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\begin{document}

\title{Exploring gut microbiota diversity in Catharsius molossus: influence of
dietary conditions on ecosystem functionality}



\author[1]{Yue Mao}%
\author[2]{Xingjian   Yang}%
\author[3]{Hui  Yuan}%
\author[1]{Tao Xiong}%
\affil[1]{Yangtze University College of Life Science}%
\affil[2]{Grinnell College}%
\affil[3]{Yangtze University}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Dung beetle serve as valuable indicators for studying environmental
changes and as model systems for exploring ecosystem functionality. By
analyzing the diversity and composition of gut microbiota in Catharsius
molossus under starvation and refeeding conditions, this study
investigates the effects of dietary states on the gut microbiota of
these insects. Artificial rearing methods, along with 16S rRNA
high-throughput sequencing and bioinformatics, were used to analyze
Catharsius molossus gut microbiota under varying dietary conditions. The
results indicate that at the phylum and genus levels, the gut microbiota
of Catharsius molossus under refeeding conditions is more diverse than
that under starvation conditions, with seven phyla and twenty-two genera
showing significant differences (P \textless{} 0.05). In terms of
functional prediction, the predicted functional genes of the gut
microbiota were annotated to the KEGG database, revealing significant
differences in thirty-two metabolic pathways at the third level (P
\textless{} 0.05). Furthermore, it provides functional prediction
information related to specific microbial taxa. Additionally,
Dysgonomonas is speculated to participate in nitrogen fixation, and the
gut microbiota of Catharsius molossus may potentially serve as a source
of antimicrobial agents like anshanmycin. These findings provide novel
insights into Coleoptera ecosystem microbial interactions and offer
theoretical support for future applications.%
\end{abstract}%



\sloppy


\textbf{Article title}

Exploring gut microbiota diversity in \emph{Catharsius molossus} :
influence of dietary conditions on ecosystem functionality

\textbf{2. Names of all authors}

Yue Mao\textsuperscript{1}, Xingjian Yang\textsuperscript{2}, Hui
Yuan\textsuperscript{1}, Tao Xiong\textsuperscript{1*}

\textbf{3.Affiliation of all authors}

\textsuperscript{1}Department of Life Science, Yangtze University,
Jingzhou 434000, Hubei Province, China;

\textsuperscript{2}Department of Biology, Grinnell College, Grinnell
50112, IA, USA

\textbf{4. Postal and E-mail address of the corresponding author}

Corresponding author: Tao Xiong. E-mail: xiongtao@hotmail.com

Exploring gut microbiota diversity in \emph{Catharsius molossus} :
influence of dietary conditions on ecosystem functionality

Yue Mao\textsuperscript{1}, Xingjian Yang\textsuperscript{2}, Hui
Yuan\textsuperscript{1}, Tao Xiong\textsuperscript{1}*

\textsuperscript{1}Department of Life Science, Yangtze University,
Jingzhou 434000, Hubei Province, China;

\textsuperscript{2}Department of Biology, Grinnell College, Grinnell
50112, IA, USA

*Corresponding author:

Tao Xiong,

Department of Life Science, Yangtze University, Jingzhou 434000, Hubei
Province;

E-mail address: xiongtao@hotmail.com

\textbf{Abstract:} Dung beetle serve as valuable indicators for studying
environmental changes and as model systems for exploring ecosystem
functionality. By analyzing the diversity and composition of gut
microbiota in \emph{Catharsius molossus} under starvation and refeeding
conditions, this study investigates the effects of dietary states on the
gut microbiota of these insects. Artificial rearing methods, along with
16S rRNA high-throughput sequencing and bioinformatics, were used to
analyze \emph{Catharsius molossus} gut microbiota under varying dietary
conditions. The results indicate that at the phylum and genus levels,
the gut microbiota of \emph{Catharsius molossus} under refeeding
conditions is more diverse than that under starvation conditions, with
seven phyla and twenty-two genera showing significant differences
(\emph{P} \textless{} 0.05). In terms of functional prediction, the
predicted functional genes of the gut microbiota were annotated to the
KEGG database, revealing significant differences in thirty-two metabolic
pathways at the third level (\emph{P} \textless{} 0.05). Furthermore, it
provides functional prediction information related to specific microbial
taxa. Additionally, \emph{Dysgonomonas} is speculated to participate in
nitrogen fixation, and the gut microbiota of \emph{Catharsius
molossus}may potentially serve as a source of antimicrobial agents like
anshanmycin. These findings provide novel insights into Coleoptera
ecosystem microbial interactions and offer theoretical support for
future applications.

\textbf{Keywords} : \emph{Catharsius molossus} ; gut microbiota; 16S
rRNA high-throughput sequencing; functional prediction; nitrogen
fixation

\section*{1. INTRODUCTION}

{\label{introduction}}

There are over twenty thousand species of beetles worldwide, distributed
across all continents except Antarctica. Notably, beetles play a crucial
role in ecosystems. They have the ability to disrupt the breeding
habitats of pests and parasites harmful to livestock(deCastro-Arrazola
et al., 2023; Gregory et al., 2015), thereby facilitating the spread of
human pathogens(Nichols \& G\selectlanguage{ngerman}ómez, 2014), Additionally, they accelerate
the return of nutrients from feces to soil(Sitters et al., 2014),
contributing to improved soil aeration, water retention, and
fertility(Kirsch, 2011). Moreover, beetles promote plant
growth(deCastro-Arrazola et al., 2020), assist in seed
dispersal(Pedersen \& Blüthgen, 2022), and even provide essential
pollination services(Pokhrel et al., 2021; Sakai \& Inoue, 1999).

The microbial community within the digestive tract of insects, also
known as gut microbiota, comprises protozoa, fungi, archaea, and
bacteria(Rozadilla et al., 2020). This diverse consortium plays a
crucial role in providing essential nutrients to the host, aiding in
efficient food digestion, enhancing host defense mechanisms, and
enabling detoxification processes(Kaltenpoth, 2020). Moreover, it exerts
a significant influence on various aspects of the host insect's life,
including its lifespan, developmental cycle, mating behaviors, and
reproductive capacities(Dong et al., 2021). Despite its importance,
research into the gut microbiota of beetles is currently in its early
exploratory stages. However, several studies have already unveiled
noteworthy findings. For instance, Franzini et al. conducted a
comparison of the gut microbiota among two distinct small desert beetle
species, revealing intriguing intra-species variations(Franzini et al.,
2016). In a separate investigation by Suarez-Moo et al., greater
similarity in gut microbiota was observed between the larvae
of\emph{Copris incertus Say} and the female adults, suggesting a
potential mode of vertical transmission that shapes the composition and
diversity of offspring gut microbiota(Suárez-Moo et al., 2020).
Furthermore, a novel yeast species named \emph{Trichosporon
heliocopridis} , extracted from the gut of \emph{Heliocopris bucephalus
Fabricius} , has exhibited the remarkable ability to assimilate various
carbon sources while withstanding environmental stressors such as high
temperature, salt, sugar, and ethanol concentrations(Nwaefuna et al.,
2021). Additionally, the gut of \emph{Thorectes lusitanicus} harbors
several species belonging to the Actinobacteria phylum, known to produce
an array of secondary metabolites with antibiotic properties, thus
contributing to host protection(Kaltenpoth, 2009). It's worth noting
that symbiotic bacteria within insects also serve as a significant
source for discovering new bioactive small molecules, presenting
valuable opportunities for the identification and utilization of
beneficial natural products.

Dung beetles, exemplified by \emph{Catharsius molossus} , are a
distinctive group of coprophagous insects that confront specific
nutritional challenges within their daily dietary regime. Being reliant
on fecal matter as their primary food source, they encounter limitations
in accessing adequate nitrogen nutrition, as feces typically harbor low
nitrogen content(Madzivhe et al., 2020). Moreover, fecal matter is laden
with diverse parasites and pathogens that could potentially impact the
health of dung beetles(Woo et al., 2023). In this study, we utilized
advanced 16S rRNA high-throughput sequencing technology to extensively
scrutinize the diversity and differential composition of the gut
microbiota of\emph{Catharsius molossus} under conditions of both
starvation and refeeding. Our endeavor is centered on unraveling the
consequences of periods of food deprivation followed by subsequent
refeeding on the structural dynamics of the gut microbiota in
\emph{Catharsius molossus} . This research holds substantial promise in
enhancing our comprehension of the intricate ecological functionalities
of the gut microbiota in dung beetles, with particular emphasis on the
roles assumed by nitrogen-fixing bacteria and the prospective
development and application of antimicrobial agents. The outcomes
anticipated from this study are poised to contribute significantly to
the foundational knowledge of dung beetle ecosystems and the broader
domain of physiological ecology, thereby ushering in novel insights and
perspectives to this realm of scientific inquiry.

\section*{2. MATERIALS AND METHODS}

{\label{materials-and-methods}}

\subsection*{2.1 Animal selection}

{\label{animal-selection}}

Wild \emph{Catharsius molossus} dung beetles were captured in their
natural habitat in Xichuan County, Nanyang City, Henan Province, China,
from April 2021 to June 2021. The captured beetles were transported to
the laboratory facilities at the College of Life Sciences, Yangtze
University, for further study. The laboratory was maintained at a
constant temperature of 21°C with a soil moisture content of 26\%.
Adequate ventilation was ensured in the laboratory, and the beetles were
provided with a daily diet of fresh cow dung.

\subsection*{2.2 Experimental procedures}

{\label{experimental-procedures}}

\subsubsection*{2.2.1 Sample collection and
grouping}

{\label{sample-collection-and-grouping}}

The gut samples from dung beetles under starvation and refeeding
conditions were collected on August 15th and August 22nd, respectively.
Six samples were collected for each condition. Before collection, the
dung beetles were artificially reared for one month to stabilize their
physiological conditions. For the starvation treatment, the beetles were
observed for defecation daily until their defecation frequency
significantly decreased. Once the beetles exhibited minimal defecation,
their gut samples were collected for the starvation condition group.
Subsequently, the beetles were re-fed for seven days and their gut
samples were collected for the refeeding condition group. The collected
gut samples were placed in sterile tubes, sequentially numbered, and
stored at -80°C for further analysis (Table 1).

\subsubsection*{2.2.2 DNA extraction and Illumina NovaSeq 6000
sequencing}

{\label{dna-extraction-and-illumina-novaseq-6000-sequencing}}

DNA extraction of the entire gut microbial community genome was
performed using the NovaSeq 6,000 SP Reagent Kit (Illumina, USA). For
bacterial 16S rRNA gene V3-V4 hypervariable region amplification,
genomic DNA served as the template, and the fusion primers 341F/805R
were utilized(Tsou et al., 2020). The PCR reaction mixture consisted of
1 \selectlanguage{greek}µ\selectlanguage{english}L 10\selectlanguage{ngerman}× Toptaq Buffer, dNTPs (2.5 mM), 0.2 \selectlanguage{greek}µ\selectlanguage{english}L Primer F/R (10 \selectlanguage{greek}µ\selectlanguage{english}M), 0.2
\selectlanguage{greek}µ\selectlanguage{english}L Toptaq DNA Polymerase, 1\textasciitilde{}3 \selectlanguage{greek}µ\selectlanguage{english}L Template DNA, with the
final volume adjusted to 10 \selectlanguage{greek}µ\selectlanguage{english}L with supplementary ddH\textsubscript{2}O.
The PCR cycling program comprised an initial denaturation step at 94 \selectlanguage{ngerman}°C
for 2 min, followed by 26 cycles of denaturation at 94 °C for 30 s,
annealing at 55 °C for 30 s, extension at 72 °C for 1 min, and a final
extension at 72 °C for 10 min. Following amplification, the products
were purified as per the Agencourt AMPure XP Nucleic Acid Purification
Kit instructions. Subsequently, Illumina NovaSeq 6000 sequencing was
performed by Shanghai Tianhao Biotechnology Co., Ltd. (Shanghai, China).

\subsubsection*{2.2.3 Analysis of 16S rRNA gene sequences and
bioinformatics}

{\label{analysis-of-16s-rrna-gene-sequences-and-bioinformatics}}

We initially subjected the raw sequencing data to preprocessing steps to
ensure the accuracy of subsequent analyses. The raw sequencing data may
contain artificial additives such as adapter sequences and primers. To
eliminate potential noise from these sources, the cutadapt plugin of
QIIME2 software (v0.5.0) was employed to remove possible adapter
sequences and primers. Subsequently, the raw data subjected to filtering
were quality assessed to guarantee high data quality(Lima et al., 2021).
For obtaining high-quality sequencing data and enhancing the accuracy of
subsequent bioinformatics analyses, the DADA2 plugin of QIIME2 software
was utilized for quality filtering, denoising, merging, and chimera
removal, thereby generating Amplicon Sequence Variants (ASVs) to assess
sample diversity(Liu et al., 2023). To evaluate the adequacy of
sequencing depth for each sample, dilution curves were constructed using
QIIME2 and the ggplot2 package (v3.3.0) in R, assessing sequencing
saturation and determining the need for further sequencing. Furthermore,
Mothur software (v1.41.1) was employed for taxonomic annotation, species
composition was assessed, and Alpha and Beta diversity analyses were
conducted using R software(Langbo et al., 2017). Leveraging amplicon
sequencing data, PICRUSt2 analysis was employed for predictive and
analytical functional profiling of microbial communities(C. Yang et al.,
2023). Employing Wilcoxon rank-sum tests and Metastats analysis, a
significance threshold of p-value \textless{}0.05 was used for
differential significance screening, with the Bonferroni correction
applied to the p-values to minimize the potential for false
positives(Feng et al., 2023; Neuhäuser, 2015). This methodology was
adopted to assess whether significant interspecies diversity exists
among groups.

\section*{3. RESULTS}

{\label{results}}

\subsection*{3.1 Sequencing quality and ASV
analysis}

{\label{sequencing-quality-and-asv-analysis}}

In this study, the composition of gut microbiota in \emph{Catharsius
molossus} under starvation and refeeding conditions was analyzed. A
total of 12 samples were sequenced, yielding a range of 49,222 to 69,752
sequences after removing chimeric sequences. The average number of valid
sequences per sample was 66,620. The hungry group exhibited 1,289 ASVs,
while the refed group displayed 2,847 ASVs. There were 419 common ASVs
between the two groups, with 870 ASVs exclusive to the hungry group and
2,428 ASVs exclusive to the refed group (Fig. 1A). This indicates shared
bacteria within the two gut microbiota groups while also highlighting
their distinctive microbial communities. The sparse curve analysis
showed a plateauing trend with increasing extracted sequence counts,
indicating that the sequencing data volume and depth were appropriately
set for the samples (Fig. 1B).

\subsection*{3.2 Composition and differential analysis of two gut
microbiota}

{\label{composition-and-differential-analysis-of-two-gut-microbiota}}

Through ASV annotation, a total of 26 phyla, 45 classes, 68 orders, 177
families, and 399 genera were identified across the two gut microbiota.
At the phylum level (abundance \textgreater{} 1\%), the predominant
phyla in \emph{Catharsius molossus} under starvation conditions were
Proteobacteria (51.93\%), Firmicutes (33.11\%), Actinobacteria (5.65\%),
Bacteroidetes (5.63\%), Others (2.66\%), and Chloroflexi (1.05\%), with
the highest abundance observed in Proteobacteria (Fig. 2A). In contrast,
the predominant phyla in \emph{Catharsius molossus} under refed
conditions were Firmicutes (44.88\%), Proteobacteria (19.73\%),
Bacteroidetes (15.16\%), Actinobacteria (4.78\%), Synergistetes
(4.15\%), Candidatus\_Saccharibacteria (3.04\%), Planctomycetes
(2.31\%), Others (2.18\%), No\_Rank (1.49\%), Acidobacteria (1.21\%),
and Chloroflexi (1.08\%), with the highest abundance observed in
Firmicutes. This reveals distinct predominant phyla between the two
conditions, with the gut microbiota of refed beetles displaying higher
phylum-level diversity (Fig. 2B).

At the genus level, the predominant genera in \emph{Catharsius
molossus}under starvation conditions were Unassigned (51.02\%), Others
(15.42\%),\emph{Vagococcus} (15.11\%), No\_Rank (8.93\%),
\emph{Dysgonomonas}(2.66\%), \emph{Sphingobacterium} (1.71\%),
\emph{Gordonia} (1.68\%),\emph{Acinetobacter} (1.26\%), and
\emph{Paracoccus} (1.13\%), with the highest classified and abundant
genus being \emph{Vagococcus} . In contrast, the predominant genera in
\emph{Catharsius molossus} under refed conditions were Others (25.96\%),
\emph{Romboutsia} (13.81\%), No\_Rank (12.24\%), Unassigned (10.09\%),
\emph{Clostridium\_XI}(7.67\%), \emph{Proteiniphilum} (6.33\%),
\emph{Cloacibacillus}(3.83\%), \emph{Clostridium\_sensu\_stricto}
(3.75\%), Saccharibacteria\_genera\_incertae\_sedis (3.04\%),
Turicibacter (2.69\%), Lysinibacillus (1.9\%), \emph{Corynebacterium}
(1.62\%),\emph{Luteimonas} (1.38\%), \emph{Hydrogenophaga}
(1.22\%),\emph{Ercella} (1.22\%), \emph{Dysgonomonas} (1.08\%),
\emph{Serpens}(1\%), \emph{Anaerovorax} (1\%), with \emph{Romboutsia}
being the highest classified and abundant genus. This indicates higher
genus-level diversity in the gut microbiota of refed beetles, with
different dominant genera observed between the two conditions (Fig. 2C).

Based on Metastats analysis, comparisons of phylum-level classifications
between the starvation and refed gut microbiota of \emph{Catharsius
molossus} revealed significant differences (\emph{P} \textless{} 0.05)
in Hydrogenedentes, Proteobacteria, Synergistetes, Planctomycetes,
Verrucomicrobia, Fusobacteria, and Bacteroidetes (Table 2). At the genus
level, 22 genera exhibited significant differences (\emph{P} \textless{}
0.05) (Table 3).

\subsection*{3.3 Alpha diversity and differential analysis of two gut
microbiota}

{\label{alpha-diversity-and-differential-analysis-of-two-gut-microbiota}}

Alpha diversity analysis was conducted based on the Wilcoxon rank-sum
test to compare the richness and diversity indices of the gut microbiota
between the starvation and refed states of \emph{Catharsius molossus} .
The Chao1 and ACE indices, representing species richness(M. Yang et al.,
2022), were found to be significantly higher in the refed group than in
the starvation group (\emph{P} \textless{} 0.05), indicating a greater
species diversity in the refed microbiota. Additionally, the Shannon
index, reflecting microbial diversity(Zhang et al., 2022), was also
higher in the refed group, while the Simpson index, indicating lower
microbial diversity, was lower in the refed group (\emph{P} \textless{}
0.05). These results collectively suggest that the gut microbiota of the
refed group exhibited both higher richness and diversity compared to the
starvation group, underscoring significant differences between the two
feeding conditions (Table 4).

\subsection*{3.4 Beta diversity and differential analysis of the two gut
microbiota}

{\label{beta-diversity-and-differential-analysis-of-the-two-gut-microbiota}}

Principal Coordinate Analysis (PCoA) was employed to evaluate the
similarity in the structure of gut microbiota between the starved and
refed groups(Song et al., 2021). The PCoA plot visualized the primary
sources of dissimilarity among the samples along the horizontal (Axis 1)
and vertical (Axis 2) axes, which accounted for the most significant
variations. Distinct colors on the PCoA plot represented different
groups, with shorter distances indicating greater similarity and reduced
dissimilarity in microbial structures between paired samples. Employing
unweighted UniFrac distances, the PCoA analysis revealed that Axis 1
contributed to 27.89\% of the variance, while Axis 2 contributed to
15.47\% (Fig. 3). Importantly, the gut microbiota of \emph{Catharsius
molossus} exhibited clustering patterns corresponding to their feeding
conditions. Notably, the starvation group displayed more scattered
microbial compositions among samples, whereas the refed group
demonstrated a higher degree of intra-group clustering. These findings
imply that under conditions of starvation, \emph{Catharsius molossus}may
employ diverse strategies in response to environmental changes, leading
to differences in their gut microbiota configurations.

\subsection*{3.5 Functional prediction using
PICRUSt}

{\label{functional-prediction-using-picrust}}

Functional gene annotations predicted by PICRUSt provided a
comprehensive insight into the metabolic potential of the gut microbiota
in Catharsius molossus. These annotations were mapped to primary,
secondary, and tertiary pathways within the KEGG database, encompassing
various hierarchical levels of metabolic classification. The primary
pathways fell under five fundamental metabolic categories: metabolism,
genetic information processing, environmental information processing,
cellular processes, and organismal systems. Within these primary
pathways, a total of 27 secondary-level pathways and 175 tertiary
pathways were identified. Remarkably, metabolic pathways were dominant
within the primary metabolic category, accounting for approximately 80\%
of the total annotations. Among the secondary pathways, amino acid
metabolism exhibited the highest gene abundance, constituting around
15\%. Notably, utilizing the Wilcoxon rank-sum test, 32 tertiary
metabolic pathways displayed significant differences
(\emph{P}\textless{} 0.05), highlighting potential alterations in the
metabolic landscape between groups at the granularity of these specific
pathway levels (Fig. 4).

\section*{4. DISCUSSION}

{\label{discussion}}

\subsection*{\texorpdfstring{4.1 Analysis of gut microbiota composition
in different states of \emph{Catharsius
molossus}}{4.1 Analysis of gut microbiota composition in different states of Catharsius molossus}}

{\label{analysis-of-gut-microbiota-composition-in-different-states-of-catharsius-molossus}}

Gut microbiota, as the ''inner ecosystem'' of the gut, plays a crucial
role in insects' digestive systems that rely on limited nutrients or
hard-to-digest food sources(Tafesh-Edwards \& Eleftherianos, 2023; Wang
et al., 2020). We observed stable and enduring microbial community
structures in the host, referred to as ''dominant microbial taxa.'' The
presence of such microbial communities is vital for maintaining
ecological balance and host health(Chen et al., 2021). Studies have
demonstrated that gut microbiota in insects like\emph{Pachysoma MacLeay}
from arid coastal regions of southwestern Africa and flightless
\emph{Thorectes lusitanicus} contain dominant taxa such as
Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria(Franzini
et al., 2016; Hernández et al., 2015). In our study, the predominant
phyla in the gut of \emph{Catharsius molossus} in both hungry and
refeeding states were Proteobacteria, Firmicutes, Actinobacteria, and
Bacteroidetes. Notably, Bacteroidetes, known as obligate or strict
anaerobes, are challenging to culture in vitro, suggesting potential
similarities in the dominant microbial taxa among different beetle
species and their significant roles in host growth and development.
After refeeding, the proportion of Firmicutes and Bacteroidetes
increased, while Proteobacteria decreased. The consistent proportion of
Actinobacteria may relate to the fiber-rich diet of\emph{Catharsius
molossus} , as Firmicutes possesses genes involved in fermenting dietary
fiber and interacts with intestinal mucosa, contributing to maintaining
host equilibrium (Sun et al., 2022). Bacteroidetes, known for
polysaccharide degradation and aiding dietary fiber energy
release(Pereira et al., 2021), exhibited significant differences between
hungry and refeeding states, indicating its potential role in
polysaccharide metabolism that warrants further experimental validation
(Vera-Ponce de León et al., 2020). Regarding the variation in
Proteobacteria, previous studies have suggested that an increase in its
proportion could lead to an elevated risk of diseases, serving as an
indicator of microbial community disruption, and potentially even as a
diagnostic marker for diseases (Rizzatti et al., 2017; Shin et al.,
2015). Importantly, within the context of our study, we have observed a
relatively higher abundance of Proteobacteria within the gut microbiota
of \emph{Catharsius molossus} during periods of dietary deprivation
compared to the refeeding state. This observation provides suggestive
insights that starvation could potentially induce an unfavorable
physiological state, potentially contributing to the instability of the
host's gut microbiota ecology. Additionally, the relatively small
proportion of \emph{Dysgonomonas} in our study differs from
\emph{Vagococcus} and \emph{Romboutsia} , which were dominant genera
according to Suarez-Moo et al. (Suárez-Moo et al.,
2020).\emph{Dysgonomonas} has been associated with nitrogen fixation
capabilities (Bar-Shmuel et al., 2020; Skrzypczak \& Przybylski, 2022),
and its higher abundance during starvation suggests a potential role in
nitrogen fixation to sustain host survival. However, more research is
needed to explore whether \emph{Dysgonomonas} assimilates nitrogen into
the beetle or its direct environment. In summary, hunger and refeeding
affect gut microbiota diversity and variability in \emph{Catharsius
molossus} . Yet, the impact of changes in gut microbial abundance on
host physiological functions and their mechanisms remains to be
investigated. Our study provides valuable insights into the adaptability
of gut microbiota in beetles under unique environments and their
ecological functions.

\subsection*{\texorpdfstring{4.2 Predictive analysis of \emph{Catharsius
molossus} gut microbiota
functions}{4.2 Predictive analysis of Catharsius molossus gut microbiota functions}}

{\label{predictive-analysis-of-catharsius-molossus-gut-microbiota-functions}}

Utilizing PICRUSt, we conducted functional predictions, revealing a
higher abundance of genes related to primary metabolic pathways and
genetic information processing at the primary pathway level. At the
tertiary pathway level, the gut microbiota of hungry \emph{Catharsius
molossus} showed higher gene abundances in pathways related to ansamycin
biosynthesis, vancomycin group biosynthesis, and D-glutamine and
D-glutamate metabolism, while refeeding state displayed elevated gene
abundances in pathways related to ansamycin biosynthesis, valine,
leucine, and isoleucine biosynthesis, and D-glutamine and D-glutamate
metabolism. Ansaamycins are essential antibiotics, particularly against
Mycobacterium tuberculosis(Skrzypczak \& Przybylski, 2022). This
suggests that \emph{Catharsius molossus} may utilize its gut
microbiota's metabolic functions to interact with the environment,
assisting in defense against pathogen and parasite infections, which may
contribute to its unique adaptation to a diet of feces and living in
such an environment. However, it's important to note that while our
functional predictions offer valuable clues, they are based on
predictions and don't fully represent the actual functions of gut
microbiota. To delve into the precise roles of these gene functions
in\emph{Catharsius molossus'} environmental adaptability, integration of
multi-omics data is essential. Such comprehensive research would enhance
our understanding of the physiological functions, interdependencies, and
coordination mechanisms of gut microbiota, uncovering their complex
functions and potential applications. This approach could facilitate
further resource development and utilization in the context of gut
microbiota.

\section*{5. CONCLUSIONS}

{\label{conclusions}}

In conclusion, this study elucidates the influence of starvation and
refeeding conditions on the diversity and differential composition of
gut microbiota in \emph{Catharsius molossus} . Additionally, it provides
insights into functional predictions associated with specific microbial
taxa. Furthermore, \emph{Dysgonomonas} is postulated to engage in
nitrogen fixation within the host and potentially contribute to the
host's defense against pathogens and parasites through gut microbiota
metabolic functions. The gut microbiota of \emph{Catharsius
molossus}also presents a potential reservoir of antimicrobial agents
akin to anshanmycin. These findings not only introduce a fresh
perspective on microbial interactions within the Coleoptera ecosystem
but also offer theoretical underpinnings for future applications.
Overall, this research contributes to a deeper understanding of the
intricate interplay between gut microbiota and the host, paving the way
for further investigations into the potential therapeutic applications
of gut microbiota modulation.

\section*{AUTHOR CONTRIBUTIONS}

{\label{author-contributions}}

\textbf{Yue Mao} : Conceptualization (supporting); Data curation
(equal); Formal analysis (lead); Investigation (equal); Methodology
(equal); Software (lead); Visualization (equal); Writing -- original
draft (lead); Writing -- review \& editing (supporting).
\textbf{Xingjian Yang} : Conceptualization (supporting); Formal analysis
(supporting); Investigation (equal); Methodology (supporting); Software
(supporting).\textbf{Hui Yuan} : Conceptualization (supporting); Formal
analysis (supporting); Investigation (equal); Methodology (supporting);
Software (supporting). \textbf{Tao Xiong} : Conceptualization (equal);
Data curation (equal); Funding acquisition (lead); Formal analysis
(supporting); Investigation (equal); Methodology (equal); Project
administration (equal); Resources (equal); Supervision (lead);
Visualization (equal); Writing -- review \& editing (lead).

\section*{DECLARATION OF COMPETING
INTEREST}

{\label{declaration-of-competing-interest}}

None.

\section*{ACKNOWLEDGEMENTS}

{\label{acknowledgements}}

The authors gratefully acknowledge financial support from the State
Veterinary Biotechnology State Key Laboratory Open Topics of China
(Youth Fund SKLVBF202002) and Northern University Agricultural Fund
(20220013).

\section*{DATA AVAILABILITY STATEMENT}

{\label{data-availability-statement}}

The data that support the findings of this study are openly available in
Figshare at doi: 10.6084/m9.figshare.24143640.

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