pharmacology, 20(2), 153-160.

Belzer, C., \& De Vos, W. M. (2012). Microbes inside---from diversity to
function: the case of \emph{Akkermansia} . The ISME journal, 6(8),
1449-1458.

Biddle, A., Stewart, L., Blanchard, J., \& Leschine, S. (2013).
Untangling the genetic basis of fibrolytic specialization by
Lachnospiraceae and Ruminococcaceae in diverse gut communities.
Diversity, 5(3), 627-640.

Campos, P., Guivernau, M., Prenafeta-Bold\selectlanguage{ngerman}ú, F. X., \& Cardona, L.
(2018). Fast acquisition of a polysaccharide fermenting gut microbiome
by juvenile green turtles \emph{Chelonia mydas} after settlement in
coastal habitats. Microbiome, 6(1), 1-11.

Chang, D.-E., Smalley, D. J., Tucker, D. L., Leatham, M. P., Norris, W.
E., Stevenson, S. J., Anderson, A. B., Grissom, J. E., Laux, D. C., \&
Cohen, P. S. (2004). Carbon nutrition of Escherichia coli in the mouse
intestine. Proceedings of the National Academy of Sciences, 101(19),
7427-7432.

Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E., \& Mann, M.
(2014). The growing landscape of lysine acetylation links metabolism and
cell signalling. Nature reviews Molecular cell biology, 15(8), 536-550.

Clarke, S. F., Murphy, E. F., Nilaweera, K., Ross, P. R., Shanahan, F.,
O'Toole, P. W., \& Cotter, P. D. (2012). The gut microbiota and its
relationship to diet and obesity: new insights. Gut microbes, 3(3),
186-202.

Colston, T. J., \& Jackson, C. R. (2016). Microbiome evolution along
divergent branches of the vertebrate tree of life: what is known and
unknown. Molecular ecology, 25(16), 3776-3800.

Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from
microbial amplicon reads. Nature methods, 10(10), 996-998.

Egerton, S., Culloty, S., Whooley, J., Stanton, C., \& Ross, R. P.
(2018). The gut microbiota of marine fish. Frontiers in microbiology, 9,
873.

Fan, C., Zhang, L., Jia, S., Tang, X., Fu, H., Li, W., Liu, C., Zhang,
H., Cheng, Q., \& Zhang, Y. (2022). Seasonal variations in the
composition and functional profiles of gut microbiota reflect dietary
changes in plateau pikas. Integrative Zoology, 17(3), 379-395.

Feng, W., Ao, H., \& Peng, C. (2018). Gut microbiota, short-chain fatty
acids, and herbal medicines. Frontiers in pharmacology, 9, 1354.

Flint, H. J., Duncan, S. H., Scott, K. P., \& Louis, P. (2015). Links
between diet, gut microbiota composition and gut metabolism. Proceedings
of the Nutrition Society, 74(1), 13-22.

Gomez, A., Rothman, J. M., Petrzelkova, K., Yeoman, C. J., Vlckova, K.,
Umaña, J. D., Carr, M., Modry, D., Todd, A., \& Torralba, M. (2016).
Temporal variation selects for diet--microbe co-metabolic traits in the
gut of \emph{Gorilla spp} . The ISME journal, 10(2), 514-526.

Gong, L., Liu, B., Wu, H., Feng, J., \& Jiang, T. (2021). Seasonal
Dietary Shifts Alter the Gut Microbiota of Avivorous Bats: Implication
for Adaptation to Energy Harvest and Nutritional Utilization. Msphere,
6(4), e00467-00421.

Grond, K., Sandercock, B. K., Jumpponen, A., \& Zeglin, L. H. (2018).
The avian gut microbiota: community, physiology and function in wild
birds. Journal of Avian Biology, 49(11), e01788.

Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C., \& Kueffer,
C. (2014). Unifying niche shift studies: insights from biological
invasions. Trends in ecology \& evolution, 29(5), 260-269.

Hale, V. L., Tan, C. L., Niu, K., Yang, Y., Zhang, Q., Knight, R., \&
Amato, K. R. (2019). Gut microbiota in wild and captive Guizhou
snub\selectlanguage{english}-nosed monkeys, \emph{Rhinopithecus brelichi} . American Journal of
Primatology, 81(10-11), e22989.

Hallen, A., Jamie, J. F., \& Cooper, A. J. (2013). Lysine metabolism in
mammalian brain: an update on the importance of recent discoveries.
Amino acids, 45(6), 1249-1272.

Hua, Y., Cao, H., Wang, J., He, F., \& Jiang, G. (2020). Gut microbiota
and fecal metabolites in captive and wild North China leopard
(\emph{Panthera pardus japonensis} ) by comparsion using 16 s rRNA gene
sequencing and LC/MS-based metabolomics. BMC veterinary research, 16(1),
1-11.

Huang, C., \& Liao, W. (2021). Seasonal variation in gut microbiota
related to diet in \emph{Fejervarya limnocharis} . Animals, 11(5), 1393.

Jiang, H.-Y., Ma, J.-E., Li, J., Zhang, X.-J., Li, L.-M., He, N., Liu,
H.-Y., Luo, S.-Y., Wu, Z.-J., \& Han, R.-C. (2017). Diets alter the gut
microbiome of crocodile lizards. Frontiers in microbiology, 8, 2073.

Kaiser, N., Cerasi, E., \& Leibowitz, G. (2012). Diet-induced diabetes
in the sand rat (\emph{Psammomys obesus} ). Animal Models in Diabetes
Research, 89-102.

Kanehisa, M. (2019). Toward understanding the origin and evolution of
cellular organisms. Protein Science, 28(11), 1947-1951.

Kartzinel, T. R., Hsing, J. C., Musili, P. M., Brown, B. R., \& Pringle,
R. M. (2019). Covariation of diet and gut microbiome in African
megafauna. Proceedings of the National Academy of Sciences, 116(47),
23588-23593.

Kim, M., \& Benayoun, B. A. (2020). The microbiome: an emerging key
player in aging and longevity. Translational medicine of aging, 4,
103-116.

Kohl, K. D., Amaya, J., Passement, C. A., Dearing, M. D., \& McCue, M.
D. (2014). Unique and shared responses of the gut microbiota to
prolonged fasting: a comparative study across five classes of vertebrate
hosts. FEMS microbiology ecology, 90(3), 883-894.

Kohl, K. D., Brun, A., Magallanes, M., Brinkerhoff, J., Laspiur, A.,
Acosta, J. C., Bordenstein, S. R., \& Caviedes-Vidal, E. (2016).
Physiological and microbial adjustments to diet quality permit
facultative herbivory in an omnivorous lizard. Journal of Experimental
Biology, 219(12), 1903-1912.

Kohl, K. D., Cary, T. L., Karasov, W. H., \& Dearing, M. D. (2013).
Restructuring of the amphibian gut microbiota through metamorphosis.
Environmental microbiology reports, 5(6), 899-903.

Le Bouguenec, C., \& Schouler, C. (2011). Sugar metabolism, an
additional virulence factor in enterobacteria. International Journal of
Medical Microbiology, 301(1), 1-6.

Li, H., Yu, Q., Li, T., Shao, L., Su, M., Zhou, H., \& Qu, J. (2020).
Rumen microbiome and metabolome of Tibetan Sheep (\emph{Ovis aries} )
reflect animal age and nutritional requirement. Frontiers in Veterinary
Science, 7, 609.

Li, W., Song, Y., \& Shi, L. (2010). Age determination
of\emph{Teratoscincus roborowskii} (Gekkonidae) by skeletochronology.
Chin J Zool, 45, 79-86.

Liu, P.Y., Cheng, A.C., Huang, S.W., Chang, H.W., Oshida, T., \& Yu,
H.T. (2019). Variations in gut microbiota of Siberian flying squirrels
correspond to seasonal phenological changes in their Hokkaido subarctic
forest ecosystem. Microbial ecology, 78(1), 223-231.

Liu, Y., Song, Y., Li, W., \& Shi, L. (2010). Sexual dimorphism in head
and body size of \emph{Teratoscincus roborowskii} and its food habits in
different seasons. Chin J Ecol, 29(2), 333-338.

Ma, T., Liu, T., Xie, P., Jiang, S., Yi, W., Dai, P., \& Guo, X. (2020).
UPLC-MS-based urine nontargeted metabolic profiling identifies
dysregulation of pantothenate and CoA biosynthesis pathway in diabetic
kidney disease. Life Sciences, 258, 118160.

MacLeod, K. J., Kohl, K. D., Trevelline, B. K., \& Langkilde, T. (2022).
Context\selectlanguage{english}-dependent effects of glucocorticoids on the lizard gut
microbiome. Molecular ecology, 31(1), 185-196.

Marshall, T. J., Dick, M. F., \& Guglielmo, C. G. (2016). Seasonal
dietary shifting in yellow-rumped warblers is unrelated to macronutrient
targets. Comparative Biochemistry and Physiology Part A: Molecular \&
Integrative Physiology, 192, 57-63.

Maurice, C. F., CL Knowles, S., Ladau, J., Pollard, K. S., Fenton, A.,
Pedersen, A. B., \& Turnbaugh, P. J. (2015). Marked seasonal variation
in the wild mouse gut microbiota. The ISME journal, 9(11), 2423-2434.

McFall-Ngai, M., Hadfield, M. G., Bosch, T. C., Carey, H. V.,
Domazet-Lo\selectlanguage{polish}š\selectlanguage{english}o, T., Douglas, A. E., Dubilier, N., Eberl, G., Fukami, T.,
\& Gilbert, S. F. (2013). Animals in a bacterial world, a new imperative
for the life sciences. Proceedings of the National Academy of Sciences,
110(9), 3229-3236.

Moeller, A. H., Ivey, K., Cornwall, M. B., Herr, K., Rede, J., Taylor,
E. N., \& Gunderson, A. R. (2020). The lizard gut microbiome changes
with temperature and is associated with heat tolerance. Applied and
environmental microbiology, 86(17), e01181-01120.

Montoya-Ciriaco, N., G\selectlanguage{ngerman}ómez-Acata, S., Muñoz-Arenas, L. C., Dendooven,
L., Estrada-Torres, A., Díaz de la Vega-Pérez, A. H., \& Navarro-Noya,
Y. E. (2020). Dietary effects on gut microbiota of the mesquite
lizard\emph{Sceloporus grammicus} (Wiegmann, 1828) across different
altitudes. Microbiome, 8(1), 1-19.

Naya, D. E., Veloso, C., Sabat, P., \& Bozinovic, F. (2010). Seasonal
flexibility in organ size in the Andean lizard \emph{Liolaemus
moradoensis} . Journal of Morphology, 271(12), 1440-1445.

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P.,
O'hara, R., Simpson, G., Solymos, P., Stevens, M. H. H., \& Wagner, H.
(2013). Community ecology package. R package version, 2(0), 321-326.

Pianka, E. R., \& Vitt, L. J. (2003). Lizards: windows to the evolution
of diversity (Vol. 5). Univ of California Press.

Prehn-Kristensen, A., Zimmermann, A., Tittmann, L., Lieb, W., Schreiber,
S., Baving, L., \& Fischer, A. (2018). Reduced microbiome alpha
diversity in young patients with ADHD. Plos one, 13(7), e0200728.

Rios-Covian, D., Salazar, N., Gueimonde, M., \& de Los Reyes-Gavilan, C.
G. (2017). Shaping the metabolism of intestinal Bacteroides population
through diet to improve human health. Frontiers in microbiology, 8, 376.

Rivera-Chávez, F., Lopez, C. A., Zhang, L. F., García-Pastor, L.,
Chávez-Arroyo, A., Lokken, K. L., Tsolis, R. M., Winter, S. E., \&
Bäumler, A. J. (2016). Energy taxis toward host-derived nitrate supports
a \emph{Salmonella} pathogenicity island 1-independent mechanism of
invasion. MBio, 7(4), e00960-00916.

Robbins, C. T., Lopez-Alfaro, C., Rode, K. D., Tøien, Ø., \& Nelson, O.
L. (2012). Hibernation and seasonal fasting in bears: the energetic
costs and consequences for polar bears. Journal of Mammalogy, 93(6),
1493-1503.

Schloss, P. D., Gevers, D., \& Westcott, S. L. (2011). Reducing the
effects of PCR amplification and sequencing artifacts on 16S rRNA-based
studies. Plos one, 6(12), e27310.

Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett,
W. S., \& Huttenhower, C. (2011). Metagenomic biomarker discovery and
explanation. Genome biology, 12(6), 1-18.

Shealy, N. G., Yoo, W., \& Byndloss, M. X. (2021). Colonization
resistance: metabolic warfare as a strategy against pathogenic
Enterobacteriaceae. Current Opinion in Microbiology, 64, 82-90.

Shi, L., Yang, J., Hou, M., Zhao, H., Dong, B., Xiong, J., Wang, X., \&
Wang, X. (2007). Herpetological surveys of Xinjiang Uygur autonomous
region. Sichuan Journal of Zoology, 26(4), 812-818.

Shi, L., Zhou, Y., \& Yuan, H. (2002). Reptile fauna and zoogeographic
division of Xinjiang Uygur Autonomous Region. Sichuan Journal of
Zoology, 21(3), 152-157.

Sommer, F., \& Bäckhed, F. (2013). The gut microbiota---masters of host
development and physiology. Nature reviews microbiology, 11(4), 227-238.

Sommer, F., Ståhlman, M., Ilkayeva, O., Arnemo, J. M., Kindberg, J.,
Josefsson, J., Newgard, C. B., Fröbert, O., \& Bäckhed, F. (2016). The
gut microbiota modulates energy metabolism in the hibernating brown
bear\emph{Ursus arctos} . Cell reports, 14(7), 1655-1661.

Song, Y., Liu, Y., Lin, Y., Liang, T., \& Shi, L. (2017). Burrow
characteristics and microhabitat use of the Turpan Wonder
Gecko\emph{Teratoscincus roborowskii} (Squamata, Gekkonidae). Asian
Herpetol Res, 8, 61-69.

Song, Y., Zhao, H., \& Shi, L. (2009). Daily activity rhythm and its
affecting environmental factors of \emph{Teratoscincus roborowskii} . J
Xinj Agri Univ, 32, 22-25.

Soriano, E. L., Ramírez, D. T., Araujo, D. R., Gómez-Gil, B., Castro, L.
I., \& Sánchez, C. G. (2018). Effect of temperature and dietary lipid
proportion on gut microbiota in yellowtail kingfish \emph{Seriola
lalandi juveniles} . Aquaculture, 497, 269-277.

Speakman, J. R. (2008). The physiological costs of reproduction in small
mammals. Philosophical Transactions of the Royal Society B: Biological
Sciences, 363(1490), 375-398.

Suzuki, T. A., \& Worobey, M. (2014). Geographical variation of human
gut microbial composition. Biology letters, 10(2), 20131037.

Tang, G.-S., Liang, X.-X., Yang, M.-Y., Wang, T.-T., Chen, J.-P., Du,
W.-G., Li, H., \& Sun, B.-J. (2020). Captivity influences gut microbiota
in crocodile lizards (\emph{Shinisaurus crocodilurus} ). Frontiers in
microbiology, 11, 550.

Tang, K.-Y., Wang, Z.-W., Wan, Q.-H., \& Fang, S.-G. (2019).
Metagenomics reveals seasonal functional adaptation of the gut
microbiome to host feeding and fasting in the Chinese alligator.
Frontiers in microbiology, 10, 2409.

Thévenot, E. A., Roux, A., Xu, Y., Ezan, E., \& Junot, C. (2015).
Analysis of the human adult urinary metabolome variations with age, body
mass index, and gender by implementing a comprehensive workflow for
univariate and OPLS statistical analyses. Journal of proteome research,
14(8), 3322-3335.

Tran, H., McConville, M., \& Loukopoulos, P. (2020). Metabolomics in the
study of spontaneous animal diseases. Journal of Veterinary Diagnostic
Investigation, 32(5), 635-647.

Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E.
R., \& Gordon, J. I. (2006). An obesity-associated gut microbiome with
increased capacity for energy harvest. nature, 444(7122), 1027-1031.

Turnbaugh, P. J., Ridaura, V. K., Faith, J. J., Rey, F. E., Knight, R.,
\& Gordon, J. I. (2009). The effect of diet on the human gut microbiome:
a metagenomic analysis in humanized gnotobiotic mice. Science
translational medicine, 1(6), 6ra14-16ra14.

Vacca, M., Celano, G., Calabrese, F. M., Portincasa, P., Gobbetti, M.,
\& De Angelis, M. (2020). The controversial role of human gut
lachnospiraceae. Microorganisms, 8(4), 573.

Wang, A. R., Ran, C., Ringø, E., \& Zhou, Z. G. (2018). Progress in fish
gastrointestinal microbiota research. Reviews in Aquaculture, 10(3),
626-640.

Wang, H.Y., Zhao, J.G., \& Zhang, Y.Q. (2020). The flavonoid-rich
ethanolic extract from the green cocoon shell of silkworm has excellent
antioxidation, glucosidase inhibition, and cell protective effects in
vitro. Food \& Nutrition Research, 64.

Werner, Y., Okada, S., Ota, H., Perry, G., \& Tokunaga, S. (1997).
Varied and fluctuating foraging modes in nocturnal lizards of the family
Gekkonidae. Asiatic Herpetological Research, 7, 153-165.

Wu, Q., Wang, X., Ding, Y., Hu, Y., Nie, Y., Wei, W., Ma, S., Yan, L.,
Zhu, L., \& Wei, F. (2017). Seasonal variation in nutrient utilization
shapes gut microbiome structure and function in wild giant pandas.
Proceedings of the Royal Society B: Biological Sciences, 284(1862),
20170955.

Xiao, G., Liu, S., Xiao, Y., Zhu, Y., Zhao, H., Li, A., Li, Z., \& Feng,
J. (2019). Seasonal changes in gut microbiota diversity and composition
in the greater horseshoe bat. Frontiers in microbiology, 10, 2247.

Yang, Y., Lin, Y., \& Shi, L. (2021). The effect of lizards on the
dispersal and germination of \emph{Capparis spinosa} (Capparaceae). Plos
one, 16(2), e0247585.

Zhang, W., Li, N., Tang, X., Liu, N., \& Zhao, W. (2018). Changes in
intestinal microbiota across an altitudinal gradient in the
lizard\emph{Phrynocephalus vlangalii} . Ecology and evolution, 8(9),
4695-4703.

Zhao, J., Yao, Y., Li, D., Xu, H., Wu, J., Wen, A., Xie, M., Ni, Q.,
Zhang, M., \& Peng, G. (2018). Characterization of the gut microbiota in
six geographical populations of Chinese rhesus macaques (\emph{Macaca
mulatta} ), implying an adaptation to high-altitude environment.
Microbial ecology, 76(2), 565-577.

Zhou, J., Zhao, Y. T., Dai, Y. Y., Jiang, Y. J., Lin, L. H., Li, H., Li,
P., Qu, Y. F., \& Ji, X. (2020). Captivity affects diversity, abundance,
and functional pathways of gut microbiota in the northern grass
lizard\emph{``} . MicrobiologyOpen, 9(9), e1095.

Zhou, L., Liang, T., \& Shi, L. (2019). Amphibian and Reptilian
Chorotypes in the Arid Land of Central Asia and Their Determinants.
Scientific reports, 9(1), 1-13.

Zierer, J., Jackson, M. A., Kastenmüller, G., Mangino, M., Long, T.,
Telenti, A., Mohney, R. P., Small, K. S., Bell, J. T., \& Steves, C. J.
(2018). The fecal metabolome as a functional readout of the gut
microbiome. Nature genetics, 50(6), 790-795.

\par\null

\textbf{Figures and legends}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} Bar diagram (A) and Venn plot (B) of gut microbiota
OTU from \emph{Teratoscincus roborowskii.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2} Alpha diversity of gut microbiota
from\emph{Teratoscincus roborowskii} .~(A) ACE index at Phylum level;
(B) Shannon index at Phylum level (C) ACE index at Family level; (D)
Shannon index at Family level. Significant differences between AG and SG
groups were tested by Wilcoxon rank-sum test (*, \emph{P}
\textless{}0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} Beta diversity analysis of gut microbiota
from\emph{Teratoscincus roborowskii} according to principal coordinates
analysis (PCoA) (A) and nonmetric multidimensional scaling (NMDS) (B).
(A) Bray-Curtis distance matrix PCoA of the gut microbiota in the SG and
AG. The boxplot shows the discrete distribution of samples along the PC1
and PC2 axes. (B) nonmetric multidimensional scaling analysis (NMDS) of
the binary Jaccard distance of the two groups. The \emph{P} -value was
based on ANOSIM.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} The relative abundance of gut microbiota at the phylum
(A), family (B), and genus (C) levels in \emph{Teratoscincus
roborowskii.} The histogram shows the top 10 bacterial phyla, families
and genera with relative abundance. The vertical coordinates of the
figure indicate the relative abundance of bacterial species and the
species names indicated by letters in the figure are shown in the legend
on the right.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5} Linear discriminant analysis effect size (LEfSe)
analysis of gut microbiota composition between seasons
for\emph{Teratoscincus roborowskii} (LDA\textgreater{}4,\emph{P}
\textless{}0.05). Red boxes and green boxes represent enrichment in the
AG (autumn) and SG (spring) groups. Circles radiating from the inside to
the outside represent taxonomic levels from phylum to species. Each
circle's diameter is proportional to the taxon's abundance. The letters
p, o, f, g, and s represent phylum, order, family, genus, and species,
respectively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6} Differential analysis of metabolic functional pathways
predicted by KEGG in the AG and SG groups. Significantly changed
pathways of the AG and SG at the secondary(A) and tertiary (B) levels
(metabolism pathways). The vertical axis represents different pathways,
and the horizontal axis represents the proportions of corresponding
pathways (\emph{P} \textless{}0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7} Principal component analysis (PCA)(A) and partial
least-squares discrimination analysis (B) of fecal metabolites
in\emph{Teratoscincus roborowskii.} AG1-4 are fecal metabolites
of\emph{Teratoscincus roborowskii} from autumn group. SG1-4 are fecal
metabolites of \emph{Teratoscincus roborowskii} from spring group.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 8} Fold change analysis of fecal metabolite differences
based on log2FC size ordering. The figure shows the top 10 metabolites
of up and down logFC, and the labels of each column indicate the name of
the metabolite.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 9} Volcano plot for statistical analysis of fecal
differential metabolites. Note: Each point in the volcano plot
represents a metabolite, the abscissa represents the multiple change of
the group compared with each substance, the ordinate represents
the\emph{P} -value of the t-test , and the size of the scatter
represents the VIP value of the OPLS-DA model. The blue dots in the
figure represent the down regulated differential expression metabolites,
the red dots represent the up regulated differential expression
metabolites, and the gray dots represent the detected but not
significantly different metabolites. In addition, the top 5 qualitative
metabolites are selected and marked in the figure after sorting by
\emph{P} value.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 10} Enrichment of functional pathways corresponding to
differential metabolites. Note: The light yellow node in the figure is
the pathway, and the small node connected with it is the specific
metabolite annotated to the pathway.The value of log2 in the difference
multiple determines the color depth.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{Figure 11} Heat map of the correlation between metabolite
clusters and gut microbiota in \emph{Teratoscincus roborowskii.} Note:
Different colors represent the size of the Pearson correlation
coefficient. Red indicates a positive correlation, whereas green
indicates a negative correlation. Asterisks indicate significant
correlations between metabolite clusters and microorganisms (\emph{P}
\textless{}0.05); *, **, and *** indicate\emph{P} \textless{}0.05,
\emph{P} \textless{}0.01, and\emph{P} \textless{}0.001, respectively.\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-1A/Figure-1A}

\end{center}
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